Professor Michael Clements

'Professor Michael Clements

Professor Michael Clements

  • Professor of Econometrics
  • PhD Programme Director

Contact details

Profile & Expertise

Michael P Clements is Professor of Econometrics at the ICMA Centre, Henley Business School, University of Reading and an Associate member of the Institute for New Economic Thinking at the Oxford Martin School, University of Oxford. He obtained a DPhil in Econometrics from Nuffield College, University of Oxford in 1993, moved to Warwick University Economics Department as a Research Fellow in 1995, and became a full professor in 2007. He moved to Reading in 2013.

Mike’s interests are in the areas of time-series econometrics and forecasting, and he has published widely in academic journals on  forecast evaluation, mixed-frequency data modelling, non-linear modelling and business cycle analysis, real-time modelling and forecasting, factor model forecasting, and the analysis of survey expectations.

Mike became a Journal of Applied Econometrics Distinguished Author in 2008.

He served as an editor of the International Journal of Forecasting between 2001 and 2012, and since standing down from this role has served as an associate editor.

He was elected an Honorary Fellow of the International Institute of Forecasters in 2014: http://forecasters.org/activities/funding-awards/fellows/.

All of Mike’s publications are available at http://ideas.repec.org/e/pcl24.html and recent Discussion Papers at http://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=157383.

Key publications, books, research & papers

Article

Predicting early data revisions to US GDP and the effects of releases on equity markets

Clements, M. P. and Galvão, A. B. (2017) Predicting early data revisions to US GDP and the effects of releases on equity markets. Journal of Business & Economic Statistics, 35 (3). pp. 389-406. ISSN 0735-0015 doi: https://doi.org/10.1080/07350015.2015.1076726

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The effects of data uncertainty on real-time decision-making can be reduced by predicting early revisions to US GDP growth. We show that survey forecasts efficiently anticipate the first-revised estimate of GDP, but that forecasting models incorporating monthly economic indicators and daily equity returns provide superior forecasts of the second-revised estimate. We consider the implications of these findings for analyses of the impact of surprises in GDP revision announcements on equity markets, and for analyses of the impact of anticipated future revisions on announcement-day returns.

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Article

Model and survey estimates of the term structure of US macroeconomic uncertainty

Clements, M. and Galvão, A. B. (2017) Model and survey estimates of the term structure of US macroeconomic uncertainty. International Journal of Forecasting, 33 (3). pp. 591-604. ISSN 0169-2070 doi: https://doi.org/10.1016/j.ijforecast.2017.01.004

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Survey data on macro-forecasters suggest that their assessments of future output growth and inflation uncertainty tend to be too high. We find that model estimates of the term structure of ex ante or perceived macro uncertainty are more in line with ex post RMSE measures than are the survey respondents’ perceptions. At shorter horizons, the models’ assessments of the uncertainty characterising the outlook are lower than those indicated by the survey data histograms, and closer to the RMSE estimates. Recent developments in econometric modelling ensure that the models’ information sets line up with the timing of information available to the survey respondents, thus enabling a fair comparison.

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Article

Do macro-forecasters herd?

Clements, M. P. (2017) Do macro-forecasters herd? Journal of Money, Credit and Banking. ISSN 1538-4616 (In Press)

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We show that typical tests of whether forecasters herd will falsely indicate herding behaviour for a variety of types of behaviour and forecasting environments that give rise to disagreement amongst forecasters. We establish that forecasters will appear to herd if di¤erences between them reject noise as opposed to private information, or if they arise from informational rigidities. Noise can have a behavioural interpretation, and if so will depend on the behavioural model under consideration. An application of the herding tests to US quarterly survey forecasts of inflation and output growth data 1981-2013 does not support herding behaviour.

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Article

Assessing macro uncertainty in real-time when data are subject to revision

Clements, M. P. (2017) Assessing macro uncertainty in real-time when data are subject to revision. Journal of Business & Economic Statistics, 35 (3). pp. 420-433. ISSN 0735-0015 doi: https://doi.org/10.1080/07350015.2015.1081596

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Model-based estimates of future uncertainty are generally based on the in-sample fit of the model, as when Box-Jenkins prediction intervals are calculated. However, this approach will generate biased uncertainty estimates in real time when there are data revisions. A simple remedy is suggested, and used to generate more accurate prediction intervals for 25 macroeconomic variables, in line with the theory. A simulation study based on an empirically-estimated model of data revisions for US output growth is used to investigate small-sample properties.

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Article

Are macroeconomic density forecasts informative?

Clements, M. (2017) Are macroeconomic density forecasts informative? International Journal of Forecasting. ISSN 0169-2070 (In Press)

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We consider whether survey density forecasts (such as the inflation and output growth histograms of the US Survey of Professional Forecasters) are superior to unconditional density forecasts. The unconditional forecasts assume that the average level of uncertainty experienced in the past will prevail in the future, whereas the SPF projections ought to be adapted to current conditions and the outlook at each forecast origin. The SPF forecasts might be expected to outperform the unconditional densities at the shortest horizons, but this does not transpire to be the case for the aggregate forecasts of either variable, or for the majority of the individual respondents for forecasting inflation.

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Article

Real-time factor model forecasting and the effects of instability

Clements, M. (2016) Real-time factor model forecasting and the effects of instability. Computational Statistics and Data Analysis, 100. pp. 661-675. ISSN 0167-9473 doi: https://doi.org/10.1016/j.csda.2015.01.011

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Factor forecasting models are shown to deliver real-time gains over autoregressive models for US real activity variables during the recent period, but are less successful for nominal variables. The gains are largely due to the Financial Crisis period, and are primarily at the shortest (one quarter ahead) horizon. Excluding the pre-Great Moderation years from the factor forecasting model estimation period (but not from the data used to extract factors) results in a marked fillip in factor model forecast accuracy, but does the same for the AR model forecasts. The relative performance of the factor models compared to the AR models is largely unaffected by whether the exercise is in real time or is pseudo out-of-sample.

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Article

Long-run restrictions and survey forecasts of output, consumption and investment

Clements, M. P. (2016) Long-run restrictions and survey forecasts of output, consumption and investment. International Journal of Forecasting, 32 (3). pp. 614-628. ISSN 0169-2070 doi: https://doi.org/10.1016/j.ijforecast.2015.10.005

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We consider the extent to which long-horizon survey forecasts of consumption, investment and output growth are consistent with theory-based steady-state values, and whether imposing these restrictions on long-horizon forecasts will enhance their accuracy. The restrictions we impose are consistent with a two-sector model in which the variables grow at different rates in steady state. The restrictions are imposed by exponential-tilting of simple auxiliary forecast densities. We show that imposing the consumption-output restriction yields modest improvements in the long-horizon output growth forecasts, and larger improvements in the forecasts of the cointegrating combination of consumption and output: the transformation of the data on which accuracy is assessed plays an important role.

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Article

Robust approaches to forecasting

Castle, J. L., Clements, M. and Hendry, D. (2015) Robust approaches to forecasting. International Journal of Forecasting, 31 (1). pp. 99-112. ISSN 0169-2070 doi: https://doi.org/10.1016/j.ijforecast.2014.11.002

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We investigate alternative robust approaches to forecasting, using a new class of robust devices, contrasted with equilibrium-correction models. Their forecasting properties are derived facing a range of likely empirical problems at the forecast origin, including measurement errors, impulses, omitted variables, unanticipated location shifts and incorrectly included variables that experience a shift. We derive the resulting forecast biases and error variances, and indicate when the methods are likely to perform well. The robust methods are applied to forecasting US GDP using autoregressive models, and also to autoregressive models with factors extracted from a large dataset of macroeconomic variables. We consider forecasting performance over the Great Recession, and over an earlier more quiescent period.

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Article

Forecasting with Bayesian multivariate vintage-based VARs

Carriero, A., Clements, M. P. and Galvao, A. B. (2015) Forecasting with Bayesian multivariate vintage-based VARs. International Journal of Forecasting, 31 (3). pp. 757-768. ISSN 0169-2070 doi: https://doi.org/10.1016/j.ijforecast.2014.05.007

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We consider the forecasting of macroeconomic variables that are subject to revisions, using Bayesian vintage-based vector autoregressions. The prior incorporates the belief that, after the first few data releases, subsequent ones are likely to consist of revisions that are largely unpredictable. The Bayesian approach allows the joint modelling of the data revisions of more than one variable, while keeping the concomitant increase in parameter estimation uncertainty manageable. Our model provides markedly more accurate forecasts of post-revision values of inflation than do other models in the literature.

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Article

Do US macroeconomic forecasters exaggerate their differences?

Clements, M. (2015) Do US macroeconomic forecasters exaggerate their differences? Journal of Forecasting, 34 (8). pp. 649-660. ISSN 1099-131X doi: https://doi.org/10.1002/for.2358

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Application of the Bernhardt et al. (Journal of Financial Economics 2006; 80(3): 657–675) test of herding to the calendar-year annual output growth and inflation forecasts suggests forecasters tend to exaggerate their differences, except at the shortest horizon, when they tend to herd. We consider whether these types of behaviour can help to explain the puzzle that professional forecasters sometimes make point predictions and histogram forecasts which are mutually inconsistent.

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Article

Are professional macroeconomic forecasters able to do better than forecasting trends?

Clements, M. (2015) Are professional macroeconomic forecasters able to do better than forecasting trends? Journal of Money, Credit and Banking, 472 (2-3). pp. 349-382. ISSN 1538-4616 doi: https://doi.org/10.1111/jmcb.12179

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This paper investigates whether survey forecasters are able to make more accurate forecasts than simply supposing that the future values of the variable will move monotonically to the long-run expectation. We consider the forecasts individually, and the consensus forecasts. Consensus survey forecasts are able to do so to varying degrees depending on the variable, but this ability is largely limited to forecasts of the current quarter.

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Article

US inflation expectations and heterogeneous loss functions, 1968-2010

Clements, M. (2014) US inflation expectations and heterogeneous loss functions, 1968-2010. Journal of Forecasting, 33 (1). pp. 1-14. ISSN 1099-131X doi: https://doi.org/10.1002/for.2277

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Recent literature has suggested that macroeconomic forecasters may have asymmetric loss functions, and that there may be heterogeneity across forecasters in the degree to which they weigh under- and over-predictions. Using an individual-level analysis that exploits the Survey of Professional Forecasters respondents’ histogram forecasts, we find little evidence of asymmetric loss for the inflation forecasters

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Article

Probability distributions or point predictions? Survey forecasts of US output growth and inflation

Clements, M. (2014) Probability distributions or point predictions? Survey forecasts of US output growth and inflation. International Journal of Forecasting, 30 (1). pp. 99-117. ISSN 0169-2070 doi: https://doi.org/10.1016/j.ijforecast.2013.07.010

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We consider whether survey respondents’ probability distributions, reported as histograms, provide reliable and coherent point predictions, when viewed through the lens of a Bayesian learning model. We argue that a role remains for eliciting directly-reported point predictions in surveys of professional forecasters.

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Article

Forecast uncertainty—ex Ante and ex Post: U.S. inflation and output growth

Clements, M. (2014) Forecast uncertainty—ex Ante and ex Post: U.S. inflation and output growth. Journal of Business & Economic Statistics, 32 (2). pp. 206-216. ISSN 0735-0015 doi: https://doi.org/10.1080/07350015.2013.859618

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Survey respondents who make point predictions and histogram forecasts of macro-variables reveal both how uncertain they believe the future to be, ex ante, as well as their ex post performance. Macroeconomic forecasters tend to be overconfident at horizons of a year or more, but overestimate (i.e., are underconfident regarding) the uncertainty surrounding their predictions at short horizons. Ex ante uncertainty remains at a high level compared to the ex post measure as the forecast horizon shortens. There is little evidence of a link between individuals’ ex post forecast accuracy and their ex ante subjective assessments.

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Article

Real-time forecasting of inflation and output growth with autoregressive models in the presence of data revisions

Clements, M. and Galvao, A.B. (2013) Real-time forecasting of inflation and output growth with autoregressive models in the presence of data revisions. Journal of Applied Econometrics, 28 (3). pp. 458-477. ISSN 1099-1255 doi: https://doi.org/10.1002/jae.2274

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We examine how the accuracy of real-time forecasts from models that include autoregressive terms can be improved by estimating the models on ‘lightly revised’ data instead of using data from the latest-available vintage. The benefits of estimating autoregressive models on lightly revised data are related to the nature of the data revision process and the underlying process for the true values. Empirically, we find improvements in root mean square forecasting error of 2–4% when forecasting output growth and inflation with univariate models, and of 8% with multivariate models. We show that multiple-vintage models, which explicitly model data revisions, require large estimation samples to deliver competitive forecasts. Copyright © 2012 John Wiley & Sons, Ltd.

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Article

Forecasting with vector autoregressive models of data vintages: US output growth and inflation

Clements, M. and Galvao, A.B. (2013) Forecasting with vector autoregressive models of data vintages: US output growth and inflation. International Journal of Forecasting, 29 (4). pp. 698-714. ISSN 0169-2070 doi: https://doi.org/10.1016/j.ijforecast.2011.09.003

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Vintage-based vector autoregressive models of a single macroeconomic variable are shown to be a useful vehicle for obtaining forecasts of different maturities of future and past observations, including estimates of post-revision values. The forecasting performance of models which include information on annual revisions is superior to that of models which only include the first two data releases. However, the empirical results indicate that a model which reflects the seasonal nature of data releases more closely does not offer much improvement over an unrestricted vintage-based model which includes three rounds of annual revisions.

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Article

Forecasting by factors, by variables, by both or neither?

Castle, J. L., Clements, M. P. and Hendry, D. F. (2013) Forecasting by factors, by variables, by both or neither? Journal of Econometrics, 177 (2). pp. 305-319. ISSN 0304-4076 doi: https://doi.org/10.1016/j.jeconom.2013.04.015

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We consider forecasting with factors, variables and both, modeling in-sample using Autometrics so all principal components and variables can be included jointly, while tackling multiple breaks by impulse-indicator saturation. A forecast-error taxonomy for factor models highlights the impacts of location shifts on forecast-error biases. Forecasting US GDP over 1-, 4- and 8-step horizons using the dataset from Stock and Watson (2009) updated to 2011:2 shows factor models are more useful for nowcasting or short-term forecasting, but their relative performance declines as the forecast horizon increases. Forecasts for GDP levels highlight the need for robust strategies, such as intercept corrections or differencing, when location shifts occur as in the recent financial crisis.

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Article

Improving real-time estimates of output and inflation gaps with multiple-vintage models

Clements, M. P. and Galvao, A. B. (2012) Improving real-time estimates of output and inflation gaps with multiple-vintage models. Journal of Business and Economic Statistics, 30 (4). pp. 554-562. ISSN 0735-0015 doi: https://doi.org/10.1080/07350015.2012.707588

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Real-time estimates of output gaps and inflation gaps differ from the values that are obtained using data available long after the event. Part of the problem is that the data on which the real-time estimates are based is subsequently revised. We show that vector-autoregressive models of data vintages provide forecasts of post-revision values of future observations and of already-released observations capable of improving estimates of output and inflation gaps in real time. Our findings indicate that annual revisions to output and inflation data are in part predictable based on their past vintages.

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Article

Forecasting US output growth with non-linear models in the presence of data uncertainty

Clements, M. (2012) Forecasting US output growth with non-linear models in the presence of data uncertainty. Studies in nonlinear dynamics & econometrics, 16 (1). pp. 1-25. ISSN 1558-3708 doi: https://doi.org/10.1515/1558-3708.1865

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We consider the impact of data revisions on the forecast performance of a SETAR regime-switching model of U.S. output growth. The impact of data uncertainty in real-time forecasting will affect a model’s forecast performance via the effect on the model parameter estimates as well as via the forecast being conditioned on data measured with error. We find that benchmark revisions do affect the performance of the non-linear model of the growth rate, and that the performance relative to a linear comparator deteriorates in real-time compared to a pseudo out-of-sample forecasting exercise.

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Article

Do professional forecasters pay attention to data releases?

Clements, M. (2012) Do professional forecasters pay attention to data releases? International Journal of Forecasting, 28 (2). pp. 297-308. ISSN 0169-2070 doi: https://doi.org/10.1016/j.ijforecast.2011.09.001

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We present a novel approach to assessing the attentiveness of professional forecasters to news about the macroeconomy. We find evidence that professional forecasters, taken as a group, do not always update their estimates of the current state of the economy to reflect the latest releases of revised estimates of key data.

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Book

The Oxford handbook of economic forecasting

Clements, M. P. and Hendry, D. F., eds. (2011) The Oxford handbook of economic forecasting. OUP USA, pp624. ISBN 9780195398649

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Forecasting from mis-specified models in the presence of unanticipated location shifts

Clements, M. and Hendry, D. (2011) Forecasting from mis-specified models in the presence of unanticipated location shifts. In: Clements, M. and Hendry, D. (eds.) The Oxford Handbook of Economic Forecasting. OUP USA, p. 271. ISBN 9780195398649

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Article

Combining probability forecasts

Clements, M. P. and Harvey, D. I. (2011) Combining probability forecasts. International Journal of Forecasting, 27 (2). pp. 208-223. ISSN 0169-2070 doi: https://doi.org/10.1016/j.ijforecast.2009.12.016

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We consider different methods for combining probability forecasts. In empirical exercises, the data generating process of the forecasts and the event being forecast is not known, and therefore the optimal form of combination will also be unknown. We consider the properties of various combination schemes for a number of plausible data generating processes, and indicate which types of combinations are likely to be useful. We also show that whether forecast encompassing is found to hold between two rival sets of forecasts or not may depend on the type of combination adopted. The relative performances of the different combination methods are illustrated, with an application to predicting recession probabilities using leading indicators.

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Article

An empirical investigation of the effects of rounding on the SPF probabilities of decine and output growth histograms

Clements, M. P. (2011) An empirical investigation of the effects of rounding on the SPF probabilities of decine and output growth histograms. Journal of Money, Credit and Banking, 43 (1). pp. 207-220. ISSN 1538-4616 doi: https://doi.org/10.1111/j.1538-4616.2010.00371.x

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I consider the possibility that respondents to the Survey of Professional Forecasters round their probability forecasts of the event that real output will decline in the future, as well as their reported output growth probability distributions. I make various plausible assumptions about respondents’ rounding practices, and show how these impinge upon the apparent mismatch between probability forecasts of a decline in output and the probabilities of this event implied by the annual output growth histograms. I find that rounding accounts for about a quarter of the inconsistent pairs of forecasts.

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Article

Forecast encompassing tests and probability forecasts

Clements, M. P. and Harvey, D. I. (2010) Forecast encompassing tests and probability forecasts. Journal of Applied Econometrics, 25 (6). pp. 1028-1062. ISSN 1099-1255 doi: https://doi.org/10.1002/jae.1097

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We consider tests of forecast encompassing for probability forecasts, for both quadratic and logarithmic scoring rules. We propose test statistics for the null of forecast encompassing, present the limiting distributions of the test statistics, and investigate the impact of estimating the forecasting models’ parameters on these distributions. The small-sample performance is investigated, in terms of small numbers of forecasts and model estimation sample sizes. We show the usefulness of the tests for the evaluation of recession probability forecasts from logit models with different leading indicators as explanatory variables, and for evaluating survey-based probability forecasts.

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Article

First announcements and real economic activity

Clements, M. P. and Galvao, A. B. (2010) First announcements and real economic activity. European Economic Review, 54 (6). pp. 803-817. ISSN 0014-2921 doi: https://doi.org/10.1016/j.euroecorev.2009.12.010

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The recent literature suggests that first announcements of real output growth in the US have predictive power for the future course of the economy while the actual value of output growth does not. We show that this need not point to a behavioural relationship, whereby agents respond to perceptions instead of the truth, but may instead simply be a by-product of the data revision process. The revisions to the initial estimates which define the final values of the observations are shown to be key in determining any relationship between first announcements and the future course of the economy.

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Article

Explanations of the inconsistencies in survey respondents' forecasts

Clements, M. P. (2010) Explanations of the inconsistencies in survey respondents' forecasts. European Economic Review, 54 (4). pp. 536-549. ISSN 0014-2921 doi: https://doi.org/10.1016/j.euroecorev.2009.10.003

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A comparison of the point forecasts and the probability distributions of inflation and output growth made by individual respondents to the US Survey of Professional Forecasters indicates that the two sets of forecasts are sometimes inconsistent. We evaluate a number of possible explanations, and find that not all forecasters update their histogram forecasts as new information arrives. This is supported by the finding that the point forecasts are more accurate than the histograms in terms of first-moment prediction.

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Book or Report Section

Internal consistency of survey respondents' forecasts: Evidence based on the Survey of Professional Forecasters

Clements, M. (2009) Internal consistency of survey respondents' forecasts: Evidence based on the Survey of Professional Forecasters. In: Castle, J. L. and Shephard, N. (eds.) The Methodology and Practice of Econometrics. A Festschrift in Honour of David F. Hendry. Oxford University Press, pp. 206-226. ISBN 9780199237197

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Article

Forecasting US output growth using leading indicators: an appraisal using MIDAS models

Clements, M. P. and Galvao, A. B. (2009) Forecasting US output growth using leading indicators: an appraisal using MIDAS models. Journal of Applied Econometrics, 24 (7). pp. 1187-1206. ISSN 1099-1255 doi: https://doi.org/10.1002/jae.1075

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We evaluate the predictive power of leading indicators for output growth at horizons up to 1 year. We use the MIDAS regression approach as this allows us to combine multiple individual leading indicators in a parsimonious way and to directly exploit the information content of the monthly series to predict quarterly output growth. When we use real-time vintage data, the indicators are found to have significant predictive ability, and this is further enhanced by the use of monthly data on the quarter at the time the forecast is made

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Article

Forecasting returns and risk in financial markets using linear and nonlinear models

Clements, M. P. , Milas, C. and van Dijk, D. (2009) Forecasting returns and risk in financial markets using linear and nonlinear models. International Journal of Forecasting, 25 (2). pp. 215-217. ISSN 0169-2070 doi: https://doi.org/10.1016/j.ijforecast.2009.01.003

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Book or Report Section

Forecast combination and encompassing

Clements, M. P. and Harvey, D. I. (2009) Forecast combination and encompassing. In: Mills, T.C. and Patterson, K. (eds.) Palgrave Handbook of Econometrics: Volume 2: Applied Econometrics. Palgrave Macmillan, London, pp. 169-198. ISBN 9781403917997

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Article

Comments on “Forecasting economic and financial variables with global VARs”

Clements, M. P. (2009) Comments on “Forecasting economic and financial variables with global VARs”. International Journal of Forecasting, 25 (4). pp. 680-683. ISSN 0169-2070 doi: https://doi.org/10.1016/j.ijforecast.2009.05.007

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Article

Quantile forecasts of daily exchange rate returns from forecasts of realized volatility

Clements, M. P. , Galvao, A. B. and Kim, J. H. (2008) Quantile forecasts of daily exchange rate returns from forecasts of realized volatility. Journal of Empirical Finance, 15 (4). pp. 729-750. ISSN 0927-5398 doi: https://doi.org/10.1016/j.jempfin.2007.12.001

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Quantile forecasts are central to risk management decisions because of the widespread use of Value-at-Risk. A quantile forecast is the product of two factors: the model used to forecast volatility, and the method of computing quantiles from the volatility forecasts. In this paper we calculate and evaluate quantile forecasts of the daily exchange rate returns of five currencies. The forecasting models that have been used in recent analyses of the predictability of daily realized volatility permit a comparison of the predictive power of different measures of intraday variation and intraday returns in forecasting exchange rate variability. The methods of computing quantile forecasts include making distributional assumptions for future daily returns as well as using the empirical distribution of predicted standardized returns with both rolling and recursive samples. Our main findings are that the Heterogenous Autoregressive model provides more accurate volatility and quantile forecasts for currencies which experience shifts in volatility, such as the Canadian dollar, and that the use of the empirical distribution to calculate quantiles can improve forecasts when there are shifts

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Article

Macroeconomic forecasting with mixed-frequency data: forecasting output growth in the United States

Clements, M. P. and Galvão, A. B. (2008) Macroeconomic forecasting with mixed-frequency data: forecasting output growth in the United States. Journal of Business and Economic Statistics, 26 (4). pp. 546-554. ISSN 0735-0015 doi: https://doi.org/10.1198/073500108000000015

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Many macroeconomic series, such as U.S. real output growth, are sampled quarterly, although potentially useful predictors are often observed at a higher frequency. We look at whether a mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth. The MIDAS specification used in the comparison uses a novel way of including an autoregressive term. We find that the use of monthly data on the current quarter leads to significant improvement in forecasting current and next quarter output growth, and that MIDAS is an effective way to exploit monthly data compared with alternative methods.

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Forecasting annual UK inflation using an econometric model over 1875-1991

Clements, M. P. and Hendry, D. F. (2008) Forecasting annual UK inflation using an econometric model over 1875-1991. In: Rapach, D.E. and Wohar, M.E. (eds.) Forecasting in the Presence of Structural Breaks and Model Uncertainty. Frontiers of Economics and Globalization. Emerald Publishing, pp. 3-39. ISBN 9780444529428 doi: https://doi.org/10.1016/S1574-8715(07)00201-1

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Article

Economic forecasting in a changing world

Clements, M. P. and Hendry, J. F. (2008) Economic forecasting in a changing world. Capitalism and Society, 3 (2). ISSN 1932-0213 doi: https://doi.org/10.2202/1932-0213.1039

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This article explains the basis for a theory of economic forecasting developed over the past decade by the authors. The research has resulted in numerous articles in academic journals, two monographs, Forecasting Economic Time Series, 1998, Cambridge University Press, and Forecasting Nonstationary Economic Time Series, 1999, MIT Press, and three edited volumes, Understanding Economic Forecasts, 2001, MIT Press, A Companion to Economic Forecasting, 2002, Blackwells, and the Oxford Bulletin of Economics and Statistics, 2005. The aim here is to provide an accessible, non-technical, account of the main ideas. The interested reader is referred to the monographs for derivations, simulation evidence, and further empirical illustrations, which in turn reference the original articles and related material, and provide bibliographic perspective.

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Article

Consensus and uncertainty: using forecast probabilities of output declines

Clements, M. P. (2008) Consensus and uncertainty: using forecast probabilities of output declines. International Journal of Forecasting, 24 (1). pp. 76-86. ISSN 0169-2070 doi: https://doi.org/10.1016/j.ijforecast.2007.06.003

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A number of studies have addressed the relationship between intra-personal uncertainty and inter-personal disagreement about the future values of economic variables such as output growth and inflation using the SPF. By making use of the SPF respondents’ probability forecasts of declines in output, we are able to construct a quarterly series of output growth uncertainty to supplement the annual series that are often used in such analyses. We also consider the relationship between disagreement and uncertainty for probability forecasts of declines in output.

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Article

Bootstrap prediction intervals for autoregressive time series

Clements, M. P. and Kim, J.H. (2007) Bootstrap prediction intervals for autoregressive time series. Computational Statistics and Data Analysis, 51 (7). pp. 3580-3594. ISSN 0167-9473 doi: https://doi.org/10.1016/j.csda.2006.09.012

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The calculation of interval forecasts for highly persistent autoregressive (AR) time series based on the bootstrap is considered. Three methods are considered for countering the small-sample bias of least-squares estimation for processes which have roots close to the unit circle: a bootstrap bias-corrected OLS estimator; the use of the Roy–Fuller estimator in place of OLS; and the use of the Andrews–Chen estimator in place of OLS. All three methods of bias correction yield superior results to the bootstrap in the absence of bias correction. Of the three correction methods, the bootstrap prediction intervals based on the Roy–Fuller estimator are generally superior to the other two. The small-sample performance of bootstrap prediction intervals based on the Roy–Fuller estimator are investigated when the order of the AR model is unknown, and has to be determined using an information criterion.

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An evaluation of the forecasts of the Federal Reserve: A pooled approach

Clements, M. , Joutz, F. and Stekler, H. O. (2007) An evaluation of the forecasts of the Federal Reserve: A pooled approach. Journal of Applied Econometrics, 22 (1). pp. 121-136. ISSN 1099-1255 doi: https://doi.org/10.1002/jae.954

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Forecasting with breaks

Clements, M. and Hendry, D. (2006) Forecasting with breaks. In: Elliot, G., Granger, C.W.J. and Timmermann, A. (eds.) Handbook of Economic Forecasting, Volume 1. North Holland, pp. 605-651. ISBN 9780444513953

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Article

Evaluating the Survey of Professional Forecasters probability distributions of expected inflation based on derived event probability forecasts

Clements, M. P. (2006) Evaluating the Survey of Professional Forecasters probability distributions of expected inflation based on derived event probability forecasts. Empirical Economics, 31 (1). pp. 49-64. ISSN 0377-7332 doi: https://doi.org/10.1007/s00181-005-0014-9

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Techniques are proposed for evaluating forecast probabilities of events. The tools are especially useful when, as in the case of the Survey of Professional Forecasters (SPF) expected probability distributions of inflation, recourse cannot be made to the method of construction in the evaluation of the forecasts. The tests of efficiency and conditional efficiency are applied to the forecast probabilities of events of interest derived from the SPF distributions, and supplement a whole-density evaluation of the SPF distributions based on the probability integral transform approach.

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Combining predictors & combining information in modelling: forecasting US recession probabilities and output growth

Clements, M. P. and Gãlvao, A.B. (2006) Combining predictors & combining information in modelling: forecasting US recession probabilities and output growth. In: Milas, C., Rothman, P. A., van Dijk, D. and Wildasin, D. E. (eds.) Non-linear Time Series Analysis of Business Cycles. Contributions to Economic Analysis, 276. Elsevier Science, pp. 57-73. ISBN 978444518385

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Article

Guest editors' introduction: information in economic forecasting

Clements, M. P. and Hendry, D. F. (2005) Guest editors' introduction: information in economic forecasting. Oxford Bulletin of Economics and Statistics, 67 (Suppl. S1). pp. 713-753. ISSN 1468-0084 doi: https://doi.org/10.1111/j.1468-0084.2005.00139.x

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Article

Forecasting aggregate quarterly crime series

Clements, M. P. and Witt, R. (2005) Forecasting aggregate quarterly crime series. The Manchester School, 73 (6). pp. 709-727. ISSN 1467-9957 doi: https://doi.org/10.1111/j.1467-9957.2005.00473.x

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Book

Evaluating econometric forecasts of economic and financial variables

Clements, M. P. (2005) Evaluating econometric forecasts of economic and financial variables. Palgrave Texts in Econometrics. Palgrave Macmillan, Basingstoke, pp186. ISBN 9781403941572

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Article

Evaluating a model by forecast performance

Clements, M. P. and Hendry, D. F. (2005) Evaluating a model by forecast performance. Oxford Bulletin of Economics and Statistics, 67 (Suppl.S1). pp. 931-956. ISSN 1468-0084 doi: https://doi.org/10.1111/j.1468-0084.2005.00146.x

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Article

Pooling of forecasts

Hendry, D. F. and Clements, M. P. (2004) Pooling of forecasts. Econometrics Journal, 7 (1). pp. 1-31. ISSN 1368-423X doi: https://doi.org/10.1111/j.1368-423X.2004.00119.x

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We consider forecasting using a combination, when no model coincides with a non-constant data generation process (DGP). Practical experience suggests that combining forecasts adds value, and can even dominate the best individual device. We show why this can occur when forecasting models are differentially mis-specified, and is likely to occur when the DGP is subject to location shifts. Moreover, averaging may then dominate over estimated weights in the combination. Finally, it cannot be proved that only non-encompassed devices should be retained in the combination. Empirical and Monte Carlo illustrations confirm the analysis.

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Article

Forecasting economic and financial time series with non-linear models

Clements, M. P. , Franses, P. H. and Swanson, N. R. (2004) Forecasting economic and financial time series with non-linear models. International Journal of Forecasting, 20 (2). pp. 169-183. ISSN 0169-2070 doi: https://doi.org/10.1016/j.ijforecast.2003.10.004

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In this paper we discuss the current state-of-the-art in estimating, evaluating, and selecting among non-linear forecasting models for economic and financial time series. We review theoretical and empirical issues, including predictive density, interval and point evaluation and model selection, loss functions, data-mining, and aggregation. In addition, we argue that although the evidence in favor of constructing forecasts using non-linear models is rather sparse, there is reason to be optimistic. However, much remains to be done. Finally, we outline a variety of topics for future research, and discuss a number of areas which have received considerable attention in the recent literature, but where many questions remain.

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Article

Evaluating the Bank of England density forecasts of inflation

Clements, M. P. (2004) Evaluating the Bank of England density forecasts of inflation. The Economic Journal, 114 (498). pp. 844-866. ISSN 1468-0297 doi: https://doi.org/10.1111/j.1468-0297.2004.00246.x

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We consider evaluating the UK Monetary Policy Committee’s inflation density forecasts using probability integral transform goodness-of-fit tests. These tests evaluate the whole forecast density. We also consider whether the probabilities assigned to inflation being in certain ranges are well calibrated, where the ranges are chosen to be those of particular relevance to the MPC, given its remit of maintaining inflation rates in a band around per annum. Finally, we discuss the decision-based approach to forecast evaluation in relation to the MPC forecasts

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Article

Can regime switching models reproduce the business cycle features of US aggregate consumption, investment and output?

Clements, M. and Krolzig, H.-M. (2004) Can regime switching models reproduce the business cycle features of US aggregate consumption, investment and output? International Journal of Finance & Economics, 9 (1). pp. 1-14. ISSN 1099-1158 doi: https://doi.org/10.1002/ijfe.231

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Article

A comparison of tests of non-linear cointegration with an application to the predictability of US interest rates using the term structure

Clements, M. P. and Galvao, A. B. (2004) A comparison of tests of non-linear cointegration with an application to the predictability of US interest rates using the term structure. International Journal of Forecasting, 20 (2). pp. 219-236. ISSN 0169-2070 doi: https://doi.org/10.1016/j.ijforecast.2003.09.001

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We test whether there are nonlinearities in the response of short- and long-term interest rates to the spread in interest rates, and assess the out-of-sample predictability of interest rates using linear and nonlinear models. We find strong evidence of nonlinearities in the response of interest rates to the spread. Nonlinearities are shown to result in more accurate short-horizon forecasts, especially of the spread.

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Article

Testing the expectations theory of the term structure in threshold models

Clements, M. P. and Galvao, A. B. C. (2003) Testing the expectations theory of the term structure in threshold models. Macroeconomic Dynamics, 7 (4). pp. 567-585. ISSN 1365-1005 doi: https://doi.org/10.1017/S1365100502020163

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We test the expectations theory of the term structure of U.S. interest rates in nonlinear systems. These models allow the response of the change in short rates to past values of the spread to depend upon the level of the spread. The nonlinear system is tested against a linear system, and the results of testing the expectations theory in both models are contrasted. We find that the results of tests of the implications of the expectations theory depend on the size and sign of the spread. The long maturity spread predicts future changes of the short rate only when it is high.

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Article

Some possible directions for future research

Clements, M. P. (2003) Some possible directions for future research. International Journal of Forecasting, 19 (1). pp. 1-3. ISSN 0169-2070 doi: https://doi.org/10.1016/S0169-2070(02)00037-7

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Article

On SETAR non-linearity and forecasting

Clements, M. P. , Franses, P. H., Smith, J. and van Dijk, D. (2003) On SETAR non-linearity and forecasting. Journal of Forecasting, 22 (5). pp. 359-375. ISSN 1099-131X doi: https://doi.org/10.1002/for.863

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We compare linear autoregressive (AR) models and self-exciting threshold autoregressive (SETAR) models in terms of their point forecast performance, and their ability to characterize the uncertainty surrounding those forecasts, i.e. interval or density forecasts. A two-regime SETAR process is used as the data-generating process in an extensive set of Monte Carlo simulations, and we consider the discriminatory power of recently developed methods of forecast evaluation for different degrees of non-linearity. We find that the interval and density evaluation methods are unlikely to show the linear model to be deficient on samples of the size typical for macroeconomic data

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Article

Evaluating interval forecasts of high-frequency financial data

Clements, M. P. and Taylor, N. (2003) Evaluating interval forecasts of high-frequency financial data. Journal of Applied Econometrics, 18 (4). pp. 445-456. ISSN 1099-1255 doi: https://doi.org/10.1002/jae.703

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A number of methods of evaluating the validity of interval forecasts of financial data are analysed, and illustrated using intraday FTSE100 index futures returns. Some existing interval forecast evaluation techniques, such as the Markov chain approach of Christoffersen (1998), are shown to be inappropriate in the presence of periodic heteroscedasticity. Instead, we consider a regression-based test, and a modified version of Christoffersen’s Markov chain test for independence, and analyse their properties when the financial time series exhibit periodic volatility. These approaches lead to different conclusions when interval forecasts of FTSE100 index futures returns generated by various GARCH(1,1) and periodic GARCH(1,1) models are evaluated.

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Article

Economic forecasting: some lessons from recent research

Hendry, D. F. and Clements, M. P. (2003) Economic forecasting: some lessons from recent research. Economic Modelling, 20 (2). pp. 301-329. ISSN 0264-9993 doi: https://doi.org/10.1016/S0264-9993(02)00055-X

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This paper describes some recent advances and contributions to our understanding of economic forecasting. The framework we develop helps explain the findings of forecasting competitions and the prevalence of forecast failure. It constitutes a general theoretical background against which recent results can be judged. We compare this framework to a previous formulation, which was silent on the very issues of most concern to the forecaster. We describe a number of aspects which it illuminates, and draw out the implications for model selection. Finally, we discuss the areas where research remains needed to clarify empirical findings which lack theoretical explanations.

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Article

Business cycle asymmetries: characterisation and testing based on Markov-switching autoregressions

Clements, M. P. and Krolzig, H.-M. (2003) Business cycle asymmetries: characterisation and testing based on Markov-switching autoregressions. Journal of Business and Economic Statistics, 21 (1). pp. 196-211. ISSN 0735-0015 doi: https://doi.org/10.1198/073500102288618892

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Tests for business cycle asymmetries are developed for Markov-switching autoregressive models. The tests of deepness, steepness, and sharpness are Wald statistics, which have standard asymptotics. For the standard two-regime model of expansions and contractions, deepness is shown to imply sharpness (and vice versa), whereas the process is always nonsteep. Two and three-state models of U.S. GNP growth are used to illustrate the approach, along with models of U.S. investment and consumption growth. The robustness of the tests to model misspecification, and the effects of regime-dependent heteroscedasticity, are investigated.

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Article

Asymmetric output gap effects in Phillips Curve and mark-up pricing models: evidence for the US and the UK

Clements, M. P. and Sensier, M. (2003) Asymmetric output gap effects in Phillips Curve and mark-up pricing models: evidence for the US and the UK. Scottish Journal of Political Economy, 50 (4). pp. 359-374. ISSN 1467-9485 doi: https://doi.org/10.1111/1467-9485.5004001

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A number of studies have found an asymmetric response of consumer price index inflation to the output gap in the US in simple Phillips curve models. We consider whether there are similar asymmetries in mark-up pricing models, that is, whether the mark-up over producers’ costs also depends upon the sign of the (adjusted) output gap. The robustness of our findings to the price series is assessed, and also whether price-output responses in the UK are asymmetric.

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Article

Modelling methodology and forecast failure

Clements, M. P. and Hendry, D. F. (2002) Modelling methodology and forecast failure. Econometrics Journal, 5 (2). pp. 319-344. ISSN 1368-423X doi: https://doi.org/10.1111/1368-423X.00086

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We analyse by simulation the impact of model-selection strategies (sometimes called pre-testing) on forecast performance in both constant-and non-constant-parameter processes. Restricted, unrestricted and selected models are compared when either of the first two might generate the data. We find little evidence that strategies such as general-to-specific induce significant over-fitting, or thereby cause forecast-failure rejection rates to greatly exceed nominal sizes. Parameter non-constancies put a premium on correct specification, but in general, model-selection effects appear to be relatively small, and progressive research is able to detect the mis-specifications.

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Explaining forecast failure in macroeconomics

Clements, M. P. and Hendry, D. (2002) Explaining forecast failure in macroeconomics. In: Clements, M. P. and Hendry, D. (eds.) A Companion to Economic Forecasting. Blackwells, pp. 539-571. ISBN 9780631215691

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Article

Evaluating multivariate forecast densities: a comparison of two approaches

Clements, M. P. and Smith, J. (2002) Evaluating multivariate forecast densities: a comparison of two approaches. International Journal of Forecasting, 18 (3). pp. 397-407. ISSN 0169-2070 doi: https://doi.org/10.1016/S0169-2070(01)00126-1

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We consider methods of evaluating multivariate density forecasts. A recently proposed method is found to lack power when the correlation structure is mis-specified. Tests that have good power to detect mis-specifications of this sort are described. We also consider the properties of the tests in the presence of more general mis-specifications.

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Article

Conditional mean functions of non-linear models of US output

Clements, M. P. and Galvao, A. B. C. (2002) Conditional mean functions of non-linear models of US output. Empirical Economics, 27 (4). pp. 569-586. ISSN 1435-8921 doi: https://doi.org/10.1007/s001810100103

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We compare a number of models of post War US output growth in terms of the degree and pattern of non-linearity they impart to the conditional mean, where we condition on either the previous period’s growth rate, or the previous two periods’ growth rates. The conditional means are estimated non-parametrically using a nearest-neighbour technique on data simulated from the models. In this way, we condense the complex, dynamic, responses that may be present in to graphical displays of the implied conditional mean.

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An overview of economic forecasting

Clements, M. P. and Hendry, D. (2002) An overview of economic forecasting. In: Clements, M. P. and Hendry, D. (eds.) A Companion to Economic Forecasting. Blackwells, pp. 1-18. ISBN 9781405126236

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A companion to economic forecasting

Clements, M. and Hendry, J. (2002) A companion to economic forecasting. Blackwell Companions to Contemporary Economics (Book 7). Wiley-Blackwell, Massachusetts USA, pp616. ISBN 9780631215691

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Article

Robust evaluation of fixed-event forecast rationality

Clements, M. and Taylor, N. (2001) Robust evaluation of fixed-event forecast rationality. Journal of Forecasting, 20. pp. 285-295. ISSN 1099-131X doi: https://doi.org/10.1002/for.806

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In this paper we introduce a new testing procedure for evaluating the rationality of fixed-event forecasts based on a pseudo-maximum likelihood estimator. The procedure is designed to be robust to departures in the normality assumption. A model is introduced to show that such departures are likely when forecasters experience a credibility loss when they make large changes to their forecasts. The test is illustrated using monthly fixed-event forecasts produced by four UK institutions. Use of the robust test leads to the conclusion that certain forecasts are rational while use of the Gaussian-based test implies that certain forecasts are irrational. The difference in the results is due to the nature of the underlying data. Copyright © 2001 John Wiley & Sons, Ltd.

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Article

Forecasting with difference and trend stationary models

Clements, M. and Hendry, J. (2001) Forecasting with difference and trend stationary models. Econometrics Journal, 4. pp. 1-19. ISSN 1368-423X doi: https://doi.org/10.1111/1368-423X.00050

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Although difference-stationary (DS) and trend-stationary (TS) processes have been subject to considerable analysis, there are no direct comparisons for each being the data-generation process (DGP). We examine incorrect choice between these models for forecasting for both known and estimated parameters. Three sets of Monte Carlo simulations illustrate the analysis, to evaluate the biases in conventional standard errors when each model is mis-specified, compute the relative mean-square forecast errors of the two models for both DGPs, and investigate autocorrelated errors, so both models can better approximate the converse DGP. The outcomes are surprisingly different from established results.

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Article

Explaining the results of the M3 forecasting competition (Part of Commentaries on the M3-Competition)

Clements, M. and Hendry, D. (2001) Explaining the results of the M3 forecasting competition (Part of Commentaries on the M3-Competition). International Journal of Forecasting, 17. pp. 550-554. ISSN 0169-2070

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Article

Evaluating forecasts from SETAR models of exchange rates

Clements, M. and Smith, J. (2001) Evaluating forecasts from SETAR models of exchange rates. Journal of International Money and Finance, 20. pp. 133-148. ISSN 0261-5606 doi: https://doi.org/10.1016/S0261-5606(00)00039-5

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We consider the forecasting performance of two SETAR exchange rate models proposed by Kräger and Kugler [J. Int. Money Fin. 12 (1993) 195]. Assuming that the models are good approximations to the data generating process, we show that whether the non-linearities inherent in the data can be exploited to forecast better than a random walk depends on both how forecast accuracy is assessed and on the ‘state of nature’. Evaluation based on traditional measures, such as (root) mean squared forecast errors, may mask the superiority of the non-linear models. Generalized impulse response functions are also calculated as a means of portraying the asymmetric response to shocks implied by such models.

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Article

Bootstrapping prediction intervals for autoregressive models

Clements, M. and Taylor, N. (2001) Bootstrapping prediction intervals for autoregressive models. International Journal of Forecasting., 17 (2). pp. 247-267. ISSN 0169-2070 doi: https://doi.org/10.1016/S0169-2070(00)00079-0

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Methods of improving the coverage of Box–Jenkins prediction intervals for linear autoregressive models are explored. These methods use bootstrap techniques to allow for parameter estimation uncertainty and to reduce the small-sample bias in the estimator of the models’ parameters. In addition, we also consider a method of bias-correcting the non-linear functions of the parameter estimates that are used to generate conditional multi-step predictions.

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Article

An historical perspective on forecast errors

Clements, M. and Hendry, D. (2001) An historical perspective on forecast errors. National Institute Economic Review, 177. pp. 70-82. doi: https://doi.org/10.1177/002795010117700109

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Using annual observations on industrial production over the last three centuries, and on GDP over a 100-year period, we seek an historical perspective on the forecastability of these UK output measures. The series are dominated by strong upward trends, so we consider various specifications of this, including the local linear trend structural time-series model, which allows the level and slope of the trend to vary. Our results are not unduly sensitive to how the trend in the series is modelled: the average sizes of the forecast errors of all models, and the wide span of prediction intervals, attests to a great deal of uncertainty in the economic environment. It appears that, from an historical perspective, the postwar period has been relatively more forecastable.

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Evaluating the forecast densities of linear and non-linear models: applications to output growth and unemployment

Clements, M. and Smith, J. (2000) Evaluating the forecast densities of linear and non-linear models: applications to output growth and unemployment. Journal of Forecasting, 19 (4). pp. 255-276. ISSN 1099-131X doi: https://doi.org/10.1002/1099-131X(200007)19:4<255::AID-FOR773>3.0.CO;2-G 3.0.CO;2-G>

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Economic forecasting in the face of structural breaks

Hendry, D. and Clements, M. (2000) Economic forecasting in the face of structural breaks. In: Holly, S. and Weale, M. (eds.) Econometric Modelling: Techniques and Applications. Cambridge University Press, pp. 3-37. ISBN 9780521650694

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Article

Seasonality, cointegration and forecasting UK residential energy demand

Clements, M. P. and Madlener, R. (1999) Seasonality, cointegration and forecasting UK residential energy demand. Scottish Journal of Political Economy, 46 (2). pp. 185-206. ISSN 1467-9485 doi: https://doi.org/10.1111/1467-9485.00128

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Much of the short-run movement in energy demand in the UK is seasonal, and the contribution of long-run factors to short-run forecasts is slight. Nevertheless, using a variety of techniques, including a recently developed estimation procedure that is applicable irrespective of the orders of integration of the data, we obtain a long-run income elasticity of demand of about one third, and we are unable to reject a zero price elasticity. An econometric model is shown to provide superior short-run forecasts to well-known seasonal time series models ex post, but is inferior to Box-Jenkins SARMA models when the determinants themselves have to be forecast. However, the relatively short data sample and small number of forecasts suggest caution in generalising these results.

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Article

On winning forecasting competitions in economics

Clements, M. P. and Hendry, D. F. (1999) On winning forecasting competitions in economics. Spanish Economic Review, 1 (2). pp. 123-160. ISSN 1435-5477 doi: https://doi.org/10.1007/s101080050006

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To explain which methods might win forecasting competitions on economic time series, we consider forecasting in an evolving economy subject to structural breaks, using mis-specified, data-based models. ‘Causal’ models need not win when facing deterministic shifts, a primary factor underlying systematic forecast failure. We derive conditional forecast biases and unconditional (asymptotic) variances to show that when the forecast evaluation sample includes sub-periods following breaks, non-causal models will outperform at short horizons. This suggests using techniques which avoid systematic forecasting errors, including improved intercept corrections. An application to a small monetary model of the UK illustrates the theory.

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Book

Forecasting non-stationary economic time series

Clements, M. and Hendry, D. (1999) Forecasting non-stationary economic time series. MIT, pp392. ISBN 9780262531894

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A Monte Carlo study of the forecasting performance of empirical SETAR models

Clements, M. P. and Smith, J. (1999) A Monte Carlo study of the forecasting performance of empirical SETAR models. Journal of Applied Econometrics, 14 (2). pp. 123-141. ISSN 1099-1255 doi: https://doi.org/10.1002/(SICI)1099-1255(199903/04)14:2<123::AID-JAE493>3.0.CO;2-K 3.0.CO;2-K>

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In this paper we investigate the multi-period forecast performance of a number of empirical self-exciting threshold autoregressive (SETAR) models that have been proposed in the literature for modelling exchange rates and GNP, among other variables. We take each of the empirical SETAR models in turn as the DGP to ensure that the ‘non-linearity’ characterizes the future, and compare the forecast performance of SETAR and linear autoregressive models on a number of quantitative and qualitative criteria. Our results indicate that non-linear models have an edge in certain states of nature but not in others, and that this can be highlighted by evaluating forecasts conditional upon the regime

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Forecasting Economic Time Series

Clements, M. and Hendry, D. (1998) Forecasting Economic Time Series. Cambridge University Press. ISBN 978-0521634809

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Forecasting economic processes

Clements, M. P. and Hendry, D. F. (1998) Forecasting economic processes. International Journal of Forecasting, 14 (1). pp. 111-131. ISSN 0169-2070 doi: https://doi.org/10.1016/S0169-2070(97)00057-5

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When the assumption of constant parameters fails, the in-sample fit of a model may be a poor guide to ex-ante forecast performance. We expound a number of models, methods, and procedures that illustrate the impacts of structural breaks on forecast accuracy, and evaluate ways of improving forecast performance. We argue that a theory of economic forecasting which allows for model mis-specification and structural breaks is feasible, and may provide a useful basis for interpreting and circumventing systematic forecast failure in macroeconomics. The empirical time series of consumers’ expenditure, and Monte Carlo simulations, illustrate the analysis.

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Article

A comparison of the forecast performance of Markov-switching and threshold autoregressive models of US GNP

Clements, M. P. and Krolzig, H.-M. (1998) A comparison of the forecast performance of Markov-switching and threshold autoregressive models of US GNP. Econometrics Journal, 1 (1). pp. 47-75. ISSN 1368-423X doi: https://doi.org/10.1111/1368-423X.11004

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While there has been a great deal of interest in the modelling of non-linearities in economic time series, there is no clear consensus regarding the forecasting abilities of non-linear time-series models. We evaluate the performance of two leading non-linear models in forecasting post-war US GNP, the self-exciting threshold autoregressive model and the Markov-switching autoregressive model. Two methods of analysis are employed: an empirical forecast accuracy comparison of the two models, and a Monte Carlo study. The latter allows us to control for factors that may otherwise undermine the performance of the non-linear models.

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The performance of alternative forecasting methods for SETAR models

Clements, M. P. and Smith, J. (1997) The performance of alternative forecasting methods for SETAR models. International Journal of Forecasting, 13 (4). pp. 463-475. ISSN 0169-2070 doi: https://doi.org/10.1016/S0169-2070(97)00017-4

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We compare a number of methods that have been proposed in the literature for obtaining h-step ahead minimum mean square error forecasts for self-exciting threshold autoregressive (SETAR) models. These forecasts are compared to those from an AR model. The comparison of forecasting methods is made using Monte Carlo simulation. The Monte-Carlo method of calculating SETAR forecasts is generally at least as good as that of the other methods we consider. An exception is when the disturbances in the SETAR model come from a highly asymmetric distribution, when a Bootstrap method is to be preferred. An empirical application calculates multi-period forecasts from a SETAR model of US gross national product using a number of the forecasting methods. We find that whether there are improvements in forecast performance relative to a linear AR model depends on the historical epoch we select, and whether forecasts are evaluated conditional on the regime the process was in at the time the forecast was made.

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Evaluating the rationality of fixed-event forecasts

Clements, M. P. (1997) Evaluating the rationality of fixed-event forecasts. Journal of Forecasting, 16 (4). pp. 225-239. ISSN 1099-131X doi: https://doi.org/10.1002/(SICI)1099-131X(199707)16:4<225::AID-FOR656>3.0.CO;2-L 3.0.CO;2-L>

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A test of forecast rationality based on the weak efficiency of fixed-event forecasts was proposed by Nordhaus (1987). This paper considers the scope for pooling fixed-event forecasts across ‘events’, to deliver more powerful tests of the weak-efficiency hypothesis, when only a small number of fixed-event forecasts are available. In an empirical illustration we demonstrate the usefulness of this approach. We also suggest an interpretation of the rejection of the null hypothesis of weak efficiency in favour of negative autocorrelation in series of revisions to fixed-event forecasts. The relationship between weak efficiency and rationality when loss functions are asymmetric and prediction error variances are time-varying is also considered.

Professor Michael Clements

Professor Michael Clements

PhD Programme Director

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An empirical study of seasonal unit roots in forecasting

Clements, M. P. and Hendry, D. F. (1997) An empirical study of seasonal unit roots in forecasting. International Journal of Forecasting, 13 (3). pp. 341-356. ISSN 0169-2070 doi: https://doi.org/10.1016/S0169-2070(97)00022-8

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We assess the usefulness of pre-testing for seasonal roots, based on the HEGY approach, for out-of-sample forecasting. It is shown that if there are shifts in the deterministic seasonal components then the imposition of unit roots can partially robustify sequences of rolling forecasts, yielding improved forecast accuracy. The analysis is illustrated with two empirical examples where more accurate forecasts are obtained by imposing more roots than is warranted by HEGY. The issue of assessing forecast accuracy when predictions of any one of a number of linear transformations may be of interest is also addressed

Professor Michael Clements

Professor Michael Clements

PhD Programme Director

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Article

Multi-step estimation for forecasting

Clements, M. P. and Hendry, D. F. (1996) Multi-step estimation for forecasting. Oxford Bulletin of Economics and Statistics, 58 (4). pp. 657-684. ISSN 1468-0084 doi: https://doi.org/10.1111/j.1468-0084.1996.mp58004005.x

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We delineate conditions which favour multi-step, or dynamic, estimation for multi-step forecasting. An analytical example shows how dynamic estimation (DE) may accommodate incorrectly-specified models as the forecast lead alters, improving forecast performance for some misspecifications. However, in correctly-specified models, reducing finite-sample biases does not justify DE. In a Monte Carlo forecasting study for integrated processes, estimating a unit root in the presence of a neglected negative moving-average error may favour DE, though other solutions exist to that scenario. A second Monte Carlo study obtains the estimator biases and explains these using asymptotic approximations.

Professor Michael Clements

Professor Michael Clements

PhD Programme Director

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Article

Intercept corrections and structural change

Clements, M. P. and Hendry, D. F. (1996) Intercept corrections and structural change. Journal of Applied Econometrics, 11 (5). pp. 475-494. ISSN 1099-1255 doi: https://doi.org/10.1002/(SICI)1099-1255(199609)11:5<475::AID-JAE409>3.0.CO;2-9 3.0.CO;2-9>

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Analyses of forecasting that assume a constant, time-invariant data generating process (DGP), and so implicitly rule out structural change or regime shifts in the economy, ignore an aspect of the real world responsible for some of the more dramatic historical episodes of predictive failure. Some models may offer greater protection against unforeseen structural breaks than others, and various tricks may be employed to robustify forecasts to change. We show that in certain states of nature, vector autoregressions in the differences of the variables (in the spirit of Box-Jenkins time-series modelling), can outperform vector ‘equilibrium-correction’ mechanisms. However, appropriate intercept corrections can enhance the performance of the latter, albeit that reductions in forecast bias may only be achieved at the cost of inflated forecast error variances.

Professor Michael Clements

Professor Michael Clements

PhD Programme Director

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Article

Rationality and the role of judgement in macroeconomic forecasting

Clements, M. P. (1995) Rationality and the role of judgement in macroeconomic forecasting. The Economic Journal, 105. pp. 410-420. ISSN 1468-0297

Professor Michael Clements

Professor Michael Clements

PhD Programme Director

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Article

Macro-economic forecasting and modelling

Clements, M. P. and Hendry, D. F. (1995) Macro-economic forecasting and modelling. The Economic Journal, 105. pp. 1001-1013. ISSN 1468-0297

Professor Michael Clements

Professor Michael Clements

PhD Programme Director

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Article

Forecasting in cointegrated systems

Clements, M. P. and Hendry, D. F. (1995) Forecasting in cointegrated systems. Journal of Applied Econometrics, 10 (2). pp. 127-146. ISSN 1099-1255 doi: https://doi.org/10.1002/jae.3950100204

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We consider the implications for forecast accuracy of imposing unit roots and cointegrating restrictions in linear systems of I(1) variables in levels, differences, and cointegrated combinations. Asymptotic formulae are obtained for multi-step forecast error variances for each representation. Alternative measures of forecast accuracy are discussed. Finite sample behaviour in a bivariate model is studied by Monte Carlo using control variables. We also analyse the interaction between unit roots and cointegrating restrictions and intercepts in the DGP. Some of the issues are illustrated with an empirical example of forecasting the demand for M1 in the UK.

Professor Michael Clements

Professor Michael Clements

PhD Programme Director

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Article

Can econometrics improve economic forecasting?

Hendry, D. F. and Clements, M. P. (1994) Can econometrics improve economic forecasting? Swiss Journal of Economics and Statistics, 130. pp. 267-298.

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After reviewing the history of analyses of economic forecasting, the role of econometrics in improving economic forecasting is considered, building on CLEMENTS and HENDRY (1994a). The basis of the analysis is a world where model selection is difficult, no model coincides with the economic mechanism, and that mechanism is both non-stationary and evolves over time. On the constructive side, econometric analysis suggests ways of reducing each of the resulting five sources of forecast uncertainty (parameter non-constancy; estimation uncertainty; variable uncertainty; innovation uncertainty; and model mis-specification). On the critical side, the lack of invariance of forecast evaluation procedures to the representation of the model may camouflage inadequate models. We show that forecasts generated from vector autoregressions in differences may be more robust to certain forms of structural change over the forecast period, and that a similar result can be achieved by suitable forms of intercept corrections in vector error-correction mechanisms.

Professor Michael Clements

Professor Michael Clements

PhD Programme Director

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Article

On the limitations of comparing mean squared forecast errors

Clements, M. P. and Hendry, D. F. (1993) On the limitations of comparing mean squared forecast errors. Journal of Forecasting, 12 (8). pp. 617-637. ISSN 1099-131X doi: https://doi.org/10.1002/for.3980120802

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Linear models are invariant under non-singular, scale-preserving linear transformations, whereas mean square forecast errors (MSFEs) are not. Different rankings may result across models or methods from choosing alternative yet isomorphic representations of a process. One approach can dominate others for comparisons in levels, yet lose to another for differences, to a second for cointegrating vectors and to a third for combinations of variables. The potential for switches in ranking is related to criticisms of the inadequacy of MSFE against encompassing criteria, which are invariant under linear transforms and entail MSFE dominance. An invariant evaluation criterion which avoids misleading outcomes is examined in a Monte Carlo study of forecasting methods.

Professor Michael Clements

Professor Michael Clements

PhD Programme Director

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Article

Empirical analysis of macroeconomic time series: VAR and structural models

Clements, M. and Mizon, G. E. (1991) Empirical analysis of macroeconomic time series: VAR and structural models. European Economic Review, 35 (4). pp. 918-922. ISSN 0014-2921 doi: https://doi.org/10.1016/0014-2921(91)90043-I

Professor Michael Clements

Professor Michael Clements

PhD Programme Director

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Taught modules

Financial Econometrics

Building on the material introduced in Quantitative Methods for Finance, this module covers a number of more advanced techniques that are relevant for financial applications, and in particular for modelling and forecasting financial time series. These include an introduction to maximum likelihood…

Building on the material introduced in Quantitative Methods for Finance, this module covers a number of more advanced techniques that are relevant for financial applications, and in particular for modelling and forecasting financial time series. These include an introduction to maximum likelihood…

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Introductory Econometrics for Finance

This module introduces students to the econometric techniques that are used in the empirical finance literature. Building on Introductory Quantitative Techniques for Finance module, this module aims to give students a solid understanding of the econometric approaches that are commonly employed to…

This module introduces students to the econometric techniques that are used in the empirical finance literature. Building on Introductory Quantitative Techniques for Finance module, this module aims to give students a solid understanding of the econometric approaches that are commonly employed to…

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