A Monte Carlo study is undertaken to analyze the small sample properties of two time domain and two frequency domain estimators for low order autoregressive fractionally integrated moving average Gaussian models, ARFIMA (p,d,q). More specifically (1,0,0), (0,0,1), (0,d,0), various (2,0,0),...

In this paper, we modify wavelets so that wavelet coefficients become statistically independent. Under standard regularity conditions, we determine the distribution of the wavelet coefficients. Using the moving average representation of the ARFIMA(p,d,q) process, we obtain wavelet-based exact...

This paper derives second-order expansions for the distributions of the Whittle and profile plug-in maximum likelihood estimators of the fractional difference parameter in the ARFIMA(0,d,0) with unknown mean and variance. Both estimators are shown to be second-order pivotal. This extends earlier...

We consider Particle Gibbs (PG) as a tool for Bayesian analysis of non-linear non-Gaussian state-space models. PG is a Monte Carlo (MC) approximation of the standard Gibbs procedure which uses sequential MC (SMC) importance sampling inside the Gibbs procedure to update the latent and potentially...

In this paper, we study identification and misspecification problems in standard closed and open-economy empirical New-Keynesian DSGE models used in monetary policy analysis. We find that problems with model misspecification still appear to be a first-order issue in monetary DSGE models, and...

This paper considers estimation methods and inference for linear dynamic panel data models with unit-specific heterogeneity and a short time dimension. In particular, we focus on the identification of the coefficients of time-invariant variables in a dynamic version of the Hausman and Taylor...

This paper considers estimation methods and inference for linear dynamic panel data models with unit-specific heterogeneity and a short time dimension. In particular, we focus on the identification of the coefficients of time-invariant variables in a dynamic version of the Hausman and Taylor...

The multinomial logit model with random coefficients is widely used in applied research. This paper is concerned with estimating a random coefficients logit model in which the distribution of each coefficient is characterized by finitely many parameters. Some of these parameters may be zero. The...

This paper develops an unbiased Monte Carlo approximation to the transition density of a jump-diffusion process with state-dependent drift, volatility, jump intensity, and jump magnitude. The approximation is used to construct a likelihood estimator of the parameters of a jump-diffusion observed...

This paper revisits the panel autoregressive model, with a primary emphasis on the unit-root case. We study a class of misspecified Random effects Maximum Likelihood (mRML) estimators when T is either fixed or large, and N tends to infinity. We show that in the unit-root case, for any fixed...