Predictive density evaluation
V Corradi, NR Swanson - Handbook of economic forecasting, 2006 - Elsevier
This chapter discusses estimation, specification testing, and model selection of predictive
density models. In particular, predictive density estimation is briefly discussed, and a variety …
density models. In particular, predictive density estimation is briefly discussed, and a variety …
Generalized method of moments and macroeconomics
Generalized Method of Moments and Macroeconomics Page 1 Generalized Method of Moments
and Macroeconomics Bruce E. Hansen and Kenneth D. West Department of Economics …
and Macroeconomics Bruce E. Hansen and Kenneth D. West Department of Economics …
Generalized method of moments
AR Hall - A companion to theoretical econometrics, 2003 - Wiley Online Library
Generalized method of moments (GMM) was first introduced into the econometrics literature
by Lars Hansen in 1982. Since then, GMM has had considerable impact on the theory and …
by Lars Hansen in 1982. Since then, GMM has had considerable impact on the theory and …
[图书][B] Resampling methods for dependent data
SN Lahiri - 2013 - books.google.com
This is a book on bootstrap and related resampling methods for temporal and spatial data
exhibiting various forms of dependence. Like the resam pling methods for independent data …
exhibiting various forms of dependence. Like the resam pling methods for independent data …
A new asymptotic theory for heteroskedasticity-autocorrelation robust tests
NM Kiefer, TJ Vogelsang - Econometric Theory, 2005 - cambridge.org
A new first-order asymptotic theory for heteroskedasticity-autocorrelation (HAC) robust tests
based on nonparametric covariance matrix estimators is developed. The bandwidth of the …
based on nonparametric covariance matrix estimators is developed. The bandwidth of the …
HAR inference: Recommendations for practice
The classic papers by Newey and West (1987) and Andrews (1991) spurred a large body of
work on how to improve heteroscedasticity-and autocorrelation-robust (HAR) inference in …
work on how to improve heteroscedasticity-and autocorrelation-robust (HAR) inference in …
Bootstrap methods for time series
The bootstrap is a method for estimating the distribution of an estimator or test statistic by
resampling one's data or a model estimated from the data. The methods that are available …
resampling one's data or a model estimated from the data. The methods that are available …
How useful is bagging in forecasting economic time series? A case study of US consumer price inflation
This article focuses on the widely studied question of whether the inclusion of indicators of
real economic activity lowers the prediction mean squared error of forecasting models of US …
real economic activity lowers the prediction mean squared error of forecasting models of US …
Higher‐Order Improvements of a Computationally Attractive k‐Step Bootstrap for Extremum Estimators
DWK Andrews - Econometrica, 2002 - Wiley Online Library
This paper establishes the higher‐order equivalence of the k‐step bootstrap, introduced
recently by Davidson and MacKinnon (1999), and the standard bootstrap. The k‐step …
recently by Davidson and MacKinnon (1999), and the standard bootstrap. The k‐step …
Bootstrap methods for Markov processes
JL Horowitz - Econometrica, 2003 - Wiley Online Library
The block bootstrap is the best known bootstrap method for time‐series data when the
analyst does not have a parametric model that reduces the data generation process to …
analyst does not have a parametric model that reduces the data generation process to …