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Dive into the research topics where David F. Hendry is active.

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Featured researches published by David F. Hendry.


Journal of Econometrics | 1993

Testing superexogeneity and invariance in regression models

Robert F. Engle; David F. Hendry

Abstract This paper introduces tests of superexogeneity and invariance. Under the null hypothesis the conditional model exhibits parameter constancy, while under the alternative shifts in the process of the independent variables induces shifts in the conditional model. The test is sensitive to particular types of parameter nonconstancy, especially with changing variances and covariances. We relate the test to rational expectations models and the Lucas critique. An empirical example of money demand finds prices and interest rates superexogenous in a conditional model, but when the inflation specification changes, superexogeneity fails although standard specification tests do not.


Handbook of Econometrics | 1984

Monte carlo experimentation in econometrics

David F. Hendry

Publisher Summary At the outset, it is useful to distinguish Monte Carlo methods from distribution sampling even though their application in econometrics may seem rather similar. The former is a general approach whereby mathematical problems of an analytical nature, which prove technically intractable, can be solved by substituting an equivalent stochastic problem and solving the latter. In contrast, distribution sampling is used to evaluate features of a statistical distribution by representing it numerically and drawing observations from that numerical distribution. The chapter investigates the distribution of the mean of random samples of T observations from a distribution that was uniform between zero and unity, one could simply draw a large number of samples of that size from a set of one million evenly spaced numbers in the interval and plot the resulting distribution. Such a procedure is invariably part of a Monte Carlo experiment.


Journal of Economic Surveys | 1998

Inference in Cointegrating Models: UK M1 Revisited

Jurgen A. Doornik; David F. Hendry; Bent Nielsen

The paper addresses the practical determination of cointegration rank. This is difficult for many reasons: deterministic terms play a crucial role in limiting distributions, and systems may not be formulated to ensure similarity to nuisance parameters; finite-sample critical values may differ from asymptotic equivalents; dummy variables alter critical values, often greatly; multiple cointegration vectors must be identified to allow inference; the data may be 1(2) rather than 1(1), altering distributions; and conditioning must be done with care. These issues are illustrated by an empirical application of multivariate cointegration analysis to a small model of narrow money, prices, output and interest rates in the UK. Copyright 1998 by Blackwell Publishers Ltd


Journal of Business & Economic Statistics | 1998

Exogeneity, cointegration, and economic policy analysis

Neil R. Ericsson; David F. Hendry; Grayham E. Mizon

This overview examines conditions for reliable economic policy analysis based on econometric models, focusing on the econometric concepts of exogeneity, cointegration, causality, and invariance. Weak, strong, and super exogeneity are discussed in general, and these concepts are then applied to the use of econometric models in policy analysis when the variables are cointegrated. Implications follow for model constancy, the Lucas critique, equation inversion, and impulse response analysis. A small money-demand model for the United Kingdom illustrates the main analytical points. This article then summarizes the other articles in this issues special section on exogeneity, cointegration, and economic policy analysis.


Economic Modelling | 2003

Economic Forecasting: Some Lessons from Recent Research

David F. Hendry; Michael P. Clements

This paper describes some recent advances and contributions to our understanding of economic forecasting. The framework we develop helps explain the findings of forecasting competitions and the prevalence of forecast failure. It constitutes a general theoretical background against which recent results can be judged. We compare this framework to a previous formulation, which was silent on the very issues of most concern to the forecaster. We describe a number of aspects which it illuminates, and draw out the implications for model selection. Finally, we discuss the areas where research remains needed to clarify empirical findings which lack theoretical explanations.


Journal of Econometrics | 1976

The structure of simultaneous equations estimators

David F. Hendry

Abstract The formula for the Full Information Maximum Likelihood Estimator for a linear simultaneous system (with finite variance, serially independent errors) is demonstrated to be an estimator generating equation for econometrics in that all presently known estimators are readily derivable from that formula if they are considered as numerical approximations to its solution. Further, the approach immediately classifies the resulting estimators into asymptotically equivalent groups. The method is then generalised to encompass the large class of estimators for dynamic systems with (vector) autoregressive errors. The very close relationship between estimation rules and non-linear optimisation algorithms is highlighted.


Journal of Business & Economic Statistics | 2011

Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate

David F. Hendry; Kirstin Hubrich

To forecast an aggregate, we propose adding disaggregate variables, instead of combining forecasts of those disaggregates or forecasting by a univariate aggregate model. New analytical results show the effects of changing coefficients, mis-specification, estimation uncertainty and mis-measurement error. Forecastorigin shifts in parameters affect absolute, but not relative, forecast accuracies; mis-specification and estimation uncertainty induce forecast-error differences, which variable-selection procedures or dimension reductions can mitigate. In Monte Carlo simulations, different stochastic structures and interdependencies between disaggregates imply that including disaggregate information in the aggregate model improves forecast accuracy. Our theoretical predictions and simulations are corroborated when forecasting aggregate US inflation pre- and post 1984 using disaggregate sectoral data.


International Journal of Forecasting | 2005

Non-Parametric Direct Multi-Step Estimation for Forecasting Economic Processes

Guillaume Chevillon; David F. Hendry

We evaluate the asymptotic and finite-sample properties of direct multi-step estimation (DMS) for forecasting at several horizons. For forecast accuracy gains from DMS in finite samples, mis-specification and non-stationarity of the DGP are necessary, but when a model is well-specified, iterating the one-step ahead forecasts may not be asymptotically preferable. If a model is mis-specified for a non-stationary DGP, omitting either negative residual serial correlation or regime shifts, DMS can forecast more accurately. Monte Carlo simulations clarify the non-linear dependence of the estimation and forecast biases on the parameters of the DGP, and explain existing results.


Archive | 2005

General-to-specific modeling: an overview and selected bibliography

Julia Campos; Neil R. Ericsson; David F. Hendry

This paper discusses the econometric methodology of general-to-specific modeling, in which the modeler simplifies an initially general model that adequately characterizes the empirical evidence within his or her theoretical framework. Central aspects of this approach include the theory of reduction, dynamic specification, model selection procedures, model selection criteria, model comparison, encompassing, computer automation, and empirical implementation. This paper thus reviews the theory of reduction, summarizes the approach of general-to-specific modeling, and discusses the econometrics of model selection, noting that general-to-specific modeling is the practical embodiment of reduction. This paper then summarizes fifty-seven articles key to the development of general-to-specific modeling.


Structural Change and Economic Dynamics | 2000

On detectable and non-detectable structural change

David F. Hendry

Abstract A range of parameter changes in I (1) cointegrated time series are not reflected in econometric models thereof, in that many shifts are not easily detected by conventional tests. The breaks in question are changes that leave the unconditional expectations of the I (0) components unaltered. Thus, dynamics, adjustment speeds etc. may alter without detection. However, shifts in long-run means are generally noticeable. Using the VECM model class, the paper discusses such results, explains why they occur, and uses Monte Carlo experiments to illustrate the contrasting ease of detection of ‘deterministic’ and ‘stochastic’ shifts.

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Mary S. Morgan

London School of Economics and Political Science

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