We improve both the specification and estimation of firm-specific betas. Time variation in betas is modeled by combining a parametric specification based on economic theory with a non-parametric approach based on data-driven filters. We increase the precision of individual beta estimates by...

This paper evaluates the dynamic out of sample nominal exchange rate forecasting performance of the canonical New Keynesian model of a small open economy. A novel Bayesian procedure for jointly estimating the hyperparameters and trend components of a state space representation of an approximate...

The purpose of this paper is twofold: 1) to highlight the widely ignored but fundamental problem of ‘superpopulations’ for the use of inferential statistics in development studies. We do not to dwell on this problem however as it has been sufficiently discussed in older papers by...

This paper assesses the ability of different models to forecast key real and nominal U.S. monthly macroeconomic variables in a data-rich environment and from the perspective of a real-time forecaster, i.e. taking into account the real-time data revisions process and data flow. We find that for...

Our goal in this chapter is to explain concretely how to implement simulation methods in a very general class of models that are extremely useful in applied work: dynamic discrete choice models where one has available a panel of multinomial choice histories and partially observed payoffs....

We consider a set of minimal identification conditions for dynamic factor models. These conditions have economic interpretations, and require fewer number of restrictions than when putting in a static-factor form. Under these restrictions, a standard structural vector autoregression (SVAR) with...

This paper studies the diffusion of knowledge and its consequences for local innovation production. In a common framework, we analyze the geographic reach of different channels of knowledge flows that thus far have been studied separately in the literature. To jointly estimate these flows, we...

We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of restricted PVARs where the restrictions involve interdependencies between and heterogeneities...

There is a vast literature that specifies Bayesian shrinkage priors for vector autoregressions (VARs) of possibly large dimensions. In this paper I argue that many of these priors are not appropriate for multi-country settings, which motivates me to develop priors for panel VARs (PVARs). The...

This paper offers a methodology to address the endogeneity of inputs in the directional technology distance function (DTDF) based formulation of banking technology which explicitly accommodates the presence of undesirable nonperforming loans --- an inherent characteristic of the bank's...