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We review the literature on robust Bayesian analysis as a tool for global sensitivity analysis and for statistical decision-making under ambiguity. We discuss the methods proposed in the literature, including the different ways of constructing the set of priors that are the key input of the...
Persistent link: https://www.econbiz.de/10014048660
We provide a methodology for testing a polynomial model hypothesis by extending the approach and results of Baek, Cho, and Phillips (2015; Journal of Econometrics; BCP) that tests for neglected nonlinearity using power transforms of regressors against arbitrary nonlinearity. We examine and...
Persistent link: https://www.econbiz.de/10014123918
We study semi-parametric estimation and inference in cointegrated panels with endogenous feedback, allowing for general time-series and cross-section dependence and heterogeneity.Central to this literature are the fully-modified OLS of Phillips and Hansen (1990) that use a spectral...
Persistent link: https://www.econbiz.de/10012970628
We study parameter estimation from the sample X, when the objective is to maximize the expected value of a criterion function, Q, for a distinct sample, Y. This is the situation that arises when a model is estimated for the purpose of describing other data than those used for estimation. The...
Persistent link: https://www.econbiz.de/10012919208
We extend the results of De Luca et al. (2021) to inference for linear regression models based on weighted-average least squares (WALS), a frequentist model averaging approach with a Bayesian flavor.We concentrate on inference about a single focus parameter, interpreted as the causal effect of a...
Persistent link: https://www.econbiz.de/10013228440
We propose an iterative procedure to efficiently estimate models with complex log-likelihood functions and the number of parameters relative to the observations being potentially high. Given consistent but inefficient estimates of sub-vectors of the parameter vector, the procedure yields...
Persistent link: https://www.econbiz.de/10013060048
Credible Granger-causality analysis appears to require post-sample inference, as it is well-known that in-sample t can be a poor guide to actual forecasting e ffectiveness. But post-sample model testing requires an often-consequential a priori partitioning of the data into an `in-sample' period...
Persistent link: https://www.econbiz.de/10013063521
Though ordinary least square (OLS) estimates are super-consistent with cointegrated variables, their finite-T bias can be large in the presence of endogenous feedback. Fully modified OLS (FMOLS) are parsimonious tools to measure the cointegrating [long-run] slope between integrated variables in...
Persistent link: https://www.econbiz.de/10013064659
This article investigates a bracketing property that purports to yield upper- and lower bounds on the treatment effects obtained from a fixed effects- and lagged dependent variable model. With reference to both analytical results and a Monte Carlo simulation, we explore the conditions under...
Persistent link: https://www.econbiz.de/10014346130
Credible Granger-causality analysis appears to require post-sample inference, as it is well-known that in-sample fit can be a poor guide to actual forecasting effectiveness. However, post-sample model testing requires an often-consequential a priori partitioning of the data into an "in-sample"...
Persistent link: https://www.econbiz.de/10010336194