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In specifying a regression equation, we need to determine which regressors to include, but also how these regressors are measured. This gives rise to two levels of uncertainty: concepts (level 1) and measurements within each concept (level 2). In this paper we propose a hierarchical weighted...
Persistent link: https://www.econbiz.de/10014172813
Empirical growth research faces a high degree of model uncertainty. Apart from the neoclassical growth model, many new (endogenous) growth models have been proposed. This causes a lack of robustness of the parameter estimates and makes the determination of the key determinants of growth...
Persistent link: https://www.econbiz.de/10012724577
This book presents in detail methodologies for the Bayesian estimation of single-regime and regime-switching GARCH models. These models are widespread and essential tools in financial econometrics and have, until recently, mainly been estimated using the classical Maximum Likelihood technique....
Persistent link: https://www.econbiz.de/10013156202
A common problem in estimating dynamic stochastic general equilibrium models is that the structural parameters of economic interest are only weakly identified. As a result, classical confidence sets and Bayesian credible sets will not coincide even asymptotically, and the mean, mode, or median...
Persistent link: https://www.econbiz.de/10011757054
This paper proposes a new estimator for least squares model averaging. A model average estimator is a weighted average of common estimates obtained from a set of models. We propose computing weights by minimizing a model average prediction criterion (MAPC). We prove that the MAPC estimator is...
Persistent link: https://www.econbiz.de/10009668445
Oil market VAR models have become the standard tool for understanding the evolution of the real price of oil and its impact in the macro economy. As this literature has expanded at a rapid pace, it has become increasingly difficult for mainstream economists to understand the differences between...
Persistent link: https://www.econbiz.de/10012174841
show that PAE have minimum worst-case bias under local misspecification of the parametric distribution of unobservables … posterior conditioning, which quantifies the bias of PAE relative to parametric modelbased estimators, and we study other …
Persistent link: https://www.econbiz.de/10012295267
-case specification error under various forms of misspecification of the parametric distribution of unobservables. In addition, we …
Persistent link: https://www.econbiz.de/10012617686
We show that in parametric likelihood models the first order bias in the posterior mode and the posterior mean can be … removed using objective Bayesian priors. These bias-reducing priors are defined as the solution to a set of differential … differential equations asymptotically and removes the first order bias. When we consider the posterior mode, this approach can be …
Persistent link: https://www.econbiz.de/10014026617
Koop, Pesaran and Smith (2011) suggest a simple diagnostic indicator for the Bayesian estimation of the parameters of a DSGE model. They show that, if a parameter is well identified, the precision of the posterior should improve as the (artificial) data size T increases, and the indicator checks...
Persistent link: https://www.econbiz.de/10009490720