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Econometric Methodology Ii : Strengthening Time Series Analysis

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  • Townsend, Rob F.

Abstract

This article reviews some of the recent methodology developed for the analysis of time series data stressing that the statistical properties of the individual series need to be analysed to avoid spurious regressions. A convergence of econometric methodology is entertained with specific focus on cointegration and error correction models which allows the testing of long run relationships between variables and allows for a more dynamic structure than some of the previous models that appear in the literature. An example of this is the commonly used partial adjustment model in supply analysis which is nested in the less restrictive error correction model. Tests can be performed on the validity of these restrictions. These models have a wide application in agricultural economic analysis.

Suggested Citation

  • Townsend, Rob F., 1998. "Econometric Methodology Ii : Strengthening Time Series Analysis," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 37(01), pages 1-17, March.
    • Handle: RePEc:ags:agreko:54908
    DOI: 10.22004/ag.econ.54908
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    References listed on IDEAS

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    1. Banerjee, Anindya & Dolado, Juan J. & Galbraith, John W. & Hendry, David, 1993. "Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data," OUP Catalogue, Oxford University Press, number 9780198288107.
    2. Perron, Pierre, 1988. "Trends and random walks in macroeconomic time series : Further evidence from a new approach," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 297-332.
    3. Sargan, John Denis & Bhargava, Alok, 1983. "Testing Residuals from Least Squares Regression for Being Generated by the Gaussian Random Walk," Econometrica, Econometric Society, vol. 51(1), pages 153-174, January.
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    5. Davidson, James E H, et al, 1978. "Econometric Modelling of the Aggregate Time-Series Relationship between Consumers' Expenditure and Income in the United Kingdom," Economic Journal, Royal Economic Society, vol. 88(352), pages 661-692, December.
    6. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    7. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
    8. Hall, Stephen G & Milne, Alistair, 1994. "The Relevance of P-Star Analysis to UK Monetary Policy," Economic Journal, Royal Economic Society, vol. 104(424), pages 597-604, May.
    9. Mosconi, Rocco & Giannini, Carlo, 1992. "Non-causality in Cointegrated Systems: Representation Estimation and Testing," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 54(3), pages 399-417, August.
    10. Hansen, Bruce E., 1992. "Efficient estimation and testing of cointegrating vectors in the presence of deterministic trends," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 87-121.
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    Cited by:

    1. Thirtle, C., 2013. "Theory, measurement, policy and politics: Agricultural R&D and productivity in three countries," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 51(3), February.
    2. Mashamaite, P. & Moholwa, B., 2005. "Price asymmetry in South African futures markets for agricultural commodities," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 44(3), pages 1-11, September.
    3. repec:ags:ijag24:345073 is not listed on IDEAS

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