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Non-Parametric Demand Analysis with an Application to the Demand for Fish

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  • Joshua D. Angrist
  • Kathryn Graddy
  • Guido W. Imbens

Abstract

Instrumental variables (IV) estimation of a demand equation using time series data is shown to produce a weighted average derivative of heterogeneous potential demand functions. This result adapts recent work on the causal interpretation of two-stage least squares estimates to the simultaneous equations context and generalizes earlier research on average derivative estimation to models with endogenous regressors. The paper also shows how to compute the weights underlying IV estimates of average derivatives in a simultaneous equations model. These ideas are illustrated using data from the Fulton Fish market in New York City to estimate an average elasticity of wholesale demand for fresh fish. The weighting function underlying IV estimates of the demand equation is graphed and interpreted. The empirical example illustrates the essentially local and context-specific nature of instrumental variables estimates of structural parameters in simultaneous equations models.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0178.

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Date of creation: Apr 1995
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Publication status: published as Review of Economic Studies, Vol. 67, No. 3, pp. 499-527, July 2000.
Handle: RePEc:nbr:nberte:0178

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  1. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, Elsevier, vol. 1(1), pages 19-46, January.
  2. Whitney K. Newey & James L. Powell & Francis Vella, 1999. "Nonparametric Estimation of Triangular Simultaneous Equations Models," Econometrica, Econometric Society, Econometric Society, vol. 67(3), pages 565-604, May.
  3. Hausman, Jerry A & Newey, Whitney K, 1995. "Nonparametric Estimation of Exact Consumers Surplus and Deadweight Loss," Econometrica, Econometric Society, Econometric Society, vol. 63(6), pages 1445-76, November.
  4. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, Econometric Society, vol. 62(2), pages 467-75, March.
  5. Joshua Angrist, 1989. "Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records," Working Papers, Princeton University, Department of Economics, Industrial Relations Section. 631, Princeton University, Department of Economics, Industrial Relations Section..
  6. Roehrig, Charles S, 1988. "Conditions for Identification in Nonparametric and Parametic Models," Econometrica, Econometric Society, Econometric Society, vol. 56(2), pages 433-47, March.
  7. David Card, 1994. "Earnings, Schooling, and Ability Revisited," NBER Working Papers 4832, National Bureau of Economic Research, Inc.
  8. repec:fth:prinin:331 is not listed on IDEAS
  9. Powell, James L & Stock, James H & Stoker, Thomas M, 1989. "Semiparametric Estimation of Index Coefficients," Econometrica, Econometric Society, Econometric Society, vol. 57(6), pages 1403-30, November.
  10. Imbens, G. & Angrist, J.D., 1992. "Average Causal Response with Variable Treatment Intensity," Harvard Institute of Economic Research Working Papers, Harvard - Institute of Economic Research 1611, Harvard - Institute of Economic Research.
  11. Newey, W.K., 1989. "Efficient Instrumental Variables Estimation Of Nonlinear Models," Papers, Princeton, Department of Economics - Econometric Research Program 341, Princeton, Department of Economics - Econometric Research Program.
  12. Graddy, K., 1993. "Testing for Imperfect Competition at the Fulton Fish Market," Papers, Princeton, Department of Economics - Financial Research Center 137, Princeton, Department of Economics - Financial Research Center.
  13. Amemiya, Takeshi, 1974. "The nonlinear two-stage least-squares estimator," Journal of Econometrics, Elsevier, Elsevier, vol. 2(2), pages 105-110, July.
  14. repec:att:wimass:9217 is not listed on IDEAS
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Cited by:
  1. Behrman, Jere R., 1996. "Measuring the effectiveness of schooling policies in developing countries: Revisiting issues of methodology," Economics of Education Review, Elsevier, Elsevier, vol. 15(4), pages 345-364, October.
  2. Pettersson Lidbom, Per, 2003. "Does the Size of the Legislature Affect the Size of Government? Evidence from a Natural Experiment," Research Papers in Economics, Stockholm University, Department of Economics 2003:18, Stockholm University, Department of Economics.
  3. Joshua Angrist & Alan B. Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," NBER Working Papers 8456, National Bureau of Economic Research, Inc.
  4. David Card, 2000. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," NBER Working Papers 7769, National Bureau of Economic Research, Inc.
  5. Daron Acemoglu & Joshua Angrist, 1999. "How Large are the Social Returns to Education? Evidence from Compulsory Schooling Laws," Working papers, Massachusetts Institute of Technology (MIT), Department of Economics 99-30, Massachusetts Institute of Technology (MIT), Department of Economics.
  6. Arild Aakvik & James J. Heckman & Edward J. Vytlacil, 2000. "Treatment Effects for Discrete Outcomes when Responses to Treatment Vary Among Observationally Identical Persons: An Application to Norwegian ..," NBER Technical Working Papers, National Bureau of Economic Research, Inc 0262, National Bureau of Economic Research, Inc.
  7. Corts, Kenneth S., 1998. "Conduct parameters and the measurement of market power," Journal of Econometrics, Elsevier, Elsevier, vol. 88(2), pages 227-250, November.
  8. Heckman, James J. & Vytlacil, Edward J., 2000. "The relationship between treatment parameters within a latent variable framework," Economics Letters, Elsevier, Elsevier, vol. 66(1), pages 33-39, January.

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