Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-23T07:06:52.512Z Has data issue: false hasContentIssue false
  • English
  • Français

MODELING MACROECONOMIC SUBAGGREGATES: AN APPLICATION OF NONLINEAR COINTEGRATION

Published online by Cambridge University Press:  01 April 2008

ADUSEI JUMAH  and
ROBERT M. KUNST
ADUSEI JUMAH*
Affiliation:
University of Vienna
ROBERT M. KUNST
Affiliation:
University of Vienna
*
Address correspondense to: Adusei Jumah, Department of Economics, University of Vienna, BWZ, Bruenner Strasse 72, 1210 Vienna, Austria. e-mail: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

Many macroeconometric models depict situations where the shares of the major demand aggregates in output are stable over time. The joint dynamic behavior of the considered demand aggregate and output may thus be approximated by a cointegrated vector autoregression. However, the shares of many demand subaggregates in output are rather mobile and changing over time. In order to simultaneously capture the flexibility of the shares of the subaggregates and the long-run constancy of the share of the total aggregate, we consider trivariate systems of two macroeconomic subaggregates and output with error-correction terms that are nonlinear functions of the original variables. The merits of the models are evaluated by means of several forecasting experiments.

Keywords

National AccountsGreat RatiosForecastingPredictive Accuracy
Type
ARTICLES
Information
Macroeconomic Dynamics , Volume 12 , Issue 2 , April 2008 , pp. 151 - 171
Copyright
Copyright © Cambridge University Press 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Aparicio, Felipe, Escribano, Alvaro, and Sipols, Ana E. 2006 Range unit-root (RUR) tests: Robust against nonlinearities, error distributions, structural breaks and outliers. Journal of Time Series Analysis 27, 545– 576.CrossRefGoogle Scholar
Berndt, Ernst R. 1996 The Practice of Econometrics: Classic and Contemporary. Boston: Addison-Wesley.Google Scholar
Christoffersen, Peter F. and Diebold, Francis X. 1998 Cointegration and long-horizon forecasting. Journal of Business & Economics Statistics 16, 450– 458.CrossRefGoogle Scholar
Diebold, Francis X. and Mariano, Roberto S. 1995 Comparing predictive accuracy. Journal of Business & Economic Statistics 13, 253– 263.CrossRefGoogle Scholar
Engle, Robert F. and Yoo, Byung S. 1987 Forecasting and testing in co-integrated systems. Journal of Econometrics 35, 143– 159.CrossRefGoogle Scholar
Escribano, Alvaro and Mira, Santiago 2002 Nonlinear error correction models. Journal of Time Series Analysis 23, 509– 522.CrossRefGoogle Scholar
Escribano, Alvaro 2004 Nonlinear error correction: The case of money demand in the UK (1878–2000). Macroeconomic Dynamics 8, 76– 116.Google Scholar
Johansen, Soren 1995 Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. Oxford: OxfordUniversity Press.CrossRefGoogle Scholar
Johansen, Søren 1988 Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control 12, 231– 254.CrossRefGoogle Scholar
King, Robert G., Plosser, Charles I., Stock, James H., and Watson, Mark W. 1991 Stochastic trends and economic fluctuations. American Economic Review 81, 819– 840.Google Scholar
Klein, Lawrence R. and Kosobud, Richard F. 1961 Some econometrics of growth: Great ratios of economics. Quarterly Journal of Economics 75, 173– 198.CrossRefGoogle Scholar
Kunst, Robert M. and Neusser, Klaus 1990 Cointegration in a macroeconomic system. Journal of Applied Econometrics 5, 351– 365.CrossRefGoogle Scholar
Romer, David 1996 Advanced Macroeconomics. New York: McGraw-Hill.Google Scholar
Stock, James H. and Watson, Mark W. 1988 Testing for common trends. Journal of the American Statistical Association 83, 1097– 1107.CrossRefGoogle Scholar