Abstract: Using 1998 trade and quote data for securities listed on the Toronto Stock Exchange, this paper employs nonparametric estimation to measure the effect of being interlisted on a US exchange on: (i) the daily number of trades, trading volume, and dollar trading volume; (ii) the number of inside quote revisions and the percentage bid-ask spread; (iii) registered trader gross trading revenues; (iv) composition of order flow between orders submitted for client, non-client, and registered trader accounts. Unlike previous studies, I use kernel-based matching estimates in addition to variants of the standard nearest-neighbor approach for constructing matched samples of interlisted stocks and non-interlisted stocks. I explore the sensitivity of results to: (i) using different bandwidth parameters and caliper-matching criteria; (ii) using different matching characteristics; (iii) the exclusion/inclusion of firms. I highlight instances when kernel-based and nearest-neighbor matching estimation techniques produce significantly different results and thereby argue that results based on standard matching techniques commonly employed in the finance literature should be interpreted cautiously.