Autocorrelation-Corrected Standard Errors in Panel Probits [electronic resource] : An Application to Currency Crisis Prediction / Andrew Berg.

By: Berg, AndrewContributor(s): Coke, Rebecca NMaterial type: TextTextSeries: IMF Working Papers; Working Paper ; No. 04/39Publication details: Washington, D.C. : International Monetary Fund, 2004Description: 1 online resource (21 p.)ISBN: 1451845863 :ISSN: 1018-5941Subject(s): Bootstrap | Correlation | Currency Crisis | Early-Warning Systems | Macroeconomic Aspects of International Trade and Finance: Forecasting and Simulation | Panel Probit | Argentina | Taiwan Province of ChinaAdditional physical formats: Print Version:: Autocorrelation-Corrected Standard Errors in Panel Probits : An Application to Currency Crisis PredictionOnline resources: IMF e-Library | IMF Book Store Abstract: Many estimates of early-warning-system (EWS) models of currency crisis have reported incorrect standard errors because of serial correlation in the context of panel probit regressions. This paper documents the magnitude of the problem, proposes and tests a solution, and applies it to previously published EWS estimates. We find that (1) the uncorrected probit estimates substantially underestimate the true standard errors, by up to a factor of four; (2) a heteroskedasicity- and autocorrelation-corrected (HAC) procedure produces accurate estimates; and (3) most variables from the original models remain significant, though substantially less so than had been previously thought.
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Many estimates of early-warning-system (EWS) models of currency crisis have reported incorrect standard errors because of serial correlation in the context of panel probit regressions. This paper documents the magnitude of the problem, proposes and tests a solution, and applies it to previously published EWS estimates. We find that (1) the uncorrected probit estimates substantially underestimate the true standard errors, by up to a factor of four; (2) a heteroskedasicity- and autocorrelation-corrected (HAC) procedure produces accurate estimates; and (3) most variables from the original models remain significant, though substantially less so than had been previously thought.

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