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.
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
.
4.
Bai, J.
and
Perron, P.
(1998),
‘Estimating and testing linear models with multiple structural changes’
, Econometrica, 66, pp.
47-78
.
5.
Bai, J.
and
Perron, P.
(2003),
‘Computation and analysis of multiple structural change models’
, Journal of Applied Econometrics, 18, pp.
1-22
.
6.
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.
7.
Berkowitz, J.
(2001),
‘Testing density forecasts, with applications to risk management’
, Journal of Business and Economic Statistics, 19,
pp.
465-474
.
8.
Blake, A.
(1996),
‘Forecast error bounds by stochastic simulation’
, National Institute Economic Review, 156, pp.
72-79
.
9.
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
.
10.
Clements, M.P.
(2004),
‘Evaluating the Bank of England density forecasts of inflation’
, Economic Journal, 114, pp.
844-866
.
11.
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
.
12.
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
.
13.
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.
14.
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.
15.
Hansen, B.E.
(1997),
‘Approximate asymptotic p-values for structural-change tests’
, Journal of Business and Economic Statistics, 15,
pp.
60-67
.
16.
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.
17.
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
.
18.
Poulizac, D.
,
Weale, M.
and
Young, G.
(1996),
‘The performance of National Institute economic forecasts’
, National Institute Economic Review, 156, pp.
55-62
.
19.
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
.
20.
Wallis, K.F.
(1989),
‘Macroeconomic forecasting: a survey’
, Economic Journal, 99, pp.
28-61
.
21.
Wallis, K.F.
(2004),
‘An assessment of Bank of England and National Institute
inflation forecast uncertainties’
, National Institute Economic Review, 189, pp.
64-71
.