GDP Modelling with Factor Model: an Impact of Nested Data on Forecasting Accuracy
Uncertainty associated with an optimal number of macroeconomic variables to be used in factor model is challenging since there is no criteria which states what kind of data should be used, how many variables to employ and does disaggregated data improve factor model’s forecasts. The paper studies an impact of nested macroeconomic data on Latvian GDP forecasting accuracy within factor modelling framework. Nested data means disaggregated data or sub-components of aggregated variables. We employ Stock-Watson factor model in order to estimate factors and to make GDP projections two periods ahead. Root mean square error is employed as the standard tool to measure forecasting accuracy. According to this empirical study we conclude that additional information that contained in disaggregated components of macroeconomic variables could be used to enhance Latvian GDP forecasting accuracy. The efficiency gain improving forecasts is about 0.15-0.20 percentage points of year on year quarterly growth for the forecasting period 1 quarter ahead, but for 2 quarter ahead it’s about half percentage point.
|Date of creation:||08 Apr 2011|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://mpra.ub.uni-muenchen.de
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.:
- Forni, Mario, et al, 2001.
"Coincident and Leading Indicators for the Euro Area,"
Royal Economic Society, vol. 111(471), pages C62-85, May.
- Lucrezia Reichlin & Mario Forni & Marc Hallin & Marco Lippi, 2001. "Coincident and leading indicators for the Euro area," ULB Institutional Repository 2013/10137, ULB -- Universite Libre de Bruxelles.
- Giovanni Caggiano & George Kapetanios & Vincent Labhard, 2011.
"Are more data always better for factor analysis? Results for the euro area, the six largest euro area countries and the UK,"
Journal of Forecasting,
John Wiley & Sons, Ltd., vol. 30(8), pages 736-752, December.
- Caggiano, Giovanni & Kapetanios, George & Labhard, Vincent, 2009. "Are more data always better for factor analysis? Results for the euro area, the six largest euro area countries and the UK," Working Paper Series 1051, European Central Bank.
- Boivin, Jean & Ng, Serena, 2006.
"Are more data always better for factor analysis?,"
Journal of Econometrics,
Elsevier, vol. 132(1), pages 169-194, May.
- Mario Forni & Lucrezia Reichlin, 1998.
"Let's get real: a factor analytical approach to disaggregated business cycle dynamics,"
ULB Institutional Repository
2013/10147, ULB -- Universite Libre de Bruxelles.
- Forni, Mario & Reichlin, Lucrezia, 1998. "Let's Get Real: A Factor Analytical Approach to Disaggregated Business Cycle Dynamics," Review of Economic Studies, Wiley Blackwell, vol. 65(3), pages 453-73, July.
- Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
- James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
- 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.
When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:30211. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ekkehart Schlicht)
If references are entirely missing, you can add them using this form.