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Flexible Distributed Lags

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  • Chotikapanich, Duangkamon
  • Griffiths, William E.

Abstract

In econometrics there is a long history of using continuous functions to force distributed lag coefficients to behave in an economically accepted way. For example, geometrically declining lags have often been used to model coefficients that we believe should be declining. Polynomial lags have been used to model lag coefficients expected to increase and then decrease. In this paper a more flexible way of imposing such prior information is investigated. Inequality constraints are used to impose knowledge about the relative magnitudes of coefficients without forcing them to lie on a smooth continuous curve. A Metropolis algorithm is used to get posterior density functions for the lag coefficients and functions of those coefficients for the Nerlove orange data and the Almon capital expenditures data.

Suggested Citation

  • Chotikapanich, Duangkamon & Griffiths, William E., 2000. "Flexible Distributed Lags," 2000 Conference (44th), January 23-25, 2000, Sydney, Australia 123623, Australian Agricultural and Resource Economics Society.
    • Handle: RePEc:ags:aare00:123623
    DOI: 10.22004/ag.econ.123623
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    References listed on IDEAS

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    1. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
    2. Marc Nerlove & Frederick V. Waugh, 1961. "Advertising without Supply Control: Some Implications of a Study of the Advertising of Oranges," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 43(4_Part_I), pages 813-837.
    3. Griffiths, William E & Chotikapanich, Duangkamon, 1997. "Bayesian Methodology for Imposing Inequality Constraints on a Linear Expenditure System with Demographic Factors," Australian Economic Papers, Wiley Blackwell, vol. 36(69), pages 321-341, December.
    4. Geweke, John, 1986. "Exact Inference in the Inequality Constrained Normal Linear Regression Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 1(2), pages 127-141, April.
    5. Chib, Siddhartha & Greenberg, Edward, 1996. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometric Theory, Cambridge University Press, vol. 12(3), pages 409-431, August.
    6. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    7. Marc Nerlove, 1956. "Estimates of the Elasticities of Supply of Selected Agricultural Commodities," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 38(2), pages 496-509.
    8. Jean-Marie Dufour & Jan F. Kiviet, 1998. "Exact Inference Methods for First-Order Autoregressive Distributed Lag Models," Econometrica, Econometric Society, vol. 66(1), pages 79-104, January.
    9. Lutkepohl, Helmut, 1981. "A model for non-negative and non-positive distributed lag functions," Journal of Econometrics, Elsevier, vol. 16(2), pages 211-219, June.
    10. John Geweke, 1999. "Using Simulation Methods for Bayesian Econometric Models," Computing in Economics and Finance 1999 832, Society for Computational Economics.
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