Measurement Error in Dependent Variables in Accounting: Illustrations Using Google Ticker Search and Simulations

This paper illustrates how measurement error (“ME”) in dependent variables not only reduces power but, under common conditions in accounting and finance studies, can lead to statistical biases and erroneous inferences. These confounds exist because ME in accounting-based proxies is typically nonadditive, which violates the simple assumptions discussed in most econometrics texts. We demonstrate the effects of nonadditive ME in papers using Google ticker search volume index (“SVI”) as a measure of investor attention. We show that ME in SVI generates both type I and II errors in published studies, and we introduce a new measure of investor-specific ticker search to reduce biases in future research. We also use simulations to show that small amounts of ME in accounting asset values can confound inferences in commonly-used accounting-based proxies such as ROA and Tobin’s Q. Our findings contribute to the literature by improving researchers’ understanding of the effects of ME in common analyses.