Bayesian inference of a smooth transition dynamic almost ideal model of food demand in the US
A dynamic ‘smooth transition’ Almost Ideal model is estimated for food consumption in the US. A Metropolis-Hastings algorithm is employed to map the posterior distributions and rejection sampling is used to evaluate and impose curvature restrictions at more than one point in the sample. The findings support the contention of structural change of a ‘smooth transition’ nature. Notably, the income food elasticity of demand becomes smaller through time, and the own price elasticities for food and non food become more elastic.
|Date of creation:||2006|
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- Philippe J. Deschamps, 2003. "Time-varying intercepts and equilibrium analysis: an extension of the dynamic almost ideal demand model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(2), pages 209-236.
- Marcus Chambers & K. Ben Nowman, 1997. "Forecasting with the almost ideal demand system: evidence from some alternative dynamic specifications," Applied Economics, Taylor & Francis Journals, vol. 29(7), pages 935-943.
- Gordon Anderson & Richard Blundell, 1983. "Testing Restrictions in a Flexible Dynamic Demand System: An Application to Consumers' Expenditure in Canada," Review of Economic Studies, Oxford University Press, vol. 50(3), pages 397-410.
- Kelvin. G. Balcombe, 2004. "Retesting symmetry and homogeneity in a cointegrated demand system with bootstrapping: The case of meat demand in Greece," Empirical Economics, Springer, vol. 29(2), pages 451-462, 05.
- Albert, James H & Chib, Siddhartha, 1993. "Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 1-15, January.
- Deschamps, Philippe J., 2000. "Exact small-sample inference in stationary, fully regular, dynamic demand models," Journal of Econometrics, Elsevier, vol. 97(1), pages 51-91, July.
- Ng, S., 1995.
"Testing for Homogeneity in Demand Systems when the Regressors Are Non-Stationary,"
Cahiers de recherche
9516, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- Ng, Serena, 1995. "Testing for Homogeneity in Demand Systems When the Regressors Are Nonstationary," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 147-163, April-Jun.
- Ng, S., 1995. "Testing for Homogeneity in Demand Systems when the Regressors Are Non-Stationary," Cahiers de recherche 9516, Universite de Montreal, Departement de sciences economiques.
- Geweke, John, 1988. "Antithetic acceleration of Monte Carlo integration in Bayesian inference," Journal of Econometrics, Elsevier, vol. 38(1-2), pages 73-89.
- Chib, Siddhartha & Greenberg, Edward, 1995. "Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models," Journal of Econometrics, Elsevier, vol. 68(2), pages 339-360, August.
- Bauwens, Luc & Lubrano, Michel & Richard, Jean-Francois, 2000. "Bayesian Inference in Dynamic Econometric Models," OUP Catalogue, Oxford University Press, number 9780198773139, December.
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