In this study, we examine how information provision affects the degree of overconfidence using an online experiment. The 4,210 experimental participants engaged in stock market prediction exercises were asked to evaluate their absolute and relative performance. We conducted a randomized...

This paper proposes asymptotically unbiased estimators of autocovariances and autocorrelations for panel data with both individual and time effects. We show that the conventional autocovariance estimators suffers from the bias caused by the elimination of individual and time effects. The bias...

In this paper, we propose a doubly robust method to estimate the heterogeneity of the average treatment effect with respect to observed covariates of interest. We consider a situation where a large number of covariates are needed for identifying the average treatment effect but the covariates of...

We consider instrumental variables estimation of a possibly infinite order dynamic panel autoregressive (AR) process with individual effects. The estimation is based on the sieve AR approximation with its lag order increasing with sample size. Transforming the variable to eliminate individual...

An important reason for analyzing panel data is to observe the dynamic nature of an economic variable separately from its time-invariant unobserved heterogeneity. This paper examines how to estimate the autocovariances of a variable separately from its time-invariant unobserved heterogeneity....

This paper considers specification testing for instrumental variables estimation in the presence of many instruments. The test proposed is a modified version of the Sargan (1958, Econometrica 26(3): 393-415) test of overidentifying restrictions. The test statistic asymptotically follows the...

This paper considers model averaging as a way to construct optimal instruments for the two-stage least squares (2SLS), limited information maximum likelihood (LIML), and Fuller estimators in the presence of many instruments. We propose averaging across least squares predictions of the endogenous...

We propose a nonparametric test for Granger-type causality. A conceptually similar work will be Hidalgo (2000), who introduced a nonparametric Granger causality test in the frequency domain for weakly stationary linear processes. He is mainly concerned with the test under long range dependent...