Item response theory is one of the modern test theories with applications in educational and psychological testing. Recent developments made it possible to characterize some desired properties in terms of a collection of manifest ones, so that hypothesis tests on these traits can, in principle,...

Persistent regressors pose a common problem in predictive regressions. Tests of the forward rate unbiased hypothesis (FRUH) constitute a prime example. Standard regression tests that strongly reject FRUH have been questioned on the grounds of potential long-memory in the forward premium....

In this paper, I examine the properties of the class of generalized empirical likelihood estimators of moment-condition models. These nonparametric likelihood estimators satisfy exactly the moment conditions and automatically remove any bias due to a lack of centering. Moreover, the bias of the...

Tests of overidentifying restrictions are widely used in practice. However, there is often confusion about the nature of their null hypothesis and about the interpretation of their outcome. In this note we argue that these tests give little information on whether the instruments are correlated...

We propose a novel statistic to test the rank of a matrix. The rank statistic overcomes deficiencies of existing rank statistics, like: a Kronecker covariance matrix for the canonical correlation rank statistic of Anderson [Annals of Mathematical Statistics (1951), 22, 327–351] sensitivity to...

While stochastic volatility models improve on the option pricing error when compared to theBlack-Scholes-Merton model, mispricings remain. This paper uses mixed normalheteroskedasticity models to price options. Our model allows for significant negative skewnessand time varying higher order...

This paper analyzes the time-varying parameter vector autoregressive (TVP-VAR) model for the Japanese economy and monetary policy. The time-varying parameters are estimated via the Markov chain Monte Carlo method and the posterior estimates of parameters reveal the time-varying structure of the...

This paper proposes the EGARCH model with jumps and heavy- tailed errors, and studies the empirical performance of different models including the stochastic volatility models with leverage, jumps and heavy-tailed errors for daily stock returns. In the framework of a Bayesian inference, the...

In this paper we replace the Gaussian errors in the standard Gaussian, linear state space model with stochastic volatility processes. This is called a GSSF-SV model. We show that conventional MCMC algorithms for this type of model are ineffective, but that this problem can be removed by...