Journal ArticleDOI
GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study
TLDR
In this paper, the authors examined alternative generalized method of moments procedures for estimation of a stochastic autoregressive volatility model by Monte Carlo methods and provided guidelines that help achieve desirable small-sample properties in settings characterized by strong conditional heteroscedasticity and correlation among the moments.Abstract:
We examine alternative generalized method of moments procedures for estimation of a stochastic autoregressive volatility model by Monte Carlo methods. We document the existence of a tradeoff between the number of moments, or information, included in estimation and the quality, or precision, of the objective function used for estimation. Furthermore, an approximation to the optimal weighting matrix is used to explore the impact of the weighting matrix for estimation, specification testing, and inference procedures. The results provide guidelines that help achieve desirable small-sample properties in settings characterized by strong conditional heteroscedasticity and correlation among the moments.read more
Citations
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Book
Econometric Analysis of Panel Data
TL;DR: In this article, the authors proposed a two-way error component regression model for estimating the likelihood of a particular item in a set of data points in a single-dimensional graph.
Journal ArticleDOI
How to do Xtabond2: An Introduction to Difference and System GMM in Stata
TL;DR: This pedagogic paper first introduces linear GMM, and shows how limited time span and the potential for fixed effects and endogenous regressors drive the design of the estimators of interest, offering Stata-based examples along the way.
Journal ArticleDOI
How to do xtabond2: An introduction to difference and system GMM in Stata
TL;DR: This paper introduced linear generalized method of moments (GMM) estimators for situations with small T, large N panels, with independent variables that are not strictly exogenous, meaning correlated with past and possibly current realizations of the error; with fixed effects; and with heteroskedasticity and autocorrelation within individuals.
Posted Content
Estimation and Inference in Econometrics
TL;DR: A theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification.
Journal ArticleDOI
A Note on the Theme of Too Many Instruments
TL;DR: This article reviewed the evidence on the effects of instrument proliferation, and described and simulated simple ways to control it, and illustrated the dangers by replicating Forbes [American Economic Review (2000) Vol. 90, pp. 869-887] on income inequality and Levine et al. [Journal of Monetary Economics] (2000] Vol. 46, pp 31-77] on financial sector development.
References
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Journal ArticleDOI
Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
ReportDOI
A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix
Whitney K. Newey,Kenneth D. West +1 more
TL;DR: In this article, a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction is described.
Journal ArticleDOI
Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev,Tim Bollerslev +1 more
TL;DR: In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.
Journal ArticleDOI
Large sample properties of generalized method of moments estimators
Journal ArticleDOI
Conditional heteroskedasticity in asset returns: a new approach
TL;DR: In this article, an exponential ARCH model is proposed to study volatility changes and the risk premium on the CRSP Value-Weighted Market Index from 1962 to 1987, which is an improvement over the widely-used GARCH model.