Hostname: page-component-76d6cb85b7-hqrjx Total loading time: 0 Render date: 2026-07-13T09:35:38.480Z Has data issue: false hasContentIssue false
  • English
  • Français

THE VALUE OF ROBUST STATISTICAL FORECASTS IN THE COVID-19 PANDEMIC

Published online by Cambridge University Press:  23 June 2021

Jennifer L. Castle Open the ORCID record for Jennifer L. Castle [Opens in a new window] ,
Jurgen A. Doornik  and
David F. Hendry
Jennifer L. Castle*
Affiliation:
Magdalen College, University of Oxford, Oxford, United Kingdom Climate Econometrics, Nuffield College, University of Oxford, Oxford, United Kingdom
Jurgen A. Doornik
Affiliation:
Climate Econometrics, Nuffield College, University of Oxford, Oxford, United Kingdom Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, Oxford, United Kingdom
David F. Hendry
Affiliation:
Climate Econometrics, Nuffield College, University of Oxford, Oxford, United Kingdom Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, Oxford, United Kingdom
*
*Corresponding author. Email: jennifer.castle@magd.ox.ac.uk
Get access Rights & Permissions [Opens in a new window]

Abstract

The Covid-19 pandemic has put forecasting under the spotlight, pitting epidemiological models against extrapolative time-series devices. We have been producing real-time short-term forecasts of confirmed cases and deaths using robust statistical models since 20 March 2020. The forecasts are adaptive to abrupt structural change, a major feature of the pandemic data due to data measurement errors, definitional and testing changes, policy interventions, technological advances and rapidly changing trends. The pandemic has also led to abrupt structural change in macroeconomic outcomes. Using the same methods, we forecast aggregate UK unemployment over the pandemic. The forecasts rapidly adapt to the employment policies implemented when the UK entered the first lockdown. The difference between our statistical and theory based forecasts provides a measure of the effect of furlough policies on stabilising unemployment, establishing useful scenarios had furlough policies not been implemented.

Keywords

time-series forecastingCovid-19epidemiologyunemploymentstructural breaksC22C53E27

Information

Type
Research Article
Information
National Institute Economic Review , Volume 256: THE IMPACT OF COVID-19 ON MACROECONOMIC FORECASTING , Spring 2021 , pp. 19 - 43
Copyright
© National Institute Economic Review, 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Assimakopoulos, V. and Nikolopoulos, K. (2000), ‘The theta model: a decomposition approach to forecasting’, International Journal of Forecasting, 16, 4, pp. 521–30.CrossRefGoogle Scholar
Barnichon, R. and Garda, P. (2016), ‘Forecasting unemployment across countries: the ins and outs’, European Economic Review, 84, pp. 165–83.CrossRefGoogle Scholar
Bates, J.M. and Granger, C.W.J. (1969), ‘The combination of forecasts’, Operations Research Quarterly, 20, pp. 451–68.CrossRefGoogle Scholar
Castle, J.L., Doornik, J.A. and Hendry, D.F. (2018), ‘Selecting a model for forecasting’, Working paper 861, Economics Department, University of Oxford, Oxford.Google Scholar
Castle, J.L., Doornik, J.A. and Hendry, D.F. (2021), ‘Forecasting principles from experience with forecasting competitions’, Forecasting, 3, 1, pp. 138–165.Google Scholar
Castle, J.L., Doornik, J.A., Hendry, D.F. and Pretis, F. (2015), ‘Detecting location shifts during model selection by step-indicator saturation’, Econometrics, 3, 2, pp. 240–64.CrossRefGoogle Scholar
Castle, J.L., Doornik, J.A., Hendry, D.F. and Pretis, F. (2019), ‘Trend-indicator saturation’, Working paper, Nuffield College, University of Oxford, Oxford.Google Scholar
Castle, J.L. and Hendry, D.F. (2011), ‘Automatic selection for non-linear models’, in Wang, L., Garnier, H. and Jackman, T. (eds), System Identification, Environmental Modelling and Control, London: Springer-Verlag, pp. 229–50.Google Scholar
Castle, J.L. and Hendry, D.F. (2014), ‘Semi-automatic non-linear model selection’, in Haldrup, N., Meitz, M. and Saikkonen, P. (eds), Essays in Nonlinear Time Series Econometrics, Oxford: Oxford University Press, pp. 163–97.CrossRefGoogle Scholar
Castle, J.L., Hendry, D.F. and Martinez, A.B. (2017), ‘Evaluating forecasts, narratives and policy using a test of invariance’, Econometrics, 5, 3, p. 39, doi:10.3390/econometrics5030039.CrossRefGoogle Scholar
Chong, Y.Y. and Hendry, D.F. (1986), ‘Econometric evaluation of linear macro-economic models’, Review of Economic Studies, 53, pp. 671–90, reprinted in Granger (1990), Modelling Economic Times Series, Oxford: Clarendon Press, Chapter 17.CrossRefGoogle Scholar
Clements, M.P. and Hendry, D.F. (1998), Forecasting Economic Time Series, Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Doornik, J.A. (2009), ‘Autometrics’, in Castle, J.L. and Shephard, N. (eds), The Methodology and Practice of Econometrics: Festschrift in Honour of David F. Hendry, Oxford: Oxford University Press, pp. 88–121.CrossRefGoogle Scholar
Doornik, J.A. (2018), OxMetrics: An Interface to Empirical Modelling, 8th edn., London: Timberlake Consultants Press.Google Scholar
Doornik, J.A., Castle, J.L. and Hendry, D.F. (2020a), ‘Card forecasts for M4’, International Journal of Forecasting, 36, pp. 129–34.CrossRefGoogle Scholar
Doornik, J.A., Castle, J.L. and Hendry, D.F. (2020b), ‘Short-term forecasting of the coronavirus pandemic’, International Journal of Forecasting, in press, doi:10.1016/j.ijforecast.2020.09.003.Google Scholar
Doornik, J.A., Castle, J.L. and Hendry, D.F. (2020c), ‘Statistical short-term forecasting of the COVID-19 pandemic’, Journal of Clinical Immunology and Immunotherapy, 6, p. 46, doi:10.24966/CIIT-8844/1000046.Google Scholar
Doornik, J.A. and Hansen, H. (2008), ‘An omnibus test for univariate and multivariate normality’, Oxford Bulletin of Economics and Statistics, 70, pp. 927–39.CrossRefGoogle Scholar
Doornik, J.A. and Hendry, D.F. (2018), Empirical Econometric Modelling using PcGive: Volume I, 8th edn., London: Timberlake Consultants Press.Google Scholar
Engle, R.F. (1982), ‘Autoregressive conditional heteroscedasticity, with estimates of the variance of United Kingdom inflation’, Econometrica, 50, pp. 987–1007.CrossRefGoogle Scholar
Ericsson, N.R. (1992), ‘Parameter constancy, mean square forecast errors, and measuring forecast performance: an exposition, extensions, and illustration’, Journal of Policy Modeling, 14, pp. 465–95.CrossRefGoogle Scholar
Gil-Alana, L. (2001), ‘A fractionally integrated exponential model for UK unemployment’, Journal of Forecasting, 20, 5, pp. 329–40.CrossRefGoogle Scholar
Godfrey, L.G. (1978), ‘Testing for higher order serial correlation in regression equations when the regressors include lagged dependent variables’, Econometrica, 46, pp. 1303–13.CrossRefGoogle Scholar
Harvey, A.C. and Kattuman, P. (2020), ‘Time series models based on growth curves with applications to forecasting coronavirus’, Harvard Data Science Review, Special Issue 1, doi:10.1162/99608f92.828f40de.CrossRefGoogle Scholar
Hendry, D.F. (1986), ‘The role of prediction in evaluating econometric models’, Proceedings of the Royal Society A, 407, pp. 25–33.Google Scholar
Hendry, D.F. (1988), ‘Encompassing’, National Institute Economic Review, 125, pp. 88–92.CrossRefGoogle Scholar
Hendry, D.F. (2001), ‘Modelling UK inflation, 1875–1991’, Journal of Applied Econometrics, 16, pp. 255–75.CrossRefGoogle Scholar
Hendry, D.F. (2006), ‘Robustifying forecasts from equilibrium-correction models’, Journal of Econometrics, 135, pp. 399–426.CrossRefGoogle Scholar
Hendry, D.F., Johansen, S. and Santos, C. (2008), ‘Automatic selection of indicators in a fully saturated regression’, Computational Statistics, 33, pp. 317–35, Erratum, pp. 337–39.Google Scholar
Johnes, G. (1999), ‘Forecasting unemployment’, Applied Economics Letters, 6, 9, pp. 605–07.CrossRefGoogle Scholar
Koop, G. and Potter, S.M. (1999), ‘Dynamic asymmetries in U.S. unemployment’, Journal of Business & Economic Statistics, 17, 3, pp. 298–312.Google Scholar
Makridakis, S., Spiliotis, E. and Assimakopoulos, V. (2020), ‘The M4 competition: 100,000 time series and 61 forecasting methods’, International Journal of Forecasting, 36, 1, pp. 54–74.CrossRefGoogle Scholar
Milas, C. and Rothman, P. (2008), ‘Out-of-sample forecasting of unemployment rates with pooled STVECM forecasts’, International Journal of Forecasting, 24, 1, pp. 101–21.CrossRefGoogle Scholar
Mizon, G.E. and Richard, J.-F. (1986), ‘The encompassing principle and its application to testing non-nested hypotheses’, Econometrica, 54, pp. 657–78.CrossRefGoogle Scholar
Montgomery, A.L., Zarnowitz, V., Tsay, R.S. and Tiao, G.C. (1998), ‘Forecasting the U.S. unemployment rate’, Journal of the American Statistical Association, 93, pp. 478–93.CrossRefGoogle Scholar
Okun, A.M. (1962), ‘Potential GNP: its measurement and significance’, in Proceedings of the Business and Economics Statistics Section of the American Statistical Association, Alexandria, VA: American Statistical Association, pp. 98–104.Google Scholar
Peel, D.A. and Speight, A. (2000), ‘Threshold nonlinearities in unemployment rates: further evidence for the UK and G3 economies’, Applied Economics, 32, 6, pp. 705–15.CrossRefGoogle Scholar
Phillips, A.W.H. (1958), ‘The relation between unemployment and the rate of change of money wage rates in the United Kingdom, 1861–1957’, Economica, 25, pp. 283–99.Google Scholar
Proietti, T. (2003), ‘Forecasting the US unemployment rate’, Computational Statistics & Data Analysis, 42, pp. 451–76.CrossRefGoogle Scholar
Ramsey, J.B. (1969), ‘Tests for specification errors in classical linear least squares regression analysis’, Journal of the Royal Statistical Society B, 31, pp. 350–71.Google Scholar
Rothman, P. (1998), ‘Forecasting asymmetric unemployment rates’, Review of Economics and Statistics, 80, 1, pp. 164–68.CrossRefGoogle Scholar
Smith, J.C. (2011), ‘The ins and outs of UK unemployment’, The Economic Journal, 121, pp. 402–44.CrossRefGoogle Scholar
White, H. (1980), ‘A heteroskedastic-consistent covariance matrix estimator and a direct test for heteroskedasticity’, Econometrica, 48, pp. 817–38.Google Scholar