Empirical Economics

, 34:537 | Cite as

GMM estimation of a structural demand model for yogurt and the effects of the introduction of new brands

  • Marina Di GiacomoEmail author
Original Paper


The demand structure for yogurt is assumed to be properly described by a one level nested logit model that is applied to aggregate market data. Given the presence of endogenous regressors, suitably lagged endogenous variables (Arellano and Bover in J Econom 68:29–51, 1995; Blundell and Bond in J Econom 87:115–143, 1998) are proposed as instrumental variables. The validity of this set of instruments is discussed and price elasticities and marginal costs are recovered from the demand estimates. Total welfare gains associated to the introduction of two new brands by the same manufacturer are finally computed. Prices and profits decreased and total welfare increased.


Nested logit GMM Yogurt Instrumental variables New products 


  1. Ackerberg D (2001) Empirically distinguishing informative and prestige effects of advertising. RAND J Econ 32:316–333CrossRefGoogle Scholar
  2. Ackerberg D, Rysman M (2005) Unobserved product differentiation in discrete choice models: estimating price elasticities and welfare effects. RAND J Econ 36:771–788Google Scholar
  3. Ackerberg D, Benkard L, Berry S, Pakes A (2005) Econometric tools for analyzing market outcomes. In: Heckman JJ (ed) Handbook of econometrics. JAI Elsevier Science, Amsterdam (forthcoming)Google Scholar
  4. Ahn SC, Schmidt P (1995) Efficient estimation of models for dynamic panel data. J Econom 68:5–27CrossRefGoogle Scholar
  5. Anderson S, De Palma A (1992) Multiproduct firms: a nested logit approach. J Ind Econ 40:261–276CrossRefGoogle Scholar
  6. Anderson S, De Palma A (2001) Product diversity in asymmetric oligopoly: is the quality of consumer goods too low?. J Ind Econ 49:113–135Google Scholar
  7. Anderson S, De Palma A, Thisse JF (1992) Discrete choice theory of product differentiation. Cambridge, MITGoogle Scholar
  8. Arellano M, Bond S (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Stud 58:277–297CrossRefGoogle Scholar
  9. Arellano M, Bond S (1998) Dynamic panel data estimation using DPD98 for Gauss: a guide for users. Institute for Fiscal Studies. Available from Scholar
  10. Arellano M, Bover O (1995) Another look at the instrumental variables estimation of error components models. J Econom 68:29–51CrossRefGoogle Scholar
  11. Arrow K (1962) Economic welfare and the allocation of resources for invention. In: Nelson R (eds.) The rate and direction on inventive activity. Princeton University Press, PrincetonGoogle Scholar
  12. Berry S (1994) Estimating discrete choice models of product differentiation. RAND J Econ 25:242–262CrossRefGoogle Scholar
  13. Berry S, Pakes A (2001) Additional information for: comments on alternative models of demand for automobiles by Charlotte Wojcik. Econ lett 74:43–51CrossRefGoogle Scholar
  14. Berry S, Pakes A (2005) The pure characteristics demand model. Int Econ Rev (forthcoming)Google Scholar
  15. Berry S, Levinsohn J, Pakes A (1995) Automobile prices in market equilibrium. Econometrica 63:841–890CrossRefGoogle Scholar
  16. Biørn E (2000) Panel data with measurement errors: instrumental variables and GMM procedures combining levels and differences. Econom Rev 19:391–424CrossRefGoogle Scholar
  17. Blundell R, Bond S (1998) Initial conditions and moment restrictions in dynamic panel data models. J Econom 87:115–143CrossRefGoogle Scholar
  18. Blundell R, Bond S, Windmeijer F (2000) Estimation in dynamic panel data models: improving on the performance of the standard GMM estimator. In: Baltagi B (ed) Advances in econometrics. Nonstationary panels, panel cointegration and dynamic panels, vol 15. JAI Elsevier Science, Amsterdam, pp 53–91CrossRefGoogle Scholar
  19. Bond S (2002) Dynamic panel data models: a guide to micro data methods and practice. Port Econ J 1:141–161CrossRefGoogle Scholar
  20. Bresnahan T (1989) Empirical studies of industries with market power. In: Schmalensee R, Willig R (eds.) Handbook of industrial organization. Elsevier, Amsterdam, pp. 1011–1057Google Scholar
  21. Bresnahan T, Gordon R (1997) The economics of new goods. In: Studies in income and wealth, vol 58. NBER, ChicagoGoogle Scholar
  22. Bresnahan T, Schmalensee R (1987) The empirical renaissance in industrial economics: an overview. J Ind Econ 35:371–378CrossRefGoogle Scholar
  23. Bresnahan T, Stern S, Trajtenberg M (1997) Market segmentation and the sources of rents from innovation: personal computers in the late 1980’s. RAND J Econ 28:17–44CrossRefGoogle Scholar
  24. Caplin A, Nalebuff B (1991) Aggregation and imperfect competition: on the existence of equilibrium. Econometrica 59:25–59CrossRefGoogle Scholar
  25. Cardell N (1989) Variance components structures for the extreme value and logistic distributions with application to models of heterogeneity. Econom Theory 13:185–213CrossRefGoogle Scholar
  26. Chintagunta P, Kyriazidou E, Perktold J (2001) Panel data analysis of household brand choices. J Econom 103:111–153CrossRefGoogle Scholar
  27. Dubé JP (2005) Product differentiation and mergers in the carbonated soft drink industry. J Econ Manage Strategy 14:879–904CrossRefGoogle Scholar
  28. Hansen LP (1982) Large sample properties of generalized method of moments estimators. Econometrica 50:1029–1054CrossRefGoogle Scholar
  29. Hausman JA (1997) Valuation of new goods under perfect and imperfect competition. In: Gordon R (eds). The economics of new goods. Studies in income and wealth, vol 58. NBER, Chicago, pp 209–248Google Scholar
  30. Hausman JA, Leonard GK (2002) The competitive effects of a new product introduction: a case study. J Ind Econ L3:237–263Google Scholar
  31. Hausman JA, Leonard GK, Zona JD (1994) Competitive analysis with differentiated products. Ann Econ Stat 34:159–180Google Scholar
  32. Hendel I (1999) Estimating multiple discrete choice models: an application to computerization returns. Rev Econ Stud 66:423–446CrossRefGoogle Scholar
  33. Ivaldi M, Verboven F (2005) Quantifying the effects from horizontal mergers in European competition policy. Int J Ind Organ 23:669–691CrossRefGoogle Scholar
  34. Lancaster K (1979) Variety, equity and efficiency: product variety in an industrial society. Columbia University Press, New YorkGoogle Scholar
  35. Mas-Colell A, Whinston MD, Green JR (1995) Microeconomic theory. Oxford University Press, New YorkGoogle Scholar
  36. McFadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (eds) Frontiers of econometrics. Academic, New York, pp. 105–142Google Scholar
  37. McFadden D (1981) Econometric models of probabilistic choice. In: Manski C, McFadden D (eds). Structural analysis of discrete data with econometric applications. MIT, Cambridge, pp. 198–272Google Scholar
  38. Nevo A (2000a) Mergers with differentiated products: the case of the ready-to-eat cereal industry. RAND J Econ 31:395–421CrossRefGoogle Scholar
  39. Nevo A (2000b) A Practitioner’s guide to estimation of random coefficients logit models of demand. J Econ Manage Strategy 9:513–548CrossRefGoogle Scholar
  40. Nevo A (2001) Measuring market power in the ready-to-eat cereal industry. Econometrica 69:307–342CrossRefGoogle Scholar
  41. Osservatorio del Mercato del Latte (2003) Annuario del latte. Università Cattolica Milano, MilanoGoogle Scholar
  42. Petrin A (2002) Quantifying the benefits of new products: the case of the Minivan. J Polit Econ 110:705–729CrossRefGoogle Scholar
  43. Pinkse J, Slade ME (2002) Mergers, brand competition and the price of a pint. Eur Econ Rev 48:617–643CrossRefGoogle Scholar
  44. Rodman D (2005) xtabond2: Stata module to extend xtabond dynamic panel data estimator. Center for Global Development, Washington. Scholar
  45. Schmalensee R (1978) Entry deterrence in the ready to eat breakfast cereal industry. Bell J Econ 9:305–327CrossRefGoogle Scholar
  46. Staiger D, Stock JH (1997) Instrumental variables regression with weak instruments. Econometrica 65:557–586CrossRefGoogle Scholar
  47. Stock JH, Yogo M (2002) Testing for weak instruments in linear IV regression. NBER, Technical working paper no. 284Google Scholar
  48. Trajtenberg M (1989) The welfare analysis of product innovations, with an application to computed tomography scanners. J Polit Econ 97:444–479CrossRefGoogle Scholar
  49. Verboven F (1996) International price discrimination in the European car market. RAND J Econ 27:240–268CrossRefGoogle Scholar
  50. Villas-Boas JM, Winer RS (1999) Endogeneity in brand choice models. Manage Sci 45:1324–1338CrossRefGoogle Scholar
  51. Windmeijer F (2005) A finite sample correction for the variance of linear efficient two-step GMM estimators. J Econom 126:25–51CrossRefGoogle Scholar
  52. Wojcik C (2000) Alternative models of demand for automobiles. Econ Lett 68:113–118CrossRefGoogle Scholar

Copyright information

© Springer Verlag 2007

Authors and Affiliations

  1. 1.Facoltà di EconomiaUniversity of TurinTorinoItaly

Personalised recommendations