Econometric Modelling of the Aggregate Time-Series Relationship Between Consumers' Expenditure and Income in the United Kingdom

Davidson, James; Hendry, David F.; Srba, Frank; Yeo, Stephen

doi:10.2307/2231972

Cited by 1,330 publications

(594 citation statements)

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“…To test the winner's curse hypothesis (WCRP) in a multivariate context, we use a general-tospecific approach (Davidson et al, 1978). That is, all coded variables are included in the WLS version of MRA model (1), and insignificant variables are removed one at a time.…”

Section: Meta-analysis Datamentioning

confidence: 99%

Publication selection in health policy research: The winner's curse hypothesis

Costa‐Font

McGuire²,

Stanley³

2013

Health Policy

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There is a widely discussed problem of publication bias in medical and health services research. Where quantitative effects form the basis of a publication a 'winner's curse' curse may apply. This phenomenon may occur as prospective authors of research papers compete by reporting 'more extreme and spectacular results' in order to increase the chances of their paper being accepted for publication. This paper examines this phenomenon using quantitative findings on income and price elasticities as reported in health economics research. We find robust statistical evidence that higher-impact journals preferentially report larger empirical estimates of these elasticities. That is, we find robust evidence of a winner's curse hypothesis contributing to the existence of publication bias found in both the income and the price elasticities of health care and drugs, as well as value of life research.2

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Section: Meta-analysis Datamentioning

confidence: 99%

Publication selection in health policy research: The winner's curse hypothesis

Costa‐Font

McGuire²,

Stanley³

2013

Health Policy

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“…The FECM in this context requires modelling the variables x ft = (f 1t , x 2t , x 3t , x 4t ) 0 , where the stochastic trend model in (12) implies that f 1t = x 1t−1 . Therefore, the relevant equations of the FECM are…”

Section: An Analytical Examplementioning

confidence: 99%

“…Starting at least with Davidson, Hendry, Srba and Yeo (1978), Hendry has argued in favour of the powerful role of error-correction mechanisms (ECM) in modelling macroeconomic data. While originally undertaken in an environment with supposedly stationary data, the subsequent development of cointegration served to renew emphasis on the long-run co-movement of macroeconomic variables.…”

Section: Introductionmentioning

confidence: 99%

Factor‐augmented Error Correction Models*

Banerjee

Marcellino

2009

The Methodology and Practice of Econometrics

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This paper brings together several important strands of the econometrics literature: errorcorrection, cointegration and dynamic factor models. It introduces the Factor-augmented Error Correction Model (FECM), where the factors estimated from a large set of variables in levels are jointly modelled with a few key economic variables of interest. With respect to the standard ECM, the FECM protects, at least in part, from omitted variable bias and the dependence of cointegration analysis on the specific limited set of variables under analysis. It may also be in some cases a refinement of the standard Dynamic Factor Model since it allows us to include the error correction terms into the equations, and by allowing for cointegration prevent the errors from being non-invertible moving average processes. In addition, the FECM is a natural generalization of factor augmented VARs (FAVAR) considered by Bernanke, Boivin and Eliasz (2005) inter alia, which are specified in first differences and are therefore misspecified in the presence of cointegration.The FECM has a vast range of applicability. A set of Monte Carlo experiments and two detailed empirical examples highlight its merits in finite samples relative to standard ECM and FAVAR models. The analysis is conducted primarily within an in-sample framework, although the out-ofsample implications are also explored.

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“…He noted, almost in passing, that his specification represented a correction to a dPhillipsT type equation. Hendry referred to the approach in a conference organized by Sims (Hendry, 1977), and it was used in the widely cited paper by Davidson, Hendry, Srba, and Yeo (1978) before getting the label that has stuck for many years. Only recently has Hendry (Clements & Hendry, 1996) begun referring to the phenomenon as bequilibrium correctionQ.…”

Section: Error Correction and Cointegrationmentioning

confidence: 99%

Twenty-five years of progress, problems, and conflicting evidence in econometric forecasting. What about the next 25 years?

Allen

Morzuch

2006

International Journal of Forecasting

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In the early 1940s, the Cowles Commission for Research (later, the Cowles Foundation) fostered the development of statistical methodology for application in economics and paved the way for large-scale econometric models to be used for both structural estimation and forecasting. This approach stood for decades. Vector autoregression (VAR), appearing in the 1980s, was a clear improvement over early Cowles Foundation models, primarily because it paid attention to dynamic structure. As a way of imposing long-run equilibrium restrictions on sets of variables, cointegration and error-correction modeling (ECM) gained popularity in the 1980s and 1990s, though ECMs have so far failed to deliver on their early promise. ARCH and GARCH modeling have been used with great success in specialized financial areas to model dynamic heteroscedasticity, though in mainstream econometrics, evidence of their value is limited and conflicting. Concerning misspecification tests, any model will inevitably fail some of them for the simple reason that there are many possible tests. Which failures matter? The root of the difficulty regarding all issues related to modeling is that we can never know the true data generating process. In the next 25 years, what new avenues will open up? With ever greater computational capacity, more complex models with larger data sets seem the way to the future. Will they require the automatic model selection methods that have recently been introduced? Preliminary evidence suggests that these methods can do well. The quality of aggregate data is no better than it was. Will greater use of more disaggregated data be sufficient to provide better forecasts? That remains an open question.

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Econometric Modelling of the Aggregate Time-Series Relationship Between Consumers' Expenditure and Income in the United Kingdom

Cited by 1,330 publications

References 1 publication

Publication selection in health policy research: The winner's curse hypothesis

Publication selection in health policy research: The winner's curse hypothesis

Factor‐augmented Error Correction Models*

Twenty-five years of progress, problems, and conflicting evidence in econometric forecasting. What about the next 25 years?

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