This paper provides a general methodology for testing for dependence in time series data, with particular emphasis given to non-Gaussian data. A dynamic model is postulated for a continuous latent variable and the dynamic structure transferred to the non-Gaussian, possibly discrete,...

Most of the literature on testing ARCH models focuses on the null hypothesis of no-ARCH effects. In this paper, we consider the general problem of testing any possible set of coefficient values in ARCH models, which may be non-stationary, with Gaussian and non-Gaussian errors, as well as with...

In this paper we propose a new test statistic that considers multiple structural breaks to analyse the non-stationarity of a panel data set. The methodology is based on the common factor analysis in an attempt to allow for some sort of dependence across the individuals. Thus allowing for...

When a k period future return is regressed on a current variable such as the log dividend yield, the marginal significance level of the t-test that the return is unpredictable typically increases over some range of future return horizons, k. Local asymptotic power analysis shows that the power...

This paper analyzes the performance of heteroskedasticity-and-autocorrelation-consistent (HAC) covariance matrix estimators in which the residuals are prewhitened using a vector autoregressive (VAR) filter. We highlight the pitfalls of using an arbitrarily fixed lag order for the VAR filter, and...

This paper examines experiments on interdependent security prisoner's dilemma games with repeated play. By utilizing a Bayesian hierarchical model, we examine how subjects make investment decisions as a function of their previous experience and their treatment condition. Our main findings are...