Spectral density bandwidth choice and prewhitening in the generalized method of moments estimators for the asset pricing model

Min-Hsien Chiang, Chihwa Kao

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper investigates the performances of GMM estimates using kernel methods with and without prewhitening and the VARHAC method in a representative agent exchange economy. A Monte Carlo study is conducted to evaluate the issues of estimating the spectral density functions, e.g., parametric vs. nonparametric, data-based bandwidth selection, and prewhitening procedures. The Monte Carlo results show that kernel methods with prewhitening procedure outperform others in terms of statistical inferences. The deviations from true parameter values, however, are larger for kernel methods with prewhitening procedure. Therefore, there exists efficiency/bias trade-offwhen choosing HAC covariance estimation method.

Original languageEnglish
JournalEconomics Bulletin
Volume3
Issue number1
Publication statusPublished - 2005 Dec 1

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Generalized method of moments estimator
Kernel methods
Spectral density
Asset pricing models
Bandwidth
Deviation
Statistical inference
Representative agent
Density function
Monte Carlo study
Exchange economy

All Science Journal Classification (ASJC) codes

  • Economics, Econometrics and Finance(all)

Cite this

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abstract = "This paper investigates the performances of GMM estimates using kernel methods with and without prewhitening and the VARHAC method in a representative agent exchange economy. A Monte Carlo study is conducted to evaluate the issues of estimating the spectral density functions, e.g., parametric vs. nonparametric, data-based bandwidth selection, and prewhitening procedures. The Monte Carlo results show that kernel methods with prewhitening procedure outperform others in terms of statistical inferences. The deviations from true parameter values, however, are larger for kernel methods with prewhitening procedure. Therefore, there exists efficiency/bias trade-offwhen choosing HAC covariance estimation method.",
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AB - This paper investigates the performances of GMM estimates using kernel methods with and without prewhitening and the VARHAC method in a representative agent exchange economy. A Monte Carlo study is conducted to evaluate the issues of estimating the spectral density functions, e.g., parametric vs. nonparametric, data-based bandwidth selection, and prewhitening procedures. The Monte Carlo results show that kernel methods with prewhitening procedure outperform others in terms of statistical inferences. The deviations from true parameter values, however, are larger for kernel methods with prewhitening procedure. Therefore, there exists efficiency/bias trade-offwhen choosing HAC covariance estimation method.

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