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Generalized Method of Moments

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Advanced Methods for Modeling Markets

Part of the book series: International Series in Quantitative Marketing ((ISQM))

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Abstract

The generalized method of moments (GMM) is a conceptually simple and flexible estimation method that has come to play an increasingly prominent role in empirical research in economics over the last 30 years. Application of GMM requires the availability of so-called moment equations or moment conditions. There should be at least as many moment equations as there are parameters to be estimated. If this condition is satisfied (plus some regularity conditions), application of GMM is in principle straightforward and delivers estimators for the parameters that are consistent and asymptotically normal. If desired, the estimators can in addition be made asymptotically efficient given the available moment equations, that is, have the lowest achievable variance or highest precision asymptotically.

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References

  • Albuquerque, P., Bronnenberg, B.J.: Estimating demand heterogeneity using aggregated data: an application to the frozen pizza category. Market. Sci. 28, 356–372 (2009)

    Article  Google Scholar 

  • Anderson, T.W.: Origins of the limited information maximum likelihood and two-stage least squares estimators. J. Econ. 127, 1–16 (2005)

    Article  Google Scholar 

  • Anderson, T.W., Hsiao, C.: Estimation of dynamic models with error components. J. Am. Stat. Assoc. 77, 598–606 (1981)

    Article  Google Scholar 

  • Anderson, T.W., Rubin, H.: Estimator of the parameters of a single equation in a complete system of stochastic equations. Ann. Math. Stat. 20, 46–63 (1949)

    Article  Google Scholar 

  • Anderson, T.W., Rubin, H.: The asymptotic properties of estimates of the parameters of a single equation in a complete system of stochastic equations. Ann. Math. Stat. 21, 570–582 (1950)

    Article  Google Scholar 

  • Angrist, J.D., Krueger, A.B.: Does compulsory school attendance affect schooling and earnings? Q. J. Econ. 106, 979–1014 (1991)

    Article  Google Scholar 

  • Angrist, J.D., Pischke, J.-S.: Mostly Harmless Econometrics. Princeton University Press, Princeton (2009)

    Google Scholar 

  • Angrist, J., Graddy, K., Imbens, G.: The interpretation of instrumental variables estimators in simultaneous equations models with an application to the demand for fish. Rev. Econ. Stud. 67, 499–527 (2000)

    Article  Google Scholar 

  • Arellano, M., Bond, S.: Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev. Econ. Stud. 58, 277–297 (1991)

    Article  Google Scholar 

  • Ashworth, S., Clinton, J.D.: Does advertising exposure affect turnout? Q. J. Polit. Sci. 2, 27–41 (2007)

    Article  Google Scholar 

  • Bass, F.M., Parsons, L.J.: Simultaneous equation regression analysis of sales and advertising. Appl. Econ. 1, 103–124 (1969)

    Article  Google Scholar 

  • Bekker, P.A.: Alternative approximations to the distributions of instrumental variable estimators. Econometrica 62, 657–681 (1994)

    Article  Google Scholar 

  • Bekker, P.A., Crudu, F.: Jackknife instrumental variable estimation with heteroskedasticity. J. Econ. 185, 332–342 (2015)

    Article  Google Scholar 

  • Berndt, E.R.: The Practice of Econometrics. Addison-Wesley, Reading, MA (1991)

    Google Scholar 

  • Berry, S.: Estimating discrete-choice models of product differentiation. RAND J. Econ. 25, 242–262 (1994)

    Article  Google Scholar 

  • Berry, S., Levinsohn, J., Pakes, A.: Automobile prices in market equilibrium. Econometrica 63, 841–890 (1995)

    Article  Google Scholar 

  • Bound, J., Jaeger, D., Baker, R.: Problems with instrumental variables estimation when the correlation between the instruments and the endogenous variables is weak. J. Am. Stat. Assoc. 90, 443–450 (1995)

    Google Scholar 

  • Breusch, T., Qian, H., Schmidt, P., Wyhowski, D.J.: Redundancy of moment conditions. J. Econ. 91, 89–111 (1999)

    Article  Google Scholar 

  • Cameron, A.C., Trivedi, P.K.: Microeconometrics. Cambridge University Press, New York (2005)

    Book  Google Scholar 

  • Chaussé, P.: Computing generalized method of moments and generalized empirical likelihood with R. J. Stat. Softw. 34(11), 1–35 (2010)

    Article  Google Scholar 

  • Cragg, J.: More efficient estimation in the presence of heteroskedasticity of unknown form. Econometrica 51, 751–763 (1983)

    Article  Google Scholar 

  • Doran, H.E., Schmidt, P.: GMM estimators with improved finite sample properties using principal components of the weighting matrix, with an application to the dynamic panel data model. J. Econ. 133, 387–409 (2006)

    Article  Google Scholar 

  • Germann, F., Ebbes, P., Grewal, R.: The chief marketing officer matters! J. Market. 79(3), 1–22 (2015)

    Article  Google Scholar 

  • Gu, B., Park, J., Konana, P.: The impact of external word-of-mouth sources on retailer sales of high-involvement products. Inf. Syst. Res. 23, 182–196 (2012)

    Article  Google Scholar 

  • Hall, A.R.: Generalized Method of Moments. Oxford University Press, Oxford (2005)

    Google Scholar 

  • Hall, A.R.: Generalized method of moments. In: Hashimzade, N., Thronton, M.A. (eds.) Handbook of Research Methods and Applications in Empirical Macroeconomics, pp. 313–333. Edward Elgar, Cheltenham (2013)

    Chapter  Google Scholar 

  • Hall, A.R.: Econometricians have their moments: GMM at 32. Econ. Rec. 91(Suppl. S1), 1–24 (2015)

    Google Scholar 

  • Hansen, L.P.: Large sample properties of generalized method of moments estimators. Econometrica 50, 1029–1054 (1982)

    Article  Google Scholar 

  • Hansen, L.P., Singleton, K.J.: Generalized instrumental variables estimation of nonlinear rational expectations models. Econometrica 50, 1269–1286 (1982)

    Article  Google Scholar 

  • Hansen, L.P., Heaton, J., Yaron, A.: Finite-sample properties of some alternative GMM estimators. J. Bus. Econ. Stat. 14, 262–280 (1996)

    Google Scholar 

  • Hausman, J.A., Taylor, W.E.: Panel data and unobservable individual effects. Econometrica 49, 1377–1398 (1981)

    Article  Google Scholar 

  • Hayashi, F.: Econometrics. Princeton University Press, Princeton (2000)

    Google Scholar 

  • Heckman, J.J.: Sample selection bias as a specification error. Econometrica 47, 153–161 (1979)

    Article  Google Scholar 

  • Jagannathan, R., Skoulakis, G., Wang, Z.: Generalized method of moments: applications in finance. J. Bus. Econ. Stat. 20, 470–481 (2002)

    Article  Google Scholar 

  • Jaibi, M.R., Ten Raa, M.H.: An asymptotic foundation for logit models. Reg. Sci. Urban Econ. 28, 75–90 (1998)

    Article  Google Scholar 

  • Kleibergen, F., Paap, R.: Generalized reduced rank tests using the singular value decomposition. J. Econ. 133, 97–126 (2006)

    Article  Google Scholar 

  • Meijer, E., Wansbeek, T.J.: The sample selection model from a method of moments perspective. Econ. Rev. 26, 25–51 (2007)

    Article  Google Scholar 

  • Murray, M.P.: Avoiding invalid instruments and coping with weak instruments. J. Econ. Perspect. 20, 111–132 (2006)

    Article  Google Scholar 

  • Narayanan, S., Nair, H.S.: Estimating causal installed-base effects: a bias-correction approach. J. Market. Res. 50, 70–94 (2013)

    Article  Google Scholar 

  • Narayanan, S., Manchanda, P., Chintagunta, P.K.: Temporal differences in the role of marketing communication in new product categories. J. Market. Res. 42, 278–290 (2005)

    Article  Google Scholar 

  • Nevo, A.: A practitioner’s guide to estimation of random coefficients logit models of demand. J. Econ. Manag. Strateg. 9, 513–548 (2000)

    Article  Google Scholar 

  • Newey, W.K.: A method of moments interpretation of sequential estimators. Econ. Lett. 14, 201–206 (1984)

    Article  Google Scholar 

  • Newey, W.K., McFadden, D.: Large sample estimation and hypothesis testing. In: Griliches, Z., Intriligator, M. (eds.) Handbook of Econometrics, vol. 4. North Holland, Amsterdam (1994)

    Google Scholar 

  • Newey, W.K., West, K.D.: A simple positive-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55, 703–708 (1987)

    Article  Google Scholar 

  • Park, S., Gupta, S.: Comparison of SML and GMM estimators for the random coefficient logit model using aggregate data. Empir. Econ. 43, 1353–1372 (2012)

    Article  Google Scholar 

  • Pearson, K.: Contributions to the mathematical theory of evolution. Philos. Trans. R. Soc. Lond. Ser. A 185, 71–110 (1894)

    Article  Google Scholar 

  • Qian, H., Schmidt, P.: Improved instrumental variables and generalized method of moments estimators. J. Econ. 91, 145–169 (1999)

    Article  Google Scholar 

  • Reiersøl, O.: Confluence analysis by means of lag moments and other methods of confluence analysis. Econometrica 9, 1–24 (1941)

    Article  Google Scholar 

  • Rossi, P.E.: Even the rich can make themselves poor: a critical examination of IV methods in marketing applications. Market. Sci. 33, 655–672 (2014)

    Article  Google Scholar 

  • Stock, J.H., Trebbi, F.: Who invented instrumental variable regression? J. Econ. Perspect. 17, 177–194 (2003)

    Article  Google Scholar 

  • Theil, H.: Repeated Least Squares Applied to Complete Equation Systems. Central Planning Bureau, The Hague (1953)

    Google Scholar 

  • Theil, H., Finke, R.: The distance from the equator as an instrumental variable. Econ. Lett. 13, 357–360 (1983)

    Article  Google Scholar 

  • Vitorino, M.A.: Understanding the effect of advertising on stock returns and firm value: theory and evidence from a structural model. Manag. Sci. 60, 227–245 (2014)

    Article  Google Scholar 

  • Wang, L., Hsiao, C.: Method of moments and identifiability of semi-parametric nonlinear errors-in-variables models. J. Econ. 165, 30–44 (2011)

    Article  Google Scholar 

  • Wansbeek, T.J.: Correcting for heteroskedasticity of unspecified form – problem 04.1.2. Econ. Theory 20, 224 (2004)

    Google Scholar 

  • Wansbeek, T.J., Meijer, E.: Measurement Error and Latent Variables in Econometrics. North-Holland, Amsterdam (2000)

    Google Scholar 

  • Wheaton, B., Muthén, B., Alwin, D., Summers, G.: Assessing reliability and stability in panel models. In: Heise, D.R. (ed.) Sociological Methodology 1977, pp. 84–136. Jossey-Bass, San Francisco (1977)

    Google Scholar 

  • White, H.L.: A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48, 817–838 (1980)

    Article  Google Scholar 

  • Windmeijer, F.: A finite sample correction for the variance of linear efficient two-step GMM estimators. J. Econ. 126, 25–51 (2005)

    Article  Google Scholar 

  • Wooldridge, J.M.: Econometric Analysis of Cross Section and Panel Data, 2nd edn. MIT Press, Cambridge (2010)

    Google Scholar 

  • Wright, P.G.: The Tariff on Animal and Vegetable Oils. Macmillan, New York (1928)

    Google Scholar 

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Acknowledgements

The author is grateful to Erik Meijer for his ever incisive and stimulating comments. He benefited greatly from comments and suggestions by Jochem de Bresser, Marnik Dekimpe, Pim Heijnen, Peter Leeflang, Laura Spierdijk, Roberto Wessels and the students in my Applied Econometrics course.

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Authors and Affiliations

  1. Department of Economics, Econometrics & Finance, Faculty of Economics and Business, University of Groningen, Groningen, The Netherlands

    Tom J. Wansbeek

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  1. Tom J. Wansbeek
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Correspondence to Tom J. Wansbeek .

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Editors and Affiliations

  1. Department of Marketing, University of Groningen, Groningen, The Netherlands

    Peter S. H. Leeflang

  2. Department of Marketing, University of Groningen, Groningen, The Netherlands

    Jaap E. Wieringa

  3. Department of Marketing, University of Groningen, Groningen, The Netherlands

    Tammo H.A Bijmolt

  4. Department of Marketing, D’Amore-McKim School of Business, Northeastern University, Boston, USA

    Koen H. Pauwels

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Wansbeek, T.J. (2017). Generalized Method of Moments. In: Leeflang, P., Wieringa, J., Bijmolt, T., Pauwels, K. (eds) Advanced Methods for Modeling Markets. International Series in Quantitative Marketing. Springer, Cham. https://doi.org/10.1007/978-3-319-53469-5_15

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