5,100 research outputs found
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
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
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
Foreman E. Hendry Uses Machinery at Florida Steel Plant, E
No full textForeman E. Hendry uses machinery at a Florida Steel Corporation plant circa 1973. Photographed for Lieberman-Harrison Advertising.https://digitalcommons.usf.edu/gandy_commercial/8867/thumbnail.jp
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/
Marriage record of Perrin, Wade and Hendry, Carrie E.
No full textMarriage license for Wade Perrin and Carrie E. Hendry. D.A. Perrin was the officiant
On selecting policy analysis models by forecast accuracy
No full textThe value of selecting the best forecasting model as the basis for empirical economic policy analysis is questioned. When no model coincides with the data generation process, non-causal statistical devices may provide the best available forecasts: examples from recent work include intercept corrections and differenced-data VARs. However, the resulting models need have no policy implications. A ‘paradox’ may result if their forecasts induce policy changes which can be used to improve the statistical forecast. This suggests correcting statistical forecasts by using the econometric model’s estimate of the ‘scenario’ change. An application to UK consumers expenditure illustrates the analysis
Foreman E. Hendry Uses Machinery at Florida Steel Plant, A
No full textForeman E. Hendry uses machinery at a Florida Steel Corporation plant circa 1973. Photographed for Lieberman-Harrison Advertising.https://digitalcommons.usf.edu/gandy_commercial/8865/thumbnail.jp
Customer enquiry management and product customization: an empirical multi-case study analysis in the Italian capital goods sector
No full textPurpose – The customer enquiry management (CEM) process is of strategic importance in engineer-to-order contexts but existing literature does not adequately describe how firms support delivery date setting and order acceptance decisions in practice. This paper seeks to explore how and why the CEM process varies between companies in the capital goods sector, thereby taking a contingency theory approach. Design/methodology/approach – Multi-case study research involving 18 Italian capital goods manufacturers in four industrial sectors. Face-to-face interviews with senior representatives have been conducted. Companies have been grouped into five clusters, based on similarities in their CEM decision-making modes, to aid analysis. Findings – Three contingency factors were found to be particularly relevant in determining CEM modes: degree of product customization, flexibility of the production system, and uncertainty of the context. These factors affect the choice of specific CEM decision-making modes. However, a high level of cross-functional coordination and formalization of the process were found to constitute best practices whatever the contingency factors. Research limitations/implications – The research focuses on companies belonging to the Italian capital goods sector – findings may differ in other countries and sectors. Practical implications – The results indicate that all firms, including small and medium-sized companies, should implement high levels of cross-functional coordination and formalization in their CEM practices, in order to improve their performance. For other aspects of the CEM process, including supplier and subcontractor monitoring, the company context will indicate whether these aspects are required, according to a need of matching the approach to CEM with specific sets of contingency factors. Originality/value – This paper provides a rare insight into the CEM processes found in practice
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