We propose computing HAC covariance matrix estimators based on one-stepahead forecasting errors. It is shown that this estimator is consistent and has smaller bias than other HAC estimators. Moreover, the tests that rely on this estimator have more accurate sizes without sacrificing its power.

This paper applies a neural network model to intraday data of exchange rate futures between January 1990 and July 1992. We use the neural network model and the "lag selection method" to explore the intraday patterns of futures market exchange rates. The approach is particularly useful in...

We propose an encompassing test for non-nested linear quantile regression models and show that it has an asymptotic [chi]2 distribution. It is also shown that the proposed test is a regression rank score test in a comprehensive model under conditional homogeneity. Our simulation results indicate...

In the finance literature, statistical inferences for large-scale testing problems usually suffer from data snooping bias. In this paper we extend the "superior predictive ability" (SPA) test of Hansen (2005, JBES) to a stepwise SPA test that can identify predictive models without potential data...

A quasi-maximum likelihood estimator of the break date is analyzed. Consistency of the estimator is demonstrated under very general conditions, provided that the data-generating process is not integrated. However, the asymptotic distribution of the estimator is quite different for time series...