This paper uses the approach of Im, Pesaran and Shin (2003) to propose seasonal unit root tests for dynamic heterogeneous panels based on the means of the individuals HEGY test statistics. The standardised t-bar and F-bar statistics are simply averages of the HEGY tests across groups. These...

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...

Procedures are developed to compute the proportion of turning points located in the sample path of time series data. It is shown that the proportion of turning points can be directly related to the data generating process. Methods for estimating model parameters are developed using counts of...

We consider LM-type tests for a unit root allowing for a break in trend at an unknown date. In addition to the minimum LM test statistic, we propose new LM-type tests based on the least squares estimator of the break date under the null. We examine asymptotic behavior under the null hypothesis...

This paper considers an important practical problem in testing time-series data for nonlinearity in mean. Most popular tests reject the null hypothesis of linearity too frequently if the the data are heteroskedastic. Two approaches to redressing this size distortion are considered, both of which...

It is a well accepted fact that stock returns data are often contaminated by market microstructure effects, such as bid-ask spreads, liquidity ratios, turnover, and asymmetric information. This is particularly relevant when dealing with high frequency data, which are often used to compute model...

It is a well accepted fact that stock returns data are often characterized by market microstructure effects, such as bid-ask spreads, liquidity ratios, turnover and asymmetric information. This is particularly relevant when dealing with high frequency data, which are often used to compute model...