2,555 research outputs found
Identification and non-unique structure
No full textDavid F. Hendry is a seminal figure in modern econometrics. He has pioneered the LSE approach to econometrics, and his influence is wide ranging. This book is a collection of papers dedicated to him and his work. Many internationally renowned econometricians who have collaborated with Hendry or have been influenced by his research have contributed to this volume, which provides a reflection on the recent advances in econometrics and considers the future progress for the methodology of econometrics. Central themes of the book include dynamic modelling and the properties of time series data, model selection and model evaluation, forecasting, policy analysis, exogeneity and causality, and encompassing. The book strikes a balance between econometric theory and empirical work, and demonstrates the influence that Hendry's research has had on the direction of modern econometrics
Reformulating empirical macro-econometric modelling
No full textThe policy implications of estimated macro-econometric systems depend on the formulations of their equations, the methodology of empirical model selection and evaluation, the techniques of policy analysis, and their forecast performance. Drawing on recent results in the theory of forecasting, we question the role of 'rational expectations'; criticize a common approach to testing economic theories; show that impulse-response methods of evaluating policy are seriously flawed; and question the mechanistic derivation of forecasts from econometric systems. In their place, we propose that expectations should be treated as instrumental to agents' decisions; discuss a powerful new approach to the empirical modelling of econometric relationships; offer viable alternatives to studying policy implications; and note modifications to forecasting devices that can enhance their robustness to unanticipated structural breaks
Understanding economic forecasts
No full textNine articles, originally presented at the Annual Festival of Science at the University of Sheffield in September 1999, explain new developments in economic forecasting. Papers examine how economists forecast (David F. Hendry); economic modeling for fun and profit (Paul Turner); making sense of published economic forecasts (Diane Coyle); forecast uncertainty in economic modeling (Neil R. Ericsson); evaluation of forecasts (Clive W. J. Granger); forecasting and the UK business cycle (Denise R. Osborn, Marianne Sensier, and Paul W. Simpson); modeling and forecasting at the Bank of England (Neal Hatch); forecasting the world economy (Ray Barrell); and the costs of forecast errors (Terence Burns). Hendry is Professor of Economics at Nuffield College, Oxford University. Ericsson is a staff economist at the Division of International Finance, Federal Reserve Board. Author and subject indexes
David L. Hendry statement to the 1972 Republican Platform Committee
No full textStatement by David L. Hendry, Chairman of the American Committee for Justice in the Middle East, on neutrality to the Platform Committee at the 1972 Republican National Convention
Understanding economic forecasts
No full textNine articles, originally presented at the Annual Festival of Science at the University of Sheffield in September 1999, explain new developments in economic forecasting. Papers examine how economists forecast (David F. Hendry); economic modeling for fun and profit (Paul Turner); making sense of published economic forecasts (Diane Coyle); forecast uncertainty in economic modeling (Neil R. Ericsson); evaluation of forecasts (Clive W. J. Granger); forecasting and the UK business cycle (Denise R. Osborn, Marianne Sensier, and Paul W. Simpson); modeling and forecasting at the Bank of England (Neal Hatch); forecasting the world economy (Ray Barrell); and the costs of forecast errors (Terence Burns). Hendry is Professor of Economics at Nuffield College, Oxford University. Ericsson is a staff economist at the Division of International Finance, Federal Reserve Board. Author and subject indexes
Econometric modelling of time series with outlying observations
No full textEconomies are buffeted by natural shocks, wars, policy changes, and other unanticipated events. Observed data can be subject to substantial revisions. Consequently, a “correct” theory can manifest serious mis-specification if just fitted to data ignoring its time-series characteristics. Modelling U.S. expenditure on food, the simplest theory implementation fails to describe the evidence. Embedding that theory in a general framework with dynamics, outliers and structural breaks and using impulse-indicator saturation, the selected model performs well, despite commencing with more variables than observations (see Doornik, 2009b), producing useful robust forecasts. Although this illustration involves a simple theory, the implications are generic and apply to sophisticated theorie
Model identification and non-unique structure
No full textIdentification is an essential attribute of any model’s parameters, so we consider its three aspects of ‘uniqueness’, ‘correspondence to reality’ and ‘interpretability’. Observationally-equivalent overidentified models can co-exist, and are mutually encompassing in the population; correctly-identified models need not correspond to the underlying structure; and may be wrongly interpreted. That a given model is over-identified with all over-identifying restrictions valid (even asymptotically) is insufficient to demonstrate that it is a unique representation. Moreover, structure (as invariance under extended information) need not be identifiable. We consider the role of structural breaks to discriminate between such representations
A Dialogue Concerning a New Instrument for Econometric Modeling.
No full textThis paper presents a set of questions prepared by Clive Granger with responses by David Hendry on the use of PcGets (see Hendry and Krolzig, 2001) in data modeling and as a new research tool. PcGets is an Ox package (see Doornik, 2001) implementing automatic general-to-specific (Gets) modeling for linear regression models based on the theory of reduction, as in Hendry (1995, Ch. 9)
Forecasting in the presence of structural breaks and policy regime shifts
No full textPart I. Identification and Efficient Estimation: 1. Incredible structural inference Thomas J. Rothenberg; 2. Structural equation models in human behavior genetics Arthur S. Goldberger; 3. Unobserved heterogeneity and estimation of average partial effects Jeffrey M. Wooldridge; 4. On specifying graphical models for causation and the identification problem David A. Freedman; 5. Testing for weak instruments in linear IV regression James H. Stock and Motohiro Yogo; 6. Asymptotic distributions of instrumental variables statistics with many instruments James H. Stock and Motohiro Yogo; 7. Identifying a source of financial volatility Douglas G. Steigerwald and Richard J. Vagnoni; Part II. Asymptotic Approximations: 8. Asymptotic expansions for some semiparametric program evaluation estimators Hidehiko Ichimura and Oliver Linton; 9. Higher-order improvements of the parametric bootstrap for Markov processes Donald W. K. Andrews; 10. The performance of empirical likelihood and its generalizations Guido W. Imbens and Richard H. Spady; 11. Asymptotic bias for GMM and GEL estimators with estimated nuisance parameters Whitney K. Newey, Joaquim J. S. Ramalho and Richard J. Smith; 12. Empirical evidence concerning the finite sample performance of EL-type structural equation estimation and inference methods Ron C. Mittelhammer, George G. Judge and Ron Schoenberg; 13. How accurate is the asymptotic approximation to the distribution of realised variance? Ole E. Barndorff-Nielsen and Neil Shephard; 14. Testing the semiparametric Box-Cox model with the bootstrap N. E. Savin and Allan H. Wurtz; Part III. Inference Involving Potentially Nonstationary Time Series: 15. Tests of the null hypothesis of cointegration based on efficient tests for a unit MA root Michael Jansson; 16. Robust confidence intervals for autoregressive coefficients near one Samuel B. Thompson; 17. A unified approach to testing for stationarity and unit roots Andrew C. Harvey; 18. A new look at panel testing of stationarity and the PPP hypothesis Jushan Bai and Serena Ng; 19. Testing for unit roots in panel data: an exploration using real and simulated data Brownwyn H. Hall and Jacques Mairesse; 20. Forecasting in the presence of structural breaks and policy regime shifts David F. Hendry and Grayham E. Mizon; Part IV. Nonparametric and Semiparametric Inference: 21. Nonparametric testing of an exclusion restriction Peter J. Bickel, Y. Ritov and James L. Powell; 23. Density weighted linear least squares Whitney K. Newey and Paul A. Ruud.<br/
Pooling of Forecasts
No full textWe consider forecasting using a combination, when no model coincides with a non-constant data generation process (DGP). Practical experience suggests that combining forecasts adds value, and can even dominate the best individual device. We show why this can occur when forecasting models are differentially mis-specified, and is likely to occur when the DGP is subject to deterministic shifts. Moreover, averaging may then dominate over estimated weights in the combination. Finally, it cannot be proved that only non-encompassed devices should be retained in the combination. Empirical and Monte Carlo illustrations confirm the analysis.
- …
