We study whether a firm’s total factor productivity dynamics is positively influenced by its own R&D activity and by the technological spillovers generated at the intra- and inter-sectorial level. Our approach corrects simultaneously for the endogeneity and the selectivity biases introduced by...

This paper critically appraises the di erent approaches that have characterized the literature on the macroeconomic e ects of job reallocations from Lilien's seminal work to recent developments rooted in structural general equilibrium models, nonlinear econometric techniques and the concepts of...

This paper critically appraises the approaches that have characterized the literature on the macroeconomic effects of job reallocations. Since Lilien's (1982) seminal contribution there has been a flourishing of empirical analysis but no unifying theoretical framework has obtained consensus in...

In this paper we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also...

We develop methods for Bayesian inference in vector error correction models which are subject to a variety of switches in regime (e.g. Markov switches in regime or structural breaks). An important aspect of our approach is that we allow both the cointegrating vectors and the number of...

The Hodrick-Prescott (HP) method is a popular smoothing method for economic time series to get a smooth or long-term component of stationary series like growth rates. We show that the HP smoother can be viewed as a Bayesian linear model with a strong prior using differencing matrices for the...

The Hodrick-Prescott (HP) method is a popular smoothing method for economic time series to get a longterm component of stationary series like growth rates. The new extended HP smoothing model is applied to data-sets with an underlying metric and requires a Bayesian linear regression model with a...

This paper proposes an infinite hidden Markov model (iHMM) to detect, date stamp, and estimate speculative bubbles. Three features make this new approach attractive to practitioners. first, the iHMM is capable of capturing the nonlinear dynamics of different types of bubble behaviors as it...