Instrumental-Variable Estimation of Bangkok-Weather Effects in the Stock Exchange of Thailand

Abstract

The incorrect fixed-effect assumption, missing-data problem, omitted-variable problem, and errors-in-variables (EIV) problem are estimation problems that are generally found in studies on weather effects on asset returns. This study proposes an approach that can address these problems simultaneously. The approach is demonstrated by revisiting the effects on the Stock Exchange of Thailand. The sample shows daily data from 2 January 1991 to 30 December 2015. Artificial Hausman instrumental-variable regressions successfully improve the quality of the analyses for ordinary least squares regressions when significant EIV problems are identified and the regression results in a conflict. The study finds significant air pressure and rainfall effects and empirically shows that the temperature effects reported by previous studies were induced by the fixed-effect assumption and are therefore incorrect.