Which Moments to Match?

@article{Gallant1995WhichMT,
  title={Which Moments to Match?},
  author={A. Ronald Gallant and George Tauchen},
  journal={Econometric Theory},
  year={1995},
  volume={12},
  pages={657 - 681}
}
We describe an intuitive, simple, and systematic approach to generating moment conditions for generalized method of moments (GMM) estimation of the parameters of a structural model. The idea is to use the score of a density that has an analytic expression to define the GMM criterion. The auxiliary model that generates the score should closely approximate the distribution' of the observed data but is not required to nest it. If the auxiliary model nests the structural model then the estimator is… 

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References

SHOWING 1-10 OF 49 REFERENCES

Semi-nonparametric Maximum Likelihood Estimation

Often maximum likelihood is the method of choice for fitting an econometric model to data but cannot be used because the correct specific ation of (multivariate) density that defines the likelihood

Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances

We study the properties of the quasi-maximum likelihood estimator (QMLE) and related test statistics in dynamic models that jointly parameterize conditional means and conditional covariances, when a

A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration

This paper proposes a simple modification of a conventional generalized method of moments estimator for a discrete response model, replacing response probabilities that require numerical integration

A NONPARAMETRIC APPROACH TO NONLINEAR TIME SERIES ANALYSIS: ESTIMATION AND SIMULATION*

We describe a method of nonlinear time series analysis suitable for nonlinear, stationary, multivariate processes whose one-step-ahead conditional density depends on a finite number of lags. Such a

Bayesian Analysis of Stochastic Volatility Models

New techniques for the analysis of stochastic volatility models in which the logarithm of conditional variance follows an autoregressive model are developed. A cyclic Metropolis algorithm is used to

Estimation of Stochastic Volatility Models with Diagnostics

Efficient Method of Moments (EMM) is used to fit the standard stochastic volatility model and various extensions to several daily financial time series. EMM matches to the score of a model determined

Multivariate stochastic variance models

Changes in variance, or volatility, over time can be modelled using the approach based on autoregressive conditional heteroscedasticity (ARCH). However, the generalizations to multivariate series can

Simulated Moments Estimation of Markov Models of Asset Prices

This paper provides a simulated moments estimator (SME) of the parameters of dynamic models in which the state vector follows a time-homogeneous Markov process. Conditions are provided for both weak

Modelling the persistence of conditional variances

This paper will discuss the current research in building models of conditional variances using the Autoregressive Conditional Heteroskedastic (ARCH) and Generalized ARCH (GARCH) formulations. The

ESTIMATING NONLINEAR TIME-SERIES MODELS USING

SUMMARY This paper develops two new methods for conducting formal statistical inference in nonlinear dynamic economic models. The two methods require very little analytical tractability, relying