Modeling National Accounts Sub-Aggregates. An Application of Non-Linear Error Correction
AbstractMany 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 sub-aggregates in output are rather mobile and changing over time. In order to simultaneously capture the flexibility of the shares of the sub-aggregates and the long-run constancy of the share of the total aggregate, we consider trivariate systems of two macroeconomic sub-aggregates and output with errorcorrection terms that are non-linear functions of the original variables. The merits of the models are evaluated by means of several forecasting experiments.
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Bibliographic InfoPaper provided by Institute for Advanced Studies in its series Economics Series with number 149.
Length: 39 pages
Date of creation: Mar 2004
Date of revision:
Postal: Institute for Advanced Studies - Library, Stumpergasse 56, A-1060 Vienna, Austria
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
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- Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501, October.
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- Jumah, Adusei & Kunst, Robert M., 2005. "Forecasting Aggregate Demand in West African Economies. The Influence of Immigrant Remittance Flows and of Asymmetric Error Correction," Economics Series 168, Institute for Advanced Studies.
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