We challenge the common practice of estimating gravity equations with interval or averaged data in order to capture dynamic-adjustment effects to trade-policy changes. Instead, we point to a series of advantages of using consecutive-year data recognizing dynamic-adjustment effects. Our analysis reveals that, relative to interval or averaged data, the use of consecutive-year data avoids downward-biased effect estimates due to the distribution of trade-policy events during an event window as well as due to anticipation (preinterval) and delayed (post-interval) effects, and it improves the efficiency of effect estimates due to the use of more data.