The ET Interview: Professor David F. Hendry

Ericsson, Neil R.

doi:10.17016/ifdp.2004.811

Cited by 4 publications

(5 citation statements)

References 115 publications

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“…That theory is now described in Hendry (2006). As we discussed in Ericsson (2004), I have had several other such insights when teaching.…”

Section: Nrementioning

confidence: 89%

“…These include econometric methodology, general-to-specific modeling, Monte Carlo techniques, software implementation, the history of econometric thought, policy analysis, and empirical investigations of consumer expenditure, money demand, inflation, and the housing market. We discussed these topics at length in Ericsson (2004), so let's focus on another important topic-forecasting. Over the last couple of decades, you've made significant contributions to our understanding of economic forecasting.…”

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confidence: 99%

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Economic forecasting in theory and practice: An interview with David F. Hendry

Ericsson

2017

International Journal of Forecasting

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David Hendry has made major contributions to many areas of economic forecasting. He has developed a taxonomy of forecast errors and a theory of unpredictability that have yielded valuable insights into the nature of forecasting. He has also provided new perspectives on many existing forecast techniques, including mean square forecast errors, add factors, leading indicators, pooling of forecasts, and multi-step estimation. In addition, David has developed new forecast tools, such as forecast encompassing; and he has improved existing ones, such as nowcasting and robustification to breaks. This interview for the International Journal of Forecasting explores David Hendry's research on forecasting. an anonymous referee for helpful comments and discussion, and to Aaron Markiewitz for research assistance. Empirical results and graphics were obtained using 64-bit Ox-Metrics 7.1; see Doornik and Hendry (2013). policy's implementation, or consumers' responses to both. Consequently, my model's forecasts failed badly.NRE: Your UK model was subsequently published as Hendry (1974), which included a new test for predictive failure. It generalized Gregory Chow's (1960) single-equation predictive failure test to systems, albeit in a  2 version rather than the  version that Jan Kiviet (1986) later developed. How did that experience with your small macromodel influence your work on forecasting?DFH: It motivated me to investigate the nature of predictive failure. Why did models built from the best available economics using the latest econometrics and fairly good data not produce useful forecasts? In Hendry (1979b), I linked predictive failure to poor model formulation, but that explanation subsequently turned out to be unhelpful, or at least incomplete. NRE:Other economists were also evaluating forecasts from macro-models. In particular, Charles Nelson wrote two influential papers on ex ante forecasts: Nelson (1972) and Cooper and Nelson (1975).DFH: Charles showed that forecasts from univariate time-series models could beat forecasts from large empirical economic models such as the FRB-MIT-PENN model. From an LSE perspective, such large models treated dynamics inadequately, often simply as autocorrelated errors in static equations. Because of that dynamic misspecification, we suspected that models that included only dynamics could forecast better. I found that simple dynamic models did indeed forecast better than static economic models, even though the latter embedded economic theory whereas the former did not. However, I had misinterpreted the implications of Nelson and Cooper's results. I had not realized that models in differences-such as those in Nelson (1972)almost invariably forecast better than models in levels if the means of the variables being forecast altered. We now refer to such changes as location shifts.

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“…That theory is now described in Hendry (2006). As we discussed in Ericsson (2004), I have had several other such insights when teaching.…”

Section: Nrementioning

confidence: 89%

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confidence: 99%

Economic forecasting in theory and practice: An interview with David F. Hendry

Ericsson

2017

International Journal of Forecasting

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“…Nesse sentido, X t é superexógeno na equação de Y t (e, como tal, constitui uma causa de Y t ) se X t for fracamente exógeno e se os parâmetros β 0 , β 1 , φ e Var (ε 1t ) = σ 2 forem invariantes relativamente a quebras de estrutura (decorrentes de intervenções ou choques exógenos na equação de X t ). A abordagem da London School of Economics, conhecida por "metodologia do geral para o particular", ou GETS -do inglês "general-to-specifi c" -, desenvolvida por Hendry e pelos seus coautores (Campos, Ericsson e Hendry, 2005;Ericsson, 2004) também pode ser incluída aqui.…”

Section: Figura 1 Abordagens à Causalidade Em Macroeconomiaunclassified

Causalidade em economia com séries temporais: uma visita guiada desde a Antiguidade Clássica

Fonseca

Sánchez-Rivero

2020

Nova econ.

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Resumo Este artigo enquadra o estudo da causalidade em Economia, com dados em série temporal, no contexto mais abrangente da análise filosófica da causalidade. Para o efeito, demonstramos que o espectro de abordagens existentes em Economia resulta das respostas que já foram dadas, até agora, aos problemas ontológico, epistemológico e pragmático da causalidade no âmbito específico dessa disciplina. Concluímos ainda que longe de ser a única resposta disponível, correta ou adequada, a onipresente causalidade à Granger constitui apenas uma das várias respostas possíveis. Numa perspectiva mais geral, nosso objetivo é proporcionar uma visão concisa, mas ao mesmo tempo articulada e historicamente fundamentada acerca das origens, evolução e problemática da análise da causalidade em Economia.

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“…We are delighted to contribute to this Festschrift in honor of David F. Hendry. As discussed in Ericsson (2004), David has contributed to numerous areas of econometrics and economics, including:…”

Section: Introductionmentioning

confidence: 99%

Constructive Data Mining: Modeling Argentine Broad Money Demand

Ericsson

Kamin

2008

internationalfinance

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This paper assesses the empirical merits of PcGets and Autometrics-two recent algorithms for computer-automated model selection-using them to improve upon Kamin and Ericsson's (1993) model of Argentine broad money demand. The selected model is an economically sensible and statistically satisfactory error correction model, in which cointegration between money, inflation, the interest rate, and exchange rate depreciation depends on the inclusion of a "ratchet" variable that captures irreversible effects of inflation. Short-run dynamics differ markedly from the long run. Algorithmically based model selection complements opportunities for the researcher to contribute value added in the empirical analysis.

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The ET Interview: Professor David F. Hendry

Cited by 4 publications

References 115 publications

Economic forecasting in theory and practice: An interview with David F. Hendry

Economic forecasting in theory and practice: An interview with David F. Hendry

Causalidade em economia com séries temporais: uma visita guiada desde a Antiguidade Clássica

Constructive Data Mining: Modeling Argentine Broad Money Demand

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