Forecasting Breaks and Forecasting During Breaks

Castle, Jennifer L.; Fawcett, Nicholas; Hendry, David F.

doi:10.1093/oxfordhb/9780195398649.013.0012

Cited by 12 publications

(11 citation statements)

References 75 publications

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“…Importantly, there is no need to robustify till after a forecast failure, because robust devices suffer the same initial forecast failure as non-robust. Improving on that performance would require such devices to be augmented by some method for forecasting shifts, although Castle, Fawcett and Hendry (2011) show the difficulties in doing so.…”

Section: Further Discussion On Robustifying Veqcmsmentioning

confidence: 99%

Robust approaches to forecasting

Castle

Clements

Hendry

2015

International Journal of Forecasting

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We investigate alternative robust approaches to forecasting, using a new class of robust devices, contrasted with equilibrium-correction models. Their forecasting properties are derived facing a range of likely empirical problems at the forecast origin, including measurement errors, impulses, omitted variables, unanticipated location shifts and incorrectly included variables that experience a shift. We derive the resulting forecast biases and error variances, and indicate when the methods are likely to perform well. The robust methods are applied to forecasting US GDP using autoregressive models, and also to autoregressive models with factors extracted from a large dataset of macroeconomic variables. We consider forecasting performance over the Great Recession, and over an earlier more quiescent period.

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Section: Further Discussion On Robustifying Veqcmsmentioning

confidence: 99%

Robust approaches to forecasting

Castle

Clements

Hendry

2015

International Journal of Forecasting

Self Cite

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“…Typically, these significance levels are somewhat arbitrarily set by convention based on desired Type I error. However, when it comes to the practice of IC adoption, the chosen α has real consequences on the accuracy of forecasts (see Castle, Fawcett, and Hendry, 2011). In the prior section, I establish that detection of mean shifts is more difficult in aggregate models.…”

Section: The Power Of Test Statisticsmentioning

confidence: 95%

Forecasting an Aggregate in the Presence of Structural Breaks in the Disaggregates

Larson¹

2014

SSRN Journal

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“…DFH: I think so, but much more research is needed. If such information could be observed in advance, then it should be feasible to develop models for forecasting breaks, an issue addressed in Castle, Fawcett, and Hendry (2011). Policy-determined events-such as allowing Lehman Brothers to go bankrupt-seem likely to remain unpredictable.…”

Section: Nrementioning

confidence: 99%

Economic Forecasting in Theory and Practice: An Interview with David F. Hendry

Ericsson

2016

Int. finance discuss. pap.

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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.Keywords: encompassing, equilibrium correction models, error correction, evaluation, exogeneity, forecasting, modeling, nowcasting, parameter constancy, robustification, structural breaks. We are grateful to Julia Campos, Jennifer Castle, Mike Clements, Vivien Hendry, Rob Hyndman, Andrew Kane, Aaron Markiewitz, Jaime Marquez, Andrew Martinez, Bent Nielsen, Felix Pretis, Angela Wenham, and an anonymous referee for helpful comments and discussion, and to Aaron Markiewitz for research assistance. Empirical results and graphics were obtained using 64-bit OxMetrics 7.1; see Doornik and Hendry (2013).1 Early Work on Forecasting NRE: David, you've made major contributions to many areas of economics and econometrics. 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. When did you first become interested in forecasting?1.1 The University of Aberdeen DFH: It was in 1964. I was an undergraduate at the University of Aberdeen, and I was very much influenced by the empirical economic models of Lawrie Klein (1950) and Jan Tinbergen (1951), who suggested that we might be able to forecast future outcomes. In my undergraduate thesis, I estimated a regression model for annual UK consumers' expenditure given current income and lagged expenditure-painstakingly worked out on a mechanical calculator. Using the whole-sample parameter estimates, I calculated a "forecast" of the last observation to see how close it was to the outcome. NRE:In effect, you were evaluating the last residual of your estimation period. What did you find? DFH:The forecast and the outcome were reasonably close. That's unsurprising, given how the "forecast" was calculated. Because the forecast was within the estimation period, the corresponding forecast error was included in the sum of squared errors that OLS minimized. Macroeconometric Models and Predictive FailureNRE: When you were writing your PhD thesis under Denis Sa...

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Forecasting Breaks and Forecasting During Breaks

Cited by 12 publications

References 75 publications

Robust approaches to forecasting

Robust approaches to forecasting

Forecasting an Aggregate in the Presence of Structural Breaks in the Disaggregates

Economic Forecasting in Theory and Practice: An Interview with David F. Hendry

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