Pretest with Caution: Event-Study Estimates after Testing for Parallel Trends

This paper discusses two important limitations of the common practice of testing for preexisting differences in trends (“ pre-trends”) when using difference-in-differences and related methods. I analyze these issues both in theory and in simulations calibrated to a survey of recent papers in leading economics journals, which suggest that these limitations are important in practice.

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• 1 First, conventional pre-trends tests may have low power.
• 2 Second, conditioning the analysis on the result of a pretest can distort estimation and inference, potentially exacerbating the bias of point estimates and under-coverage of confidence intervals.
• 3 I analyze these issues both in theory and in simulations calibrated to a survey of recent papers in leading economics journals, which suggest that these limitations are important in practice.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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