Autocorrelation-Corrected Standard Errors in Panel Probits: An Application to Currency Crisis Prediction

March 1, 2004

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Format: Chicago

Andrew Berg, and Rebecca N. Coke Autocorrelation-Corrected Standard Errors in Panel Probits: An Application to Currency Crisis Prediction, (USA: International Monetary Fund, 2004) accessed September 18, 2024

Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate

Summary

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.

Subject: Currency crises, Early warning systems, Econometric analysis, Estimation techniques, Export performance, Financial crises, Foreign exchange, International trade, Real exchange rates

Keywords: Bootstrap estimate, Bootstrap estimator, Coefficient estimate, Covariance estimator, Currency crises, Currency crisis, Early warning systems, Early-warning systems, Estimation techniques, Export performance, HAC estimate, Panel probit, Real exchange rates, Serial correlation, Standard error estimate, WP

Publication Details

Pages:

21
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2004/039
Stock No:

WPIEA0392004
ISBN:

9781451845860
ISSN:

1018-5941

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