Macroeconomic practitioners frequently work with multivariate time series models such as VARs, factor augmented VARs as well as time-varying parameter versions of these models (including variants with multivariate stochastic volatility). These models have a large number of parameters and, thus,...

This paper proposes a new panel unit root test based on the generalized method of moments approach for panels with a small number of time periods and a large number of cross-section units, N. In the model that we consider the deterministic trend function is essentially unrestricted and the...

This paper studies the selection of valid and relevant moments for the generalized method of moments (GMM) estimation. For applications with many candidate moments, our asymptotic analysis accommodates a diverging number of moments as the sample size increases. The proposed procedure achieves...

This paper considers the selection of valid and relevant moments for the generalized method of moments (GMM) estimation. For applications with many candidate moments, our asymptotic analysis ccommodates a diverging number of moments as the sample size increases. The proposed procedure achieves...

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...

In recent years, major advances have taken place in three areas of random utility modeling: (1) semiparametric estimation, (2) computational methods for multinomial probit models, and (3) computational methods for Bayesian stimation. This paper summarizes these developments and discusses their...

In this paper I use a medium scale open economy DSGE model developed by Baksa, Benk and Jakab (2010) for the Hungarian economy. This model provides a notable degree of disaggregation both on the government revenue and expenditure side, being able to capture the shocks that come from fiscal...

We address the problem of parameter estimation for diffusion driven stochastic volatility models through Markov chain Monte Carlo (MCMC). To avoid degeneracy issues we introduce an innovative reparametrisation defined through transformations that operate on the time scale of the diffusion. A...

We address the problem of likelihood based inference for correlated diffusion processes using Markov chain Monte Carlo (MCMC) techniques. Such a task presents two interesting problems. First, the construction of the MCMC scheme should ensure that the correlation coefficients are updated subject...