Which moments to match gallant

Get my own profile Cited by View all All Since Citations h-index 62 29 iindex Public access. View all. Stephen Ellner Cornell University Verified email at cornell.

William A. Daniel F. Han Hong Stanford University Verified email at stanford. Robert Dittmar University of Michigan Verified email at umich. Professor of Economics, Penn State University.

Statistics econometrics. Articles Cited by Public access Co-authors. Title Sort Sort by citations Sort by year Sort by title. The Review of Financial Studies 5 2 , , Econometrica: Journal of the econometric society, , Econometrica: Journal of the Econometric Society, , The Review of financial studies 15 1 , , Journal of the American Statistical Association 68 , , Articles 1—20 Show more. Help Privacy Terms. Which moments to match? On the bias in flexible functional forms and an essentially unbiased form: the Fourier flexible form AR Gallant Journal of Econometrics 15 2 , , There are two difficulties with the implementation of the characteristic function-based estimators.

First, the optimal instrument yielding the ML efficiency depends on the unknown probability density … Expand. View 5 excerpts, cites methods. Indirect Likelihood Inference Michael Creel. Given a sample from a fully specified parametric model, let Zn be a given finite-dimensional statistic for example, an initial estimator or a set of sample moments.

We propose to re- estimate the … Expand. Indirect Inference: Which Moments to Match? Indirect likelihood inference. Given a sample from a fully specified parametric model, let Zn be a given finite-dimensional statistic - for example, an initial estimator or a set of sample moments. Gallant and Tauchen describe an estimation technique, known as Efficient Method of Moments EMM , that uses numerical methods to estimate parameters of a structural model.

The technique uses … Expand. View 1 excerpt, cites methods. We describe a simulated method of moments estimator that is implemented by choosing the vector valued moment function to be the expectation under the structural model of the score function of an … Expand. 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 … Expand.

View 1 excerpt. 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 … Expand. 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 … Expand.

View 1 excerpt, references methods. 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 … Expand.

View 2 excerpts, references background and methods. Simulation and the Asymptotics of Optimization Estimators. A general central limit theorem is proved for estimators defined by minimization of the length of a vector-valued, random criterion function. No smoothness assumptions are imposed on the criterion … Expand.

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 … Expand. 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 … Expand. View 2 excerpts, references background. 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 … Expand. 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.