This paper is concerned with efficient GMM estimation and inference in GARCH models. Sufficient conditions for the estimator to be consistent and asymptotically normal are established for the GARCH(1,1) conditional variance process. In addition efficiency results are obtained in the general...

This paper is concerned with efficient GMM estimation and inference in GARCH models. Sufficient conditions for the estimator to be consistent and asymptotically normal are established for the GARCH(1,1) conditional variance process. In addition efficiency results are obtained in the general...

The panel cointegration test of Larsson et al (1998) test for the maximum number of cointegrating relations in a dynamic panel given the assumption of a common cointegrating rank. This paper presents a test for this assumption. The test is based on the test statistic of Larsson et al (1998) and...

This paper presents a general likelihood-based framework for inference in panel-VAR models with cointegrating restrictions. The cointegrating relations are restricted to each cross-section while the rest of the model is unrestricted. The homogenous restriction of common cointegrating space is...

This paper presents a likelihood-based panel test of cointegrating rank in heterogeneous panel models based on the mean of the individual rank trace statistics. The existence of the first two moments of the asymptotic distribution of the individual trace statistic is established. Based on this,...

This paper investigates the presence of asymmetric GARCH effects in a number of equity return series, and compare the modeling performance of seven different conditional variance models, within the parametric GARCH class of models. The data consists of daily returns for 45 Nordic stocks, during...

Estimating a density function over a bounded domain can be very complicated and resulting in an unsatisfactory or unrealistic density estimate. In many cases a one-to-one transformation can be applied to the considered data set, but there are also situations where such a unique transformation...

In this paper two techniques, long memory and panel data models, are combined in order to increase the power of unit root tests. The power is shown to be always better against fractional alternatives and usually against autoregressive alternatives. The test is then used to reanalyze data sets...

This paper is concerned with maximum likelihood based inference in random effects models with serial correlation. Allowing for individual effects we introduce serial correlation of general form in the time effects as well as the idiosyncratic errors. A straightforward maximum likelihood...

This paper considers the large sample behavior of the maximum likelihood estimator of random effects models with serial correlation in the form of AR(1) for the idiosyncratic or time-specific error component. Consistent estimation and asymptotic normality as N and/or T grows large is established...