A Multi-Step Forecast Density
AbstractThis paper makes two contribution to the literature on density forecasts. First, we propose a novel bootstrap approach to estimate forecasting densities based on nonparametric techniques. The method is based on the Markov Bootstrap that is suitable to resample dependent data. The combination of nonparametric and bootstrap methods delivers density forecasts that are flexible in capturing markovian dependence (linear and nonlinear) occurring in any moment of the distribution. Second, we improve the testing approach to evaluate density forecasts by considering a set of tests for dynamical misspecification such as autocorrelation, heteroskedasticity and neglected nonlinearity. The approach is useful because rejections of the tests give insights into ways to improve the forecasting model. By Monte Carlo simulations we show that the proposed evaluation strategy has much higher power to detect misspecification of the density forecasts compared to previous analysis. The proposed nonparametric-bootstrap forecasting method exhibits the ability to capture correctly the dynamics of linear and nonlinear time series models. We also investigate the performance at higher orders and propose methods to deal with the \u201ccurse of dimensionality\u201d. Finally, we empirically investigate the relevance of the method in out-of-sample forecasting the density of 3 business cycles variables for the US: real GDP, the Coincident Indicator and Industrial Production. The results indicate that the method gives reliable density forecasts for all variables and performs better compared to parametric forecasting methods.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance in its series CeNDEF Working Papers with number 05-05.
Date of creation: 2005
Date of revision:
Contact details of provider:
Postal: Dept. of Economics and Econometrics, Universiteit van Amsterdam, Roetersstraat 11, NL - 1018 WB Amsterdam, The Netherlands
Phone: + 31 20 525 52 58
Fax: + 31 20 525 52 83
Web page: http://www.fee.uva.nl/cendef/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Michael P. Clements & Philip Hans Franses & Norman R. Swanson, 2003.
"Forecasting economic and financial time-series with non-linear models,"
Departmental Working Papers
200309, Rutgers University, Department of Economics.
- Clements, Michael P. & Franses, Philip Hans & Swanson, Norman R., 2004. "Forecasting economic and financial time-series with non-linear models," International Journal of Forecasting, Elsevier, vol. 20(2), pages 169-183.
- M. Rajarshi, 1990. "Bootstrap in Markov-sequences based on estimates of transition density," Annals of the Institute of Statistical Mathematics, Springer, vol. 42(2), pages 253-268, June.
- Siliverstovs, B. & van Dijk, D.J.C., 2003. "Forecasting industrial production with linear, nonlinear, and structural change models," Econometric Institute Research Papers EI 2003-16, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Pesaran, M. Hashem & Potter, Simon M., 1997.
"A floor and ceiling model of US output,"
Journal of Economic Dynamics and Control,
Elsevier, vol. 21(4-5), pages 661-695, May.
- Clements, Michael P. & Smith, Jeremy, 1997. "The performance of alternative forecasting methods for SETAR models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 463-475, December.
- Valentina Corradi & Norman Swanson, 2004.
"Predictive Density Evaluation,"
Departmental Working Papers
200419, Rutgers University, Department of Economics.
- Dick van Dijk & Philip Hans Franses & Michael P. Clements & Jeremy Smith, 2003.
"On SETAR non-linearity and forecasting,"
Journal of Forecasting,
John Wiley & Sons, Ltd., vol. 22(5), pages 359-375.
- Cees Diks & Sebastiano Manzan, 2001.
"Tests for Serial Independence and Linearity based on Correlation Integrals,"
Tinbergen Institute Discussion Papers
01-085/1, Tinbergen Institute.
- Diks Cees & Manzan Sebastiano, 2002. "Tests for Serial Independence and Linearity Based on Correlation Integrals," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(2), pages 1-22, July.
- Diks, C.G.H. & Manzan, S., 2001. "Tests for serial independence and linearity based on correlation integrals," CeNDEF Working Papers 01-02, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
- Hyndman, R.J. & Yao, Q., 1998.
"Nonparametric Estimation and Symmetry Tests for Conditional Density Functions,"
Monash Econometrics and Business Statistics Working Papers
17/98, Monash University, Department of Econometrics and Business Statistics.
- Qiwei Yao & Rob J. Hyndman, 2002. "Nonparametric estimation and symmetry tests for conditional density functions," LSE Research Online Documents on Economics 6092, London School of Economics and Political Science, LSE Library.
- Clements, M.P. & Smith J., 1998. "Evaluating The Forecast of Densities of Linear and Non-Linear Models: Applications to Output Growth and Unemployment," The Warwick Economics Research Paper Series (TWERPS) 509, University of Warwick, Department of Economics.
- Joel L. Horowitz, 2003. "Bootstrap Methods for Markov Processes," Econometrica, Econometric Society, vol. 71(4), pages 1049-1082, 07.
- Yongmiao Hong & Haitao Li & Feng Zhao, 2004. "Out-of-Sample Performance of Discrete-Time Spot Interest Rate Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 457-473, October.
- Yongmiao Hong, 2005. "Nonparametric Specification Testing for Continuous-Time Models with Applications to Term Structure of Interest Rates," Review of Financial Studies, Society for Financial Studies, vol. 18(1), pages 37-84.
- Paparoditis, Efstathios & Politis, Dimitris N., 2001. "A Markovian Local Resampling Scheme For Nonparametric Estimators In Time Series Analysis," Econometric Theory, Cambridge University Press, vol. 17(03), pages 540-566, June.
- Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Cees C.G. Diks).
If references are entirely missing, you can add them using this form.