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An introduction to maximum entropy and minimum cross-entropy estimation using Stata

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  • Martin Wittenberg

    (University of Cape Town)

Abstract

Maximum entropy and minimum cross-entropy estimation are applica- ble when faced with ill-posed estimation problems. I introduce a Stata command that estimates a probability distribution using a maximum entropy or minimum cross-entropy criterion. I show how this command can be used to calibrate survey data to various population totals. Copyright 2010 by StataCorp LP.

Suggested Citation

  • Martin Wittenberg, 2010. "An introduction to maximum entropy and minimum cross-entropy estimation using Stata," Stata Journal, StataCorp LP, vol. 10(3), pages 315-330, September.
    • Handle: RePEc:tsj:stataj:v:10:y:2010:i:3:p:315-330
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    References listed on IDEAS

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    1. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
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    2. Maya Goldman & Ihsaan Bassier & Joshua Budlender & Lindi Mzankomo & Ingrid Woolard & Murray Leibbrandt, 2021. "Simulation of options to replace the special COVID-19 Social Relief of Distress grant and close the poverty gap at the food poverty line," WIDER Working Paper Series wp-2021-165, World Institute for Development Economic Research (UNU-WIDER).
    3. Osorio Rodarte, Israel, 2016. "Modeling Distributional Effects of Macroeconomic Shocks: Increasing female participation and human capital in Turkey," Conference papers 332777, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    4. Amy Thornton & Martin Wittenberg, 2022. "Reweighting the OHS and GHS to improve data quality: Representativeness, household counts, and small households," South African Journal of Economics, Economic Society of South Africa, vol. 90(4), pages 513-534, December.
    5. Lakatos,Csilla & Maliszewska,Maryla & Osorio-Rodarte,Israel & Go,Delfin Sia, 2016. "China's slowdown and rebalancing: potential growth and poverty impacts on Sub-Saharan Africa," Policy Research Working Paper Series 7666, The World Bank.
    6. Edward J Balistreri & Maryla Maliszewska & Israel Osorio-Rodarte & David G Tarr & Hidemichi Yonezawa, 2018. "Poverty, Welfare and Income Distribution Implications of Reducing Trade Costs Through Deep Integration in Eastern and Southern Africa," Journal of African Economies, Centre for the Study of African Economies, vol. 27(2), pages 172-200.
    7. Balistreri, Edward J. & Maliszewska, Maryla & Osorio-Rodarte, Israel & Tarr, David G. & Yonezawa, Hidemichi, 2016. "Poverty and Shared Prosperity Implications of Deep Integration in Eastern and Southern Africa," Conference papers 332681, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    8. Jean Drèze & Anmol Somanchi, 2024. "Weighty Evidence? Poverty Estimation with Missing Data," Studies in Microeconomics, , vol. 12(1), pages 93-106, April.
    9. Jesse Tack & David Ubilava, 2013. "The effect of El Niño Southern Oscillation on U.S. corn production and downside risk," Climatic Change, Springer, vol. 121(4), pages 689-700, December.
    10. Nicola Branson & Martin Wittenberg, 2014. "Reweighting South African National Household Survey Data to Create a Consistent Series Over Time: A Cross-Entropy Estimation Approach," South African Journal of Economics, Economic Society of South Africa, vol. 82(1), pages 19-38, March.
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    13. Anne Konrad & Jan Pablo Burgard & Ralf Münnich, 2021. "A Two‐level GREG Estimator for Consistent Estimation in Household Surveys," International Statistical Review, International Statistical Institute, vol. 89(3), pages 635-656, December.

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