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Likelihood-based inference for regular functions with fractional polynomial approximations

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  • Geweke, John
  • Petrella, Lea

Abstract

This paper shows that regular fractional polynomials can approximate regular cost, production and utility functions and their first two derivatives on closed compact subsets of the strictly positive orthant of Euclidean space arbitrarily well. These functions therefore can provide reliable approximations to demand functions and other economically relevant characteristics of tastes and technology. Using canonical cost function data, it shows that full Bayesian inference for these approximations can be implemented using standard Markov chain Monte Carlo methods.

Suggested Citation

  • Geweke, John & Petrella, Lea, 2014. "Likelihood-based inference for regular functions with fractional polynomial approximations," Journal of Econometrics, Elsevier, vol. 183(1), pages 22-30.
  • Handle: RePEc:eee:econom:v:183:y:2014:i:1:p:22-30
    DOI: 10.1016/j.jeconom.2014.06.007
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    References listed on IDEAS

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    1. Barnett, William A. & Geweke, John & Wolfe, Michael, 1991. "Seminonparametric Bayesian estimation of the asymptotically ideal production model," Journal of Econometrics, Elsevier, vol. 49(1-2), pages 5-50.
    2. Barnett, William A. & Serletis, Apostolos, 2008. "Consumer preferences and demand systems," Journal of Econometrics, Elsevier, vol. 147(2), pages 210-224, December.
    3. Gallant, A. Ronald, 1981. "On the bias in flexible functional forms and an essentially unbiased form : The fourier flexible form," Journal of Econometrics, Elsevier, vol. 15(2), pages 211-245, February.
    4. Barnett, William A. & Serletis, Apostolos, 2008. "Measuring Consumer Preferences and Estimating Demand Systems," MPRA Paper 12318, University Library of Munich, Germany.
    5. Terrell, Dek, 1996. "Incorporating Monotonicity and Concavity Conditions in Flexible Functional Forms," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(2), pages 179-194, March-Apr.
    6. Barnett, William A. & Jonas, Andrew B., 1983. "The Muntz-Szatz demand system : An application of a globally well behaved series expansion," Economics Letters, Elsevier, vol. 11(4), pages 337-342.
    7. W. Sauerbrei & P. Royston, 1999. "Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(1), pages 71-94.
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    1. repec:taf:jnlasa:v:112:y:2017:i:519:p:948-965 is not listed on IDEAS

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