Consistent covariance matrix estimation in probit models with autocorrelated errors
AbstractSome recent time-series applications use probit models to measure the forecasting power of a set of variables. Correct inferences about the significance of the variables requires a consistent estimator of the covariance matrix of the estimated model coefficients. A potential source of inconsistency in maximum likelihood standard errors is serial correlation in the underlying disturbances, which may arise, for example, from overlapping forecasts. We discuss several practical methods for constructing probit autocorrelation-consistent standard errors, drawing on the generalized method of moments techniques of Hansen (1982), Newey-West (1987) and others, and we provide simulation evidence that these methods can work well.
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Bibliographic InfoPaper provided by Federal Reserve Bank of New York in its series Staff Reports with number 39.
Date of creation: 1998
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-1998-06-08 (All new papers)
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