Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-23T12:59:38.240Z Has data issue: false hasContentIssue false
  • English
  • Français

Where are we now? The UK Recession and Nowcasting GDP Growth Using Statistical Models

Published online by Cambridge University Press:  26 March 2020

James Mitchell
James Mitchell*
Affiliation:
National Institute of Economic and Social Research
*
e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Extract

GDP data are published after a lag. The Office for National Statistics (ONS) in the UK, which is quicker than statistical offices in other European countries, publishes quarterly GDP estimates about 27 days after the end of the quarter. Inevitably, this means that economists and policymakers neither know where we are now, nor yet where we might be in the future.

Type
Articles
Information
National Institute Economic Review , Volume 209 , July 2009 , pp. 60 - 69
Copyright
Copyright © 2009 National Institute of Economic and Social Research

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.)

Footnotes

∗

Thanks to Ray Barrell and Martin Weale for helpful comments.

References

Baffigi, A., Golinelli, R. and Parigi, G. (2004), ‘Bridge models to forecast the euro area GDP’, International Journal of Forecasting, 20, pp. 447–60.CrossRefGoogle Scholar
Barrell, R., Khoman, E. and Kirby, S. (2008), ‘Evaluating forecast uncertainty’, National Institute Economic Review, 201, pp. 55–60.CrossRefGoogle Scholar
Barrell, R. and Kirby, S. (2008), ‘Forecast uncertainty and prospects for the UK economy’, National Institute Economic Review, 204, pp. 61–3.CrossRefGoogle Scholar
Carriero, A. and Marcellino, M. (2007), ‘A comparison of methods for the construction of composite coincident and leading indexes for the UK’, International Journal of Forecasting, 23, pp. 219–36.CrossRefGoogle Scholar
Clements, M.P. and Hendry, D.F. (1998), Forecasting Economic Time Series, Cambridge, Cambridge University Press.CrossRefGoogle Scholar
Corradi, V., Fernandez, A. and Swanson, N.R. (2009), ‘Real-time datasets really do make a difference: definitional change, data release and forecasting’, available at http://www.bundesbank.de/download/vfz/konferenzen/20090323_24_berlin/paper_corradi_fernandez.pdfCrossRefGoogle Scholar
Diron, M. (2008), ‘Short-term forecasts of euro area real GDP growth: an assessment of real-time performance based on vintage data’, Journal of Forecasting, 27, pp. 371–90.CrossRefGoogle Scholar
Eklund, J. and Kapetanios, G. (2008), ‘A review of forecasting techniques for large datasets’, National Institute Economic Review, 203, pp. 109–15.Google Scholar
Garratt, A. and Vahey, S.P. (2006), ‘UK real-time macro data characteristics’, Economic Journal, 116, pp. 119–35.CrossRefGoogle Scholar
Giannone, D., Reichlin, L. and Small, D. (2008), ‘Nowcasting: the real-time informational content of macroeconomic data’, Journal of Monetary Economics, 55, pp. 665–76.CrossRefGoogle Scholar
Hansson, J., Jansson, P. and Löf, M. (2005), ‘Business survey data: do they help in forecasting GDP growth?’, International Journal of Forecasting, 21, pp. 377–89.CrossRefGoogle Scholar
Marcellino, M. (2008), ‘A linear benchmark for forecasting GDP growth and inflation?’, Journal of Forecasting, 27, pp. 305–40.CrossRefGoogle Scholar
Mitchell, J. (2004), ‘Revisions to economic statistics’, Statistics Commission Report No. 17 Vol. 2. (http://www.statscom.org.uk/uploads/files/reports/Revisions_vol_2.pdf).Google Scholar
Mitchell, J., Smith, R.J., Weale, M.R., Wright, S. and Salazar, E.L. (2005), ‘An indicator of monthly GDP and an early estimate of quarterly GDP growth’, Economic Journal, 115, pp. 108–29.CrossRefGoogle Scholar
Pesaran, M.H. and Timmermann, A. (2007), ‘Selection of estimation window in the presence of breaks’, Journal of Econometrics, 137, pp. 134–61.CrossRefGoogle Scholar
Rhodes, E.C. (1937), ‘The construction of an index of business activity’ (with discussion), Journal of the Royal Statistical Society: Series C, 100, pp. 18–66.CrossRefGoogle Scholar
Salazar, E. and Weale, M. (1999), ‘Monthly data and short-term forecasting: an assessment of monthly data in a VAR model’, Journal of Forecasting, 18, pp. 447–62.3.0.CO;2-T>CrossRefGoogle Scholar
Smith, J. and Wallis, K.F. (2009), ‘A simple explanation of the forecast combination puzzle’, Oxford Bulletin of Economics and Statistics, 71, pp. 331–55.Google Scholar
Stock, J.H. and Watson, M.W. (1999), ‘A comparison of linear and nonlinear univariate models for forecasting macroeconomic time series’, in Engle, R. and White, R. (eds), Cointegration, Causality and Forecasting: A Festschrift in Honor of Clive W.J. Granger, Oxford, Oxford University Press, pp. 1–44.Google Scholar
-(2002), ‘Macroeconomic forecasting using diffusion indexes’, Journal of Business and Economic Statistics, 20, pp. 147–62.Google Scholar
-(2007), ‘Has inflation become harder to forecast?’, Journal of Money, Credit, and Banking, 39, pp. 3–34.CrossRefGoogle Scholar
Timmermann, A. (2006), ‘Forecast combinations’, in Elliott, G., Granger, C.W.J. and Timmermann, A. (eds), Handbook of Economic Forecasting Volume 1, Amsterdam, North-Holland, pp. 135–96.Google Scholar