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

Khanthavit, Anya

doi:10.21315/aamjaf2017.13.1.4

Cited by 2 publications

(1 citation statement)

References 42 publications

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“…For a long sample period, the assumption is unlikely. To lessen the effects of the incorrect assumption, I follow Khanthavit (2017) to estimate the models using sample periods of one year at a time. The Wald statistics for a full sample test are the sum of statistics for all the N years in the full period.…”

Section: A Fixed-effect Assumptionmentioning

confidence: 99%

Weather-Induced Moods and Stock-Return Autocorrelation

Khanthavit

2020

Zagreb International Review of Economics and Business

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Moods affect investors’ attention, memory, and capacity to process information. Inattentive investors delay the price adjustment process, thus leading to a positive autocorrelation of asset returns. In this study, I investigate the relationship between weather-induced moods and stock-return autocorrelation in the Stock Exchange of Thailand from January 2, 1991, to December 29, 2017. Only good moods contribute significantly to return autocorrelation.

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Section: A Fixed-effect Assumptionmentioning

confidence: 99%

Weather-Induced Moods and Stock-Return Autocorrelation

Khanthavit

2020

Zagreb International Review of Economics and Business

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Rain, Rain, Go Away: 137 Potential Exclusion-Restriction Violations for Studies Using Weather as an Instrumental Variable

Mellon

2020

SSRN Journal

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Instrumental variable (IV) analysis assumes that the instrument only affects the dependent variable via its relationship with the independent variable. Other possible causal routes from the IV to the dependent variable are exclusion-restriction violations and make the instrument invalid. Weather has been widely used as an instrumental variable in social science to predict many different variables. The use of weather to instrument different independent variables represents strong prima facie evidence of exclusion violations for all studies using weather as an IV. A review of 217 social science studies reveals 176 variables which have been linked to weather, all of which represent potential exclusion violations. I conclude with practical steps to systematically review existing literature to identify possible exclusion violations when using IV designs. I demonstrate how sensitivity analysis can quantify the vulnerability of a particular IV estimate to exclusion restriction violations in the literature.

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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)

Cited by 2 publications

References 42 publications

Weather-Induced Moods and Stock-Return Autocorrelation

Weather-Induced Moods and Stock-Return Autocorrelation

Rain, Rain, Go Away: 137 Potential Exclusion-Restriction Violations for Studies Using Weather as an Instrumental Variable

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