Recent papers

Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models (with R. Liesenfeld) is now available.

Professor Richard's talk, "An Integrated Treatment of Monte Carlo Numerical Integration Techniques," presented at the SCE 11th International Conference, June 2005, is now available.

The GDP Forecast Model at the University of Pittsburgh is devoted to reporting forecasts of GDP growth, and characterizations of the current state of the macroeconomy.

 

 

   

Use of Prior Information in the Analysis and Estimation of Cobb-Douglas Production Function Models, (with A. Zellner), International Economic Review, 14(1), 1973, 107-119.
Abstract: In the present paper, we review a sampling-theory analysis of the Cobb-Douglas production function put forward by Morishima and Saito, in which nondata-based prior information was added to aggregate time-series information in order to deal with what appeals to be a muticollinearity problem. Then we turn to an analysis of the Morishima-Saito (MS) production function problem, using the Bayesian approach in which nondata-based prior information is introduced by use of prior probability density functions (PDFs) for the parameters. By the use of prior PDFs in the Bayesian approach, it will be seen that prior information can be introduced in a rather flexible manner, and posterior PDFs for parameters of interest, which reflect both sample and prior information, can be readily computed.

Bayesian Inference in Error-in-Variables Models, (with J.P. Florens and M. Mouchart), Journal of Multivariate Analysis, 1974, 419-452.

A Note on the Information Matrix of the Multivariate Normal Distribution, Journal of Econometrics, 3, 1975, 57-60

Bayesian Analysis of the Regression Model when the Disturbances are Generated by an Autoregressive Process, in New Developments in the Applications of Bayesian Methods, (Chapter 11), edited by Aykac, A. and C. Brumat, North Holland, 1977, 185-210.

Models with Several Regimes and Changes in Exogeneity, Review of Economic Studies, XLVII, 1980, 1-20.
Abstract: In recent years, increasing attention has been devoted to models with a finite (usually small) number of regimes. Various strategies have been discussed in the literature to handle situations where each regime is characterized by a different value of a common parameter vector. It appears however that no satisfactory treatment has yet been given to cases where the partitioning between "endogenous" and "exogenous" variables changes over time. Our objective is therefore to define a class of models with several regimes which is flexible enough to cover such situations.

On the Evaluation of Poly-t Density Functions, (with H. Tompa), Journal of Econometrics, 12, 1980, 335-351.
Abstract: Poly-t densities are defined by the property that their kernel is a product, or a ratio of products, of multivariate t-density kernels. As discussed in Drèze (1977), these densities arise as Bayesian posterior densities for regression coefficients under a variety of specifications for the prior density and the data generating process. We have therefore developed methods and computer algorithms. To evaluate integrating constants and other characteristics of poly-t densities with no more than a single quadratic form in the numerator (section 2). As a by-product of our analysis we have also derived an algorithm for the computation of moments of positive definite quadratic forms in Normal variables (section 3). In section 4 we discuss inference on the sampling variances associated with the models discussed in Drèze (1977).

Bayesian Analysis of Simultaneous Equation Systems, (with J. Drèze), The Handbook of Econometrics, Volume 1, (Chapter 9), edited by Z. Griliches and M.D. Intriligator, North Holland, 1983, 519-598.

The Econometric Analysis of Economic Time Series, (with D.F. Hendry), The International Statistical Review, 51, 1983, 111-163. Reprinted in Econometrics Alchemy or Science?, (Chapter 17), edited by D. F. Hendry , 1993, Blackwell, Oxford. Reprinted in Foundations of Probability, Econometrics and Economic Games, edited by O.F. Hamouda and J.C.R. Rowley, Edward Elgar, 1996. (~21 Mb)
Abstract: A framework is proposed for interpreting recent developments in time series econometrics, emphasizing the problems of linking economics and statistics. There are six main expository themes: models are viewed as (reduced) reparameterizations of data processes through marginalizing and conditioning; the latter operation is related to the economic notion of contingent plans based on weakly exogenous variables; a typology of dynamic equations clarifies the properties of conditional models; estimation of unknown parameters is treated using estimator generating equations; and tests are interrelated in terms of the efficient score statistic; finally, the concept of encompassing rival hypotheses (separate or nested) provides an 'overview' criterion for evaluating empirical estimates which have been selected to satisfy conventional criteria. The discussion is illustrated by an estimated model of the demand for money.

Exogeneity, (with R.F. Engle and D.F. Hendry), Econometrica, 51, 1983, 277-304. Reprinted in Testing Exogeneity, (Chapter 2), edited by N. R. Ericsson and J. S. Irons, 1994, Oxford University Press, Oxford. Reprinted in Econometrics Alchemy or Science? (Chapter 15), edited by D. F. Hendry, Blackwell, 1993. Reprinted in The Methodology of Econometrics, edited by D.J. Poirer, Edward Elgar, 1994. Reprinted in Time Series, edited by A. Harvey, Edward Elgar, 1994. Reprinted in Foundations of Probability, Econometrics and Economic Games, edited by O.F. Hamouda and J.C.R. Rowley, Edward Elgar, 1996. Reprinted in General ­ to ­ Specific Modelling, edited by J. Campos, N. Ericsson and D. F. Hendry, Edward Elgar, 2005.
Abstract: Definitions are proposed for weak and strong exogeneity in terms of the distribution of observable variables. The objectives of the paper are to clarify the concepts involved, isolate the essential requirements for a variable to be exogenous, and relate them to notions of predeterminedness, strict exogeneity and causality in order to facilitate econometric modelling. Worlds of parameter change are considered and exogeneity is related to structural invariance leading to a definition of super exogeneity. Throughout the paper, illustrative models are used to exposit the analysis.

Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models, (with Roman Liesenfeld), Economics working paper, Christian-Albrechts-Universität Kiel, Department of Economics; 2004,12.
Abstract: In this paper, Efficient Importance Sampling (EIS) is used to perform a classical and Bayesian analysis of univariate and multivariate Stochastic Volatility (SV) models for financial return series. EIS provides a highly generic and very accurate procedure for the Monte Carlo (MC) evaluation of high-dimensional interdependent integrals. It can be used to carry out ML-estimation of SV models as well as simulation smoothing where the latent volatilities are sampled at once. Based on this EIS simulation smoother a Bayesian Markov Chain Monte Carlo (MCDC) posterior analysis of the parameters of SV models can be performed.

Efficient High-Dimensional Importance Sampling (with Wei Zhang), Unpublished manuscript, University of Pittsburgh, Department of Economics, 2005.
Abstract: The paper describes a simple, generic, and yet highly accurate Efficient Importance Sampling (EIS) Monte Carlo (MC) procedure for the evaluation of high-dimensional numerical integrals. EIS is based upon a sequence of auxiliary weighted regressions which actually are linear under appropriate conditions. It can be used to evaluate likelihood functions and byproducts thereof, such as ML estimators, for models which depend upon unobservable variances. A dynamic stochastic volatility model and a logit panel data model with unobserved heterogeneity (random effects) in both dimensions are used to provide illustrations of EIS high numerical accuracy, even under a small number of MC draws. MC simulations are used to characterize the finite sample numerical and statistical properties of EIS-based ML estimators.

 

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