This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Bušs, Ginters

Additional information is available for the following registered author(s):

Abstract

This paper contributes to the literature by comparing predictive accuracy of one-period real-time simple seasonal ARIMA forecasts of Latvia's Gross Domestic Product (GDP) as well as by comparing a direct forecast of Latvia's GDP versus three kinds of indirect forecasts. Four main results are as follows. Direct forecast of Latvia's Gross Domestic Product (GDP) seems to yield better precision than an indirect one. AR(1) model tends to give more precise forecasts than the benchmark moving-average models. An extra regular differencing appears to help better forecast Latvia's GDP in an economic downturn. Finally, only AR(1) gives forecasts with better precision compared to a naive Random Walk model.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. 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.

File URL: http://mpra.ub.uni-muenchen.de/16684/
File Format:
File Function: orginal version
Download Restriction: no
File URL: http://mpra.ub.uni-muenchen.de/16825/
File Format:
File Function: revised version
Download Restriction: no
File URL: http://mpra.ub.uni-muenchen.de/16832/
File Format:
File Function: revised version
Download Restriction: no

Publisher Info
Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 16684.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: 06 Aug 2009
Date of revision:
Handle: RePEc:pra:mprapa:16684

Contact details of provider:
Postal: Schackstr. 4, D-80539 Munich, Germany
Phone: +49-(0)89-2180-2219
Fax: +49-(0)89-2180-3900
Web page: http://mpra.ub.uni-muenchen.de
More information through EDIRC

For technical questions regarding this item, or to correct its listing, contact: (Ekkehart Schlicht).

Related research
Keywords: real-time forecasting; seasonal ARIMA; Direct versus indirect forecasting; Latvia's GDP;

Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods

This paper has been announced in the following NEP Reports:

References listed on IDEAS
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.:
  1. Ghosh, Damayanti, 1989. "Maximum likelihood estimation of the dynamic shock-error model," Journal of Econometrics, Elsevier, vol. 41(1), pages 121-143, May. [Downloadable!] (restricted)
  2. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May. [Downloadable!] (restricted)
    Other versions:
  3. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2003. "The Generalized Dynamic Factor Model. One-Sided Estimation and Forecasting," LEM Papers Series 2003/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy. [Downloadable!]
    Other versions:
  4. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP," Economics Working Papers ECO2009/13, European University Institute. [Downloadable!]
    Other versions:
  5. Igor Masten & Massimiliano Marcellino & Anindya Banerjeey, 2009. "Forecasting with Factor-augmented Error Correction Models," RSCAS Working Papers 2009/32, European University Institute. [Downloadable!]
    Other versions:
  6. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  7. Watson, Mark W., 1989. "Recursive solution methods for dynamic linear rational expectations models," Journal of Econometrics, Elsevier, vol. 41(1), pages 65-89, May. [Downloadable!] (restricted)
  8. Hylleberg, S. & Engle, R. F. & Granger, C. W. J. & Yoo, B. S., 1990. "Seasonal integration and cointegration," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 215-238. [Downloadable!] (restricted)
    Other versions:
  9. Konstantins Benkovskis, 2008. "Short-Term Forecasts of Latvia's Real Gross Domestic Product Growth Using Monthly Indicators," Working Papers 2008/05, Latvijas Banka. [Downloadable!]
  10. Wang, Mu-Chun, 2008. "Comparing the DSGE model with the factor model: an out-of-sample forecasting experiment," Discussion Paper Series 1: Economic Studies 2008,04, Deutsche Bundesbank, Research Centre. [Downloadable!]
  11. Schumacher, Christian, 2009. "Factor forecasting using international targeted predictors: the case of German GDP," Discussion Paper Series 1: Economic Studies 2009,10, Deutsche Bundesbank, Research Centre. [Downloadable!]
  12. Barhoumi, K. & Darné, O. & Ferrara, L., 2009. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Documents de Travail 232, Banque de France. [Downloadable!]
  13. Sandra Eickmeier & Tim Ng, 2009. "Forecasting national activity using lots of international predictors: an application to New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2009/04, Reserve Bank of New Zealand. [Downloadable!]
    Other versions:
  14. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank, Research Centre. [Downloadable!]
  15. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  16. Viktors Ajevskis & Gundars Davidsons, 2008. "Dynamic Factor Models in Forecasting Latvia's Gross Domestic Product," Working Papers 2008/02, Latvijas Banka. [Downloadable!]
  17. Dreger, Christian & Schumacher, Christian, 2002. "Estimating Large-Scale Factor Models for Economic Activity in Germany: Do They Outperform Simpler Models?," Discussion Paper Series 26321, Hamburg Institute of International Economics. [Downloadable!]
  18. Hamilton, James D, 1985. "Uncovering Financial Market Expectations of Inflation," Journal of Political Economy, University of Chicago Press, vol. 93(6), pages 1224-41, December. [Downloadable!] (restricted)
  19. Hannan, E J, 1971. "The Identification Problem for Multiple Equation Systems with Moving Average Errors," Econometrica, Econometric Society, vol. 39(5), pages 751-65, September. [Downloadable!] (restricted)
  20. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
    Other versions:
Full references

Statistics
Access and download statistics

Did you know? You too can volunteer for RePEc, for example by encouraging others to use our services.

This page was last updated on 2009-11-29.


This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.