Hostname: page-component-848d4c4894-ttngx Total loading time: 0 Render date: 2024-05-04T13:24:14.827Z Has data issue: false hasContentIssue false
  • English
  • Français

The National Institute Density Forecasts of Inflation

Published online by Cambridge University Press:  26 March 2020

James Mitchell
James Mitchell*
Affiliation:
National Institute of Economic and Social Research
*
e-mail: j.mitchell@niesr.ac.uk
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper has three aims. First, we summarise and thereby make readily available the historical time-series of quarterly density forecasts of year ahead RPIX (RPI excluding mortgage payments) inflation made by the National Institute for the period 1994Q1–2004Q4. Previous work has focused on those forecasts made in Q4 only. Secondly, we evaluate the quality of these density forecasts. Thirdly, with the benefit of hindsight we draw lessons for the future production and use of density forecasts.

Keywords

density forecastsevaluating forecastsinflation forecastingC53E37
Type
Articles
Information
National Institute Economic Review , Volume 193 , July 2005 , pp. 60 - 69
Copyright
Copyright © 2005 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.)

References

Andrews, D.W.K. (1993), ‘Tests for parameter instability and structural change with unknown change point’, Econometrica, 61, pp. 821–856.CrossRefGoogle Scholar
Andrews, D.W.K. (2003), ‘End-of-sample instability tests’, Econometrica, 71, pp. 1661–1694.CrossRefGoogle Scholar
Bai, J. and Ng, S. (2005), ‘Tests of skewness, kurtosis and normality for time series data’, Journal of Business and Economics Statistics, 23, pp. 49–60.CrossRefGoogle Scholar
Bai, J. and Perron, P. (1998), ‘Estimating and testing linear models with multiple structural changes’, Econometrica, 66, pp. 47–78.CrossRefGoogle Scholar
Bai, J. and Perron, P. (2003), ‘Computation and analysis of multiple structural change models’, Journal of Applied Econometrics, 18, pp. 1–22.CrossRefGoogle Scholar
Barrell, R. (2001), ‘Forecasting the world economy’, in Hendry, D.F. and Ericsson, N.R. (eds), Understanding Economic Forecasts, Cambridge, Mass., MIT Press, pp. 152–173.Google Scholar
Berkowitz, J. (2001), ‘Testing density forecasts, with applications to risk management’, Journal of Business and Economic Statistics, 19, pp. 465–474.CrossRefGoogle Scholar
Blake, A. (1996), ‘Forecast error bounds by stochastic simulation’, National Institute Economic Review, 156, pp. 72–79.CrossRefGoogle Scholar
Britton, E., Fisher, P. and Whitley, J. (1998), ‘The Inflation Report projections: understanding the fan chart’, Bank of England Quarterly Bulletin, 38, pp. 30–37.Google Scholar
Clements, M.P. (2004), ‘Evaluating the Bank of England density forecasts of inflation’, Economic Journal, 114, pp. 844–866.CrossRefGoogle Scholar
Clements, M.P. and Smith, J. (2000), ‘Evaluating the forecast densities of linear and nonlinear models: applications to output growth and unemployment’, Journal of Forecasting, 19, pp. 255–276.3.0.CO;2-G>CrossRefGoogle Scholar
Diebold, F.X., Gunther, A.S. and Tay, K.F. (1998), ‘Evaluating density forecasts with application to financial risk management’, International Economic Review, 39, pp. 863–883.CrossRefGoogle Scholar
Hall, S.G. and Mitchell, J. (2004), ‘Density forecast combination’, NIESR Discussion Paper No. 249, available at http://www.niesr.ac.uk/pubs/dps/dp249.pdf.Google Scholar
Hall, S.G. and Mitchell, J. (2005), ‘Optimal combination of density forecasts’, NIESR Discussion Paper No. 248 (revised), available at http://www.niesr.ac.uk/pubs/dps/dp248.pdf.Google Scholar
Hansen, B.E. (1997), ‘Approximate asymptotic p-values for structural-change tests’, Journal of Business and Economic Statistics, 15, pp. 60–67.Google Scholar
Mitchell, J. and Hall, S.G. (2005), ‘Evaluating, comparing and combining density forecasts using the KLIC with an application to the Bank of England and NIESR ‘fan’ charts of inflation’, NIESR Discussion Paper No. 253, available at http://www.niesr.ac.uk/pubs/dps/dp253.pdf.CrossRefGoogle Scholar
Pesaran, M.H. and Timmermann, A. (2004), ‘How costly is it to ignore breaks when forecasting the direction of a time series?’, International Journal of Forecasting, 20, pp. 411–425.CrossRefGoogle Scholar
Poulizac, D., Weale, M. and Young, G. (1996), ‘The performance of National Institute economic forecasts’, National Institute Economic Review, 156, pp. 55–62.CrossRefGoogle Scholar
Sensier, M. and van Dijk, D. (2004), ‘Testing for volatility changes in U.S. macroeconomic time series’, Review of Economics and Statistics, 86, pp. 833–839.CrossRefGoogle Scholar
Wallis, K.F. (1989), ‘Macroeconomic forecasting: a survey’, Economic Journal, 99, pp. 28–61.CrossRefGoogle Scholar
Wallis, K.F. (2004), ‘An assessment of Bank of England and National Institute inflation forecast uncertainties’, National Institute Economic Review, 189, pp. 64–71.CrossRefGoogle Scholar