This paper proposes a method for comparing and combining conditional quantile forecasts in an out-of-sample framework. We construct a Conditional Quantile Forecast Encompassing (CQFE) test as a Wald-type test of superior predictive ability. Rejection of CQFE provides a basis for combination of...

This paper proposes tests for comparing the accuracy of density forecasts. The evaluation makes use of scoring rules, which are loss functions defined over the density forecast and the realizations of the variable. In particular, a logarithmic scoring rule leads to the development of asymptotic...

We discuss instrumental variables (IV) estimation in the broader context of the generalized method of moments (GMM), and describe an extended IV estimation routine that provides GMM estimates as well as additional diagnostic tests. Stand-alone test procedures for heteroskedasticity,...

We extend our 2003 paper on instrumental variables (IV) and GMM estimation and testing and describe enhanced routines that address HAC standard errors, weak instruments, LIML and k-class estimation, tests for endogeneity and RESET and autocorrelation tests for IV estimates.

In this paper we develop some econometric theory for factor models of large dimensions. The focus is the determination of the number of factors, which is an unresolved issue in the rapidly growing literature on multifactor models. We propose some panel C(p) criteria and show that the number of...

The assumption of conditional symmetry is often invoked to validate adaptive estimation and consistent estimation of ARCH/GARCH models by quasi maximum likelihood. Imposing conditional symmetry can increase the efficiency of bootstraps if the symmetry assumption is valid. This paper proposes a...

This paper develops an asymptotic theory of inference for a two-regime threshold autoregressive (TAR) model with an autoregressive root which is local-to-unity. We find that the asymptotic null distribution of the Wald test for a threshold is non-standard and mildly dependent on the...

This note corrects an error in Hansen, Journal of Applied Econometrics (1992)

Monte Carlo experiments establish that the usual "t-statistic" used for testing for first-order serial correlation with artificial regressions is far from being distributed as a Student's t in small samples. Rather, it is badly biased in both mean and variance and results in grossly misleading...

We present the sampling distributions for the coefficient of skewness, kurtosis, and a joint test of normality for time series observations. In contrast to independent and identically distributed data, the limiting distributions of the statistics are shown to depend on the long run rather than...