Interval forecasts and parameter uncertainty

Journal of Econometrics

Volumn 135, Issue 1-2, 2006, Pages 377-398

  Hansen, Bruce E ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

Estimation; Forecast intervals; Nonparametric; Quantile

Indexed keywords

ASYMPTOTIC STABILITY; COMPUTER SIMULATION; DIFFERENCE EQUATIONS; FORECASTING; MEASUREMENT ERRORS; PARAMETER ESTIMATION; PROBLEM SOLVING;

FORECAST INTERVALS; FORECASTING EQUATIONS; NONPARAMETRIC; QUANTILE;

INDUSTRIAL ECONOMICS;

EID: 33747894556     PISSN: 03044076     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jeconom.2005.07.030     Document Type: Article

References (33)

1. Andrews D.W.K. Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica 59 (1991) 817-858
2. Andrews, D.W.K., 1994. Empirical process methods in econometrics. In: Engle, R.F., McFadden, D.L. (Eds.), Handbook of Econometrics, vol. IV, Elsevier Science, Amsterdam, pp. 2248-2296.
3. Bean, C., Jenkinson, N., 2001. The formulation of monetary policy at the Bank of England. Quarterly Bulletinm, Bank of England, 434-441.
4. Britton, E., Fisher, P., Whitley, J., 1998. The Inflation Report projections: understanding the fan chart. Quarterly Bulletin, Bank of England, 30-37.
5. Chatfield C. Calculating interval forecasts. Journal of Business and Economic Statistics 11 (1993) 121-135
6. Christoffersen P.F. Evaluating interval forecasts. International Economic Review 39 (1998) 840-841
7. Cogley, T., Morozov, S., Sargent, T.J., 2003. Bayesian fan charts for UK inflation: forecasting and sources of uncertainty in an evolving monetary system, working paper.
8. Corradi, V., Swanson, N.R., 2004. Predictive Density Accuracy Tests. Rutgers University, working paper.
9. Diebold F.X., Gunther T.A., and Tay A.S. Evaluating density forecasts with applications to financial risk management. International Economic Review 39 (1998) 863-883
10. Diebold F.X., Hahn J., and Tay A.S. Multivariate density forecast evaluation and calibration in financial risk management: high-frequency returns on foreign exchange. The Review of Economics and Statistics 81 (1999) 661-673
11. Diebold F.X., Tay A.S., and Wallis K.F. Evaluating density forecasts of inflation: the survey of professional forecasters. In: Granger W.J., Engle R., and White H. (Eds). Cointegration, Causality, and Forecasting: a Festschrift in Honour of Clive (1999), Oxford University Press, Oxford 76-90
12. Doukhan P., Massart P., and Rio E. Invariance principles for absolutely regular empirical processes. Annales de l'Institut H. Poincare, Probabiltes et Statistiques 31 (1995) 393-427
13. Fan J., and Qiwei Y. Nonlinear Time Series: nonparametric and Parametric Methods (2003), Springer, New York
14. Granger C.W.J. Can we improve the perceived quality of economic forecasts?. Journal of Applied Econometrics 11 (1996) 455-473
15. Granger C.W.J., and Newbold P. Forecasting Economic Time Series. second ed (1986), Academic Press, New York
16. Granger C.W.J., and Pesaran M.H. A decision theoretic approach to forecast evaluation. In: Chan W.S., Li W.K., and Tong H. (Eds). Statistics and Finance: An Interface (1999), Imperial College Press
17. Granger C.W.J., White H., and Kamstra M. Interval forecasting: an analysis based upon ARCH-quantile estimators. Journal of Econometrics 40 (1989) 87-96
18. Hansen, B.E., 2004. Bandwidth Selection for Nonparametric Distribution Estimation. University of Wisconsin, manuscript.
19. Hirukawa M. A two-stage plug-in bandwidth selection and its implementation for covariance estimation (2005), Concordia University, manuscript
20. Ing C.K. Multistep prediction in autoregressive processes. Econometric Theory 19 (2003) 254-279
21. Marron J.S., and Wand M.P. Exact mean integrated squared error. Annals of Statistics 20 (1992) 712-736
22. Newey W.K., and West K.D. Automatic lag selection in covariance matrix estimation. Review of Economic Studies 61 (1994) 631-653
23. Otrok, C., Whiteman, C.H., 1997. What to do when the crystal ball is cloudy: conditional and unconditional forecasting in Iowa. Proceedings of the National Tax Association. pp. 326-334.
24. Otrok C., and Whiteman C.H. Bayesian leading indicators: measuring and predicting economic conditions in Iowa. International Economic Review 39 (1998) 997-1014
25. Robertson, J.C., Tallman, E.W., Whiteman, C.H., 2003. Forecasting using relative entropy, Federal Reserve Bank of Atlanta, Working paper no. 2002-2022.
26. Stock J.H., and Watson M.W. Forecasting inflation. Journal of Monetary Economics 44 (1999) 293-335
27. Stock J.H., and Watson M.W. Combination forecasts of output growth in a seven-country data set (2003), Harvard University
28. Tay A.S., and Wallis K.F. Density forecasting: a survey. Journal of Forecasting 19 (2000) 235-254
29. Tiao G.C., and Tsay R.S. Some advances in nonlinear and adaptive modeling in time series. Journal of Forecasting 13 (1994) 109-131
30. Wallis K.F. Chi-square tests of interval and density forecasts, and the Bank of England's Fan Charts. International Journal of Forecasting 19 (2003) 165-175
31. Wallis, K.F., 2003b. Forecast uncertainty, its representation and evaluation. Boletin Inflacion y Analisis Macroeconomico. pp. 89-98.
32. West K.D. Another heteroskedasticity- and autocorrelation-consistent covariance matrix estimator. Journal of Econometrics 76 (1997) 171-191
33. Zarnowitz V., and Braun P. Twenty-two years of the NBER-ASA quarterly economic outlook surveys: aspects and comparisons of forecasting performance. In: Stock J.H., and Watson M.W. (Eds). Business Cycles, Indicators, and Forecasting (1993), University of Chicago Press, Chicago