Balcombe, Kelvin and Bailey, Alastair (2006): Bayesian inference of a smooth transition dynamic almost ideal model of food demand in the US.
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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.
|Item Type:||MPRA Paper|
|Original Title:||Bayesian inference of a smooth transition dynamic almost ideal model of food demand in the US|
|Subjects:||C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General|
|Depositing User:||Kelvin Balcombe|
|Date Deposited:||16. Sep 2009 11:21|
|Last Modified:||13. Feb 2013 00:35|
Albert J.H. and Chib S. (1993) Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts. Journal of Business & Economic Statistics, January 1993, Vol 11. No 1.
Anderson G. and R. Blundell (1984). Testing Restrictions in a Flexible Dynamic System: An Application to Consumers Expenditure in the U.K. Economic Journal 94: 35-44.
Attfield C (1997) Estimating a Cointegrating Demand System. European Economic Review 41:61-73
Balcombe, K. S. Davidova, and Morrison J.A. (1999). Consumer Behaviour in a Country in Transition with a Strongly Contracting Economy, Journal of Agricultural Economics, 50, 36-47.
Balcombe K. (2003). Retesting Symmetry and Homogeneity in a Cointegrated Demand System with Bootstrapping: the Case of Meat Demand in Greece. Empirical Economics.
Bauwens, L. Lubrano, M. and J.F. Richard. (1999). Bayesian Inference in Dynamic Econometric Models. Advanced Texts in Econometrics. Oxford University Press.
Buse A (1994) Linearised Almost Ideal Demand System. American Journal of Agricultural Economics 76:4, 781-793
Buse A (1998) Homogeneity in the Almost Ideal Demand System. American Journal of Agricultural Economics 80:1, 208-220
Chalfant, J.A., Gray R.S. and White K.J. (1991). Evaluating Prior Belief in a Demand System: The Case of Meat Demand in Canada, American Journal of Agricultural Economics, 73, 476-490
Casella G. and George E.I. (1992) Explaining the Gibbs Sampler. The American Statistician, August 1992, 46, 3, pp.167-174.
Chambers M.J. and Nowman K.B., (1997) Forecasting with the almost ideal demand system: evidence from some alternative dynamic specifications Applied Economics 29, pp.935-943. 18
Chib S. and Greenberg E. (1995a) Heirarchical Analysis of SUR Models with Extensions to Correlated Serial Errors and Time Varying Parameters, Journal of Econometrics, 68: 339 360.
Chib C. and E. Greenberg, (1995b). Understanding the Metropolis-Hastings Algorithm. The American Statistician, November, 1995, 49. No 4.: 327- 335
Deaton, A.S. and J . Muellbauer (1980) An almost ideal demand system. American Economic Review 70, pp312-326.
Deschamps P.J. (2000) Exact small sample interence in stationary fully regular, dynamic demand models. Journal of Econometrics, 97, 51-91.
Deschamps P.J. (2003) Time Varying Intercepts and Equilibrium Analysis: An Extension of the Dynamic Almost Ideal Demand Model, Journal of Applied Econometrics (forthcoming).
De Crombrugge D., Palm F.C. and J.P. Urbain, ‘Statistical Demand Function for Food in the USA and the Netherlands, Journal of Applied Econometrics, Vol 12. 615-645.
Duffy M., (2002) On the estimation of an advertising-augmented cointegrating demand system. Economic Modelling, 20, pp.181-206.
Geweke J. (1988) Antithetic Accleration of Monte Carlo Integration in Bayesian Inference. Journal of Econometrics, 38: 73-89.
Geweke, J. (1989). Bayesian Inference In econometricModels UsingMonte Carlo Integration. Econometrica. 57: 1317-39.
Geweke J. (1997) Using Simulation Methods for Bayesian Econometric Models: Inference, Development and Communication. Paper preparedfor the Australsian meeing so the Econometric Socitey, Melbourne
Griffiths, W.E., O’Donnell, C.J. and A. Tan Cruz (2000) ”Imposing regularity conditions on a system of cost and cost-share equations: a Bayesian approach”, Australian Journal of Agricultural Economics, 44(1):107-127
Hendry (1993) Econometrics Alchemy of Science? Essays in EconometricMethodology. Blackwell, Oxford UK. 19
Lau, L.J. (1978), Testing and imposing monotonicity, convexity and quasiconcavity constraints’, in Fuss M. and McFadden, D. Production Economics: A Dual Approach to Theory and Applications, Vol 1, North Holland, Amsterdam.
Morrison J.A. K.Balcombe, A.Bailey, S Klonaris, and G.Rapsomanikis (2003). Expenditure on different cataegoris of meat in Greece: the influence of changing tastes, Agricultural Economics, 28: 139-150.
Ng S (1995). Testing for Homogeneity in Demand Systems when the Regressors are Non-Stationary. Journal of Applied Econometrics, 10, pp.147-163.
Terasvirta, T. (1994). Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of Applied Econometrics, 7, S119-S136
Tiffin A. and Tiffin R. (1999). Estimates of Food Demand Elasticities for Great Britain: 1972-1994, Journal of Agricultural Economics, 50, 140-147.
Pesaran MH Shin Y (2002) Long-run Structural Modelling. Econometrics Reviews 21:49-87 20