The fixed-b asymptotic framework provides refinements in the use of heteroskedasticity and autocorrelation consistent variance estimators. We show however that the fixed-b limiting distributions of t-statistics are not pivotal when the variance of the underlying data generating process changes...

PRELIMINARY DRAFT We discuss maximum likelihood (ML) analysis for panel count data models, in which the observed counts are linked via a measurement density to a latent Gaussian process with spatial as well as temporal dynamics and random effects. For likelihood evaluation requiring...

This research applies data from the Livingston survey to study the time variation in the sentiment of U.S. stock-market forecasters. A Panel Smooth Transition Regression (STR) model is estimated to identify the importance of market conditions summarized by stock-market misalignments and recent...

Following the seminal work by Bullard and Sebald [Effects of Parametric Uncertainty and Technological Change on In put-Out put Models, Rev. of Ec. And Stat., vol. 59,75-81], in this paper we present an innovative approach to sensitivity analysis in Input-Out put model. In particular, we propose...

The traditional approach to estimate spatial models bases on a preconceived spatial weights matrix to measure spatial interaction among locations. The a priori assumptions used to define this matrix are supposed to be in line with the "true" spatial relationships among the locations of the...

In this paper, we evaluate the spatial location patterns of Spanish manufacturing firms and we assess the different tendencies to cluster in each industry relative to the whole of manufacturing. To do this, we use a distance-based method (Marcon and Puech, 2003; Duranton and Overman, 2005), more...

This study develops an easy forecasting model using prefectural data in Japan. The Markov chain known as a stochastic model corresponds to the vector auto-regressive (VAR) model of the first order. If the transition probability matrix can be appropriately estimated, the forecasting model using...

We generalize the basic Wishart multivariate stochastic volatility model of Philipov and Glickmann (2006) to encompass regime switching behavior. The latent state variable is driven by a first-order Markov process. In order to estimate the proposed model we use Bayesian Markov Chain Monte Carlo...

The superiority of full information approaches when estimating a system of equation is well known for large samples. However, less is known about the small sample properties of these estimators relative to limited information approachs. This is especially true for the context of Panel data...

In this paper, we build a Computable General Equilibrium (CGE)-microsimulation model for the economy of Nicaragua following the Top-Down approach (see Bourguignon et al., 2003), that is, the reform is simulated first at the macro level with the CGE model, and then it is passed onto the...