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Forecasting Turkish Industrial Production Growth With Static Factor Models

Year 2015, Volume: 7 Issue: 2, 64 - 78, 01.09.2015
https://doi.org/10.33818/ier.278041

Abstract

In this paper, we forecast industrial production growth for the Turkish economy using static factor models. We evaluate how the performance of the models change based on the number of factors we extract from our data as well as the level of aggregation for the series in the data set. We consider two evaluation samples for the out-of-sample forecasting exercise to assess the stability of the forecasting performance. We find that the effect of the data set size on the forecasting performance is not independent from the number of factors extracted from this data set. Rankings of the models change in different evaluation samples. We conclude that using a dynamic approach to evaluate models from different dimensions is important in the forecasting process.

References

  • Altug, S. and E. Uluceviz (2013). Identifying Leading Indicators of Real Activity and Inflation for Turkey, 1988-2010: A Pseudo Out-of-Sample Forecasting Approach. OECD Journal: Journal of Business Cycle Measurement and Analysis, 2, 1-37.
  • Angelini, E., M. Banbura and G. Rünstler (2010). Estimating and Forecasting the Euro Area Monthly National Accounts from a Dynamic Factor Model. OECD Journal: Journal of Business Cycle Measurement and Analysis, 1, 1-22.
  • Bai, J. and S. Ng (2002). Determining the Number of Factors in Approximate Factor Models. Econometrica, 70, 191-221.
  • Barhoumi, K., O. Darne and L. Ferrara (2010). Are Disaggregate Data Useful for Factor Analysis in Forecasting French GDP? Journal of Forecasting, 29, 132-144.
  • Barhoumi, K., O. Darne and L. Ferrara (2013). Testing the Number of Factors: An Empirical Assessment for a Forecasting Purpose. Oxford Bulletin of Economics and Statistics, 75, 64-79.
  • Boivin, J. and S. Ng (2005). Understanding and Comparing Factor Based Forecasts. International Journal of Central Banking, 1, 117-151.
  • Boivin, J. and S. Ng (2006). Are More Data Always Better for Factor Analysis? Journal of Econometrics, 132, 169-194.
  • Eickmeier, S. and C. Ziegler (2008). How Successful are Dynamic Factor Models at Forecasting Output and Inflation? A Meta-Analytic Approach. Journal of Forecasting, 27, 237-265.
  • Gupta, R. and A. Kabundi (2011). A Large Factor Model for Forecasting Macroeconomic Variables in South Africa. International Journal of Forecasting, 27, 1076-1088.
  • Rünstler, G., K. Barhoumi, S. Benk, R. Cristadoro, A. Den Reijer, A. Jakaitiene, P. Jelonek, A. Rua, K. Ruth and C. Van Nieuwenhuyze (2009). Short-term Forecasting of GDP Using Large Datasets: A Pseudo Real-time Forecast Evaluation Exercise. Journal of Forecasting, 28, 595-611.
  • Stock, J. and M. Watson (2002a). Macroeconomic Forecasting Using Diffusion Indexes. Journal of Business and Economic Statistics, 20 (2), 147-162.
  • Stock, J. and M. Watson (2002b). Forecasting with Principal Components from a Large Number of Predictors. Journal of American Statistical Association, 97, 1167-1179.
  • Stock, J. and M. Watson (2003). Forecasting Output and Inflation: The Role of Asset Prices. Journal of Economic Literature, 16, 788-829.
Year 2015, Volume: 7 Issue: 2, 64 - 78, 01.09.2015
https://doi.org/10.33818/ier.278041

Abstract

References

  • Altug, S. and E. Uluceviz (2013). Identifying Leading Indicators of Real Activity and Inflation for Turkey, 1988-2010: A Pseudo Out-of-Sample Forecasting Approach. OECD Journal: Journal of Business Cycle Measurement and Analysis, 2, 1-37.
  • Angelini, E., M. Banbura and G. Rünstler (2010). Estimating and Forecasting the Euro Area Monthly National Accounts from a Dynamic Factor Model. OECD Journal: Journal of Business Cycle Measurement and Analysis, 1, 1-22.
  • Bai, J. and S. Ng (2002). Determining the Number of Factors in Approximate Factor Models. Econometrica, 70, 191-221.
  • Barhoumi, K., O. Darne and L. Ferrara (2010). Are Disaggregate Data Useful for Factor Analysis in Forecasting French GDP? Journal of Forecasting, 29, 132-144.
  • Barhoumi, K., O. Darne and L. Ferrara (2013). Testing the Number of Factors: An Empirical Assessment for a Forecasting Purpose. Oxford Bulletin of Economics and Statistics, 75, 64-79.
  • Boivin, J. and S. Ng (2005). Understanding and Comparing Factor Based Forecasts. International Journal of Central Banking, 1, 117-151.
  • Boivin, J. and S. Ng (2006). Are More Data Always Better for Factor Analysis? Journal of Econometrics, 132, 169-194.
  • Eickmeier, S. and C. Ziegler (2008). How Successful are Dynamic Factor Models at Forecasting Output and Inflation? A Meta-Analytic Approach. Journal of Forecasting, 27, 237-265.
  • Gupta, R. and A. Kabundi (2011). A Large Factor Model for Forecasting Macroeconomic Variables in South Africa. International Journal of Forecasting, 27, 1076-1088.
  • Rünstler, G., K. Barhoumi, S. Benk, R. Cristadoro, A. Den Reijer, A. Jakaitiene, P. Jelonek, A. Rua, K. Ruth and C. Van Nieuwenhuyze (2009). Short-term Forecasting of GDP Using Large Datasets: A Pseudo Real-time Forecast Evaluation Exercise. Journal of Forecasting, 28, 595-611.
  • Stock, J. and M. Watson (2002a). Macroeconomic Forecasting Using Diffusion Indexes. Journal of Business and Economic Statistics, 20 (2), 147-162.
  • Stock, J. and M. Watson (2002b). Forecasting with Principal Components from a Large Number of Predictors. Journal of American Statistical Association, 97, 1167-1179.
  • Stock, J. and M. Watson (2003). Forecasting Output and Inflation: The Role of Asset Prices. Journal of Economic Literature, 16, 788-829.
There are 13 citations in total.

Details

Subjects Business Administration
Other ID JA45UT67RH
Journal Section Articles
Authors

Mahmut Günay This is me

Publication Date September 1, 2015
Submission Date September 1, 2015
Published in Issue Year 2015 Volume: 7 Issue: 2

Cite

APA Günay, M. (2015). Forecasting Turkish Industrial Production Growth With Static Factor Models. International Econometric Review, 7(2), 64-78. https://doi.org/10.33818/ier.278041
AMA Günay M. Forecasting Turkish Industrial Production Growth With Static Factor Models. IER. December 2015;7(2):64-78. doi:10.33818/ier.278041
Chicago Günay, Mahmut. “Forecasting Turkish Industrial Production Growth With Static Factor Models”. International Econometric Review 7, no. 2 (December 2015): 64-78. https://doi.org/10.33818/ier.278041.
EndNote Günay M (December 1, 2015) Forecasting Turkish Industrial Production Growth With Static Factor Models. International Econometric Review 7 2 64–78.
IEEE M. Günay, “Forecasting Turkish Industrial Production Growth With Static Factor Models”, IER, vol. 7, no. 2, pp. 64–78, 2015, doi: 10.33818/ier.278041.
ISNAD Günay, Mahmut. “Forecasting Turkish Industrial Production Growth With Static Factor Models”. International Econometric Review 7/2 (December 2015), 64-78. https://doi.org/10.33818/ier.278041.
JAMA Günay M. Forecasting Turkish Industrial Production Growth With Static Factor Models. IER. 2015;7:64–78.
MLA Günay, Mahmut. “Forecasting Turkish Industrial Production Growth With Static Factor Models”. International Econometric Review, vol. 7, no. 2, 2015, pp. 64-78, doi:10.33818/ier.278041.
Vancouver Günay M. Forecasting Turkish Industrial Production Growth With Static Factor Models. IER. 2015;7(2):64-78.