This paper documents that ECB announcements increase the stock market volatility in the euro area (EA) on the same day. I consider two volatility measures from January 1998 to May 2019. First, a realized volatility measure uses intraday data for 8 different stock market indices. Second, a range...

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...

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 topic of this paper is the estimation uncertainty of the Stock-Watson and Gonzalo-Granger permanent-transitory decompositions in the framework of the co-integrated vector autoregression. We suggest an approach to construct the confidence interval of the transitory component estimate in a...

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...