I model the strategic interactions between the manager of a firm and an outside investor in a dynamic cheap talk game with two-sided asymmetric information. Each period, the investor selectively discloses his information to influence the manager's capital investment decision. While the manager knows that she can learn from the investor's disclosures, she also knows that the investor is trying to manipulate her; in equilibrium, the investor's incentives to mislead the manager constrain the credibility of his disclosures, leading to a mutually-deleterious loss of information. I compare the set of cheap talk equilibria with the Bayesian persuasion equilibrium. My model has implications for short-termism, management guidance, and investor credibility over the business cycle.
Available upon request.
I present a cheap talk model in which an informed sender strategically communicates with an uninformed receiver. This model nests Crawford and Sobel’s (1982) lauded uniform-quadratic environment. Counterintuitively, I show that if the sender’s optimal action is less sensitive to the state of nature, then communication will be least informative in states where the sender’s and receiver’s preferences are most aligned.
Best Paper Award, Utah Winter Finance Conference
Instrumental variable (IV) estimates are typically much larger than their ordinary least squares (OLS) counterparts, often suggesting implausible values of the omitted variable bias. We derive a meta-regression of OLS on IV estimates that can resolve this puzzle by separately identifying omitted variable bias from measurement error in the endogenous regressor. We show that meta-regression estimates can also reveal when the underlying instruments are invalid. We apply the meta-regression to four published papers. In three of them, measurement error is quantitatively important, while omitted variable bias is negligible; in the fourth paper, the instruments appear to be invalid.
We highlight the importance of measurement error in applied empirical work using 2,185 instrumental variable regressions from 326 papers published in top economics and finance journals. We derive meta-regression estimators that distinguish measurement error from omitted variable bias and simultaneity bias, and find that 50–80% of the variance of the average regressor is noise, even after accounting for weak instruments and publication bias. Our results suggest that measurement error is a severe, pervasive, and understated source of bias in economics and finance, and researchers can obtain substantially less-biased estimates simply by obtaining better proxy variables.
Instrumental variables estimators are commonly used in economics and finance to establish causal relationships. Although instruments that fail the exclusion restriction do not reliably estimate parameters of interest, testing the exclusion restriction is uncommon, due to the difficulty of finding multiple valid instruments. We derive closed-form instrumental variable estimators that allow for tests of over-identifying restrictions even for the case of a single valid instrument. We also derive estimators that are consistent when instruments and regressors are mis-measured with correlated errors. Monte Carlo simulations suggest that our estimators have power to reject even in relatively small samples. We also apply our estimators to the IV regressions of Mian & Sufi (2014) and cannot reject the null hypothesis that the exclusion restriction holds.