Abstract
A structural break was suspected for the Canadian gross domestic product (GDP) time series when the reporting system switched from the Standard Industrial Classification system to the North American Industry Classification System system in 1997, as was previously detected for the United States. Any failure to identify in-sample breaks not only will produce biased parameter estimates but may adversely affect the model’s out-of-sample forecasting performance. This study investigated the possibility of poor forecast performance and biased estimation in the presence of the 1997 structural break in Canadian GDP. We confirmed the detected break in Canadian GDP data (1973–2014). All statistics indicated that the coefficients were not stable over time. Three models were employed to provide more accurate forecasts of GDP. The results demonstrate gains in forecasting precision when out-of-sample models accounted for structural breaks. Decision and policy makers might benefit from more precise GDP anticipation if the models were corrected for the 1997 break.
Similar content being viewed by others
Notes
One of the main problems with economic forecasting is change in the legislation (Clements and Hendry 2002).
Eviews® 10 was used to run built-in tests for model stability.
It is based on the Durbin-Watson h statistics for lagged independent variables. However, in Eviews the Cochrane-Orcutt procedure (AR1) is a non‐linear least squares (NLLS) estimator to overcome inconsistency in estimating beta (EViews® 10 User’s Guide II. (2017), pp. 143–148).
References
Armstrong, J. S. (1978). Forecasting with econometric methods: Folklore versus fact. The Journal of Business, 51(4), 549–564.
Christ, C. F. (1975). Judging the performance of econometric models of the U.S. economy. International Economic Review, 16(1), 54–74.
Clements, M. P., & Hendry, D. F. (2002). A companion to economic forecasting (1st ed.). Wiley-Blackwell Publication.
Diebold, F. X. (1998). The past, present, and future of macroeconomic forecasting. Journal of Economic Perspectives, 12(2), 175–192.
Economic Classification Policy Committee. (1993a). Conceptual issues. Issue Paper No. 1, Bureau of Economic Analysis, Department of Commerce, Washington D. C. https://www.census.gov/eos/www/naics/history/docs/issue_paper_1.pdf. Accessed June 2018.
Economic Classification Policy Committee. (1993b). Aggregation structure and hierarchies. Issue Paper No. 2, Bureau of Economic Analysis, Department of Commerce, Washington D.C. https://www.census.gov/eos/www/naics/history/docs/issue_paper_2.pdf. Accessed June 2018.
Economic Classification Policy Committee. (1993c). Collectability of data. Issue Paper No. 3, Bureau of Economic Analysis, Department of Commerce, Washington D.C. https://www.census.gov/eos/www/naics/history/docs/issue_paper_3.pdf. Accessed June 2018.
Economic Classification Policy Committee. (1993d). Criteria for determining industries. Issue Paper No. 4, Bureau of Economic Analysis, Department of Commerce, Washington D.C. https://www.census.gov/eos/www/naics/history/docs/issue_paper_4.pdf Accessed June 2018.
Economic Classification Policy Committee. (1993e). The impact of classification revisions on time series. Issue Paper No. 5, Bureau of Economic Analysis, Department of Commerce, Washington D.C. https://www.census.gov/eos/www/naics/history/docs/issue_paper_5.pdf. Accessed June 2018.
Economic Classification Policy Committee. (1993f). Services classifications. Issue Paper No. 6, Bureau of Economic Analysis, Department of Commerce, Washington D.C. https://www.census.gov/eos/www/naics/history/docs/issue_paper_6.pdf. Accessed June 2018.
EViews® 10 User’s Guide II. (2017). IHS Global Inc., Irvine, CA (pp. 143–148 and 421–423).
Gujarati, D. N. (2009). Basic econometrics. Tata McGraw-Hill Education.
Harris, H. & Sollis, R. (2003). Applied Time Series Modeling and Forecasting. Wiley, West Essex.
Klein, L. R. (1984). The importance of the forecast. Journal of Forecasting, 3(1), 1–9.
Klein, P. A., & Moore, G. H. (1983). The leading indicator approach to economic forecasting – Retrospect and prospect. Journal of Forecasting, 2(2), 119–135.
Luitel, H. S., & Mahar, G. J. (2015). A short note on the application of chow test of structural break in U.S. GDP. International Business Research, 8(10), 112–116.
Pindyck, R. S., & Rubinfeld, D. L. (1998). Econometric models and economic forecasts (Vol. 4). Boston: Irwin/McGraw-Hill.
Statistics Canada. (2018). CANSIM, electronic database. Retrieved from https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3610043401 (last accessed: December 2018).
Tong, H., & Lim, K. (1980). S. Threshold autoregression, limit cycles and cyclical data, Journal of the Royal Statistical Society. Series B (Methodological), Vol. 42, No. 3. pp. 245–292.
U.S. Department of Commerce (2017), Bureau of Economic Analysis’ electronic database. Retrieved from https://www.bea.gov/data/gdp/gross-domestic-product#gdp (last accessed: July 2017).
Acknowledgments
We thank Lillian Kamal and session participants in the conference for their helpful comments and suggestions. We also thank one anonymous reviewer and the editor for their comments and suggestions.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(DOCX 38 kb)
Rights and permissions
About this article
Cite this article
Amiraslany, A., Luitel, H.S. & Mahar, G.J. Structural Breaks, Biased Estimations, and Forecast Errors in a GDP Series of Canada versus the United States. Int Adv Econ Res 25, 235–244 (2019). https://doi.org/10.1007/s11294-019-09731-w
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11294-019-09731-w
Keywords
- Structural break
- Forecast errors
- US GDP
- Canadian GDP
- Lagged dependent variable
- Static forecast
- Policy making