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Measuring consumption smoothing in CEX data

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  • Gervais, Martin
  • Klein, Paul

Abstract

A new method of measuring the degree of consumption smoothing is proposed and implemented using data from the Consumer Expenditure Survey. The structure of this Survey is such that estimators previously used in the literature are inconsistent, simply because income is measured annually and consumption is measured quarterly. An AR(1) structure is imposed on the income process to obtain a proxy for quarterly income through a projection on annual income. By construction, this proxy gives rise to a measurement error which is orthogonal to the proxy itself--as opposed to the unobserved regressor--leading to a consistent estimator. Our estimates are contrasted with the output of two estimators used in the literature. This comparison shows that while the first (OLS) estimator tends to overstate the degree of risk sharing, the second (IV) estimator grossly understates it.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Monetary Economics.

Volume (Year): 57 (2010)
Issue (Month): 8 (November)
Pages: 988-999

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Handle: RePEc:eee:moneco:v:57:y:2010:i:8:p:988-999

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Web page: http://www.elsevier.com/locate/inca/505566

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Citations

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Cited by:
  1. Giorgio E. Primiceri & Thijs van Rens, 2006. "Heterogeneous life-cycle profiles, income risk and consumption inequality," Economics Working Papers 945, Department of Economics and Business, Universitat Pompeu Fabra, revised Aug 2008.
  2. Heathcote, Jonathan & Perri, Fabrizio & Violante, Giovanni L, 2009. "Unequal We Stand: An Empirical Analysis of Economic Inequality in the United States, 1967-2006," CEPR Discussion Papers 7538, C.E.P.R. Discussion Papers.
  3. Telyukova, Irina A., 2012. "Household Need for Liquidity and the Credit Card Debt Puzzle," University of California at San Diego, Economics Working Paper Series qt0ww2c04z, Department of Economics, UC San Diego.
  4. Laura Blow & Valérie Lechene & Peter Levell, 2014. "Using the CE to Model Household Demand," NBER Chapters, in: Improving the Measurement of Consumer Expenditures National Bureau of Economic Research, Inc.
  5. Tommaso Ciarli, 2012. "Structural interactions and long run growth: An application of Experimental Design to Agent Based Models," Papers on Economics and Evolution 2012-06, Philipps University Marburg, Department of Geography.
  6. Kathleen W. Johnson & Geng Li, 2007. "Do high debt payments hinder household consumption smoothing?," Finance and Economics Discussion Series 2007-52, Board of Governors of the Federal Reserve System (U.S.).
  7. Klein, Paul & Telyukova, Irina A., 2013. "Measuring high-frequency income risk from low-frequency data," Journal of Economic Dynamics and Control, Elsevier, vol. 37(3), pages 535-542.
  8. Fabrizio Perri & Dirk Krueger, 2008. "How does Household Consumption Respond to Income Shocks? Evidence and Theory," 2008 Meeting Papers 910, Society for Economic Dynamics.
  9. Laurence Ales & Maziero Pricila, . "Accounting for Private Information," GSIA Working Papers 2010-E58, Carnegie Mellon University, Tepper School of Business.
  10. Fabrizio Perri & Dirk Krueger, 2009. "How does Household Consumption Respond to Income Shocks?," 2009 Meeting Papers 14, Society for Economic Dynamics.
  11. Tommaso Ciarli & Andre' Lorentz & Maria Savona & Marco Valente, 2012. "The role of technology, organisation, and demand in growth and income distribution," LEM Papers Series 2012/06, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  12. Islam, TM Tonmoy, 2013. "Childhood neighborhood conditions and the persistence of adult income," Regional Science and Urban Economics, Elsevier, vol. 43(4), pages 684-693.

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