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

Author

Listed:
  • 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.

Suggested Citation

  • Joshua D. Angrist & Kathryn Graddy & Guido W. Imbens, 1995. "Non-Parametric Demand Analysis with an Application to the Demand for Fish," NBER Technical Working Papers 0178, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberte:0178
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    References listed on IDEAS

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    1. Imbens, G. & Angrist, J.D., 1992. "Average Causal Response with Variable Treatment Intensity," Harvard Institute of Economic Research Working Papers 1611, Harvard - Institute of Economic Research.
    2. Manski, C.F., 1992. "Identification Problems in the Social Sciences," Working papers 9217, Wisconsin Madison - Social Systems.
    3. Roehrig, Charles S, 1988. "Conditions for Identification in Nonparametric and Parametic Models," Econometrica, Econometric Society, vol. 56(2), pages 433-447, March.
    4. David Card, 1994. "Earnings, Schooling, and Ability Revisited," Working Papers 710, Princeton University, Department of Economics, Industrial Relations Section..
    5. Angrist, Joshua D, 1990. "Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records," American Economic Review, American Economic Association, vol. 80(3), pages 313-336, June.
    6. Newey, Whitney K, 1990. "Efficient Instrumental Variables Estimation of Nonlinear Models," Econometrica, Econometric Society, vol. 58(4), pages 809-837, July.
    7. Hausman, Jerry A & Newey, Whitney K, 1995. "Nonparametric Estimation of Exact Consumers Surplus and Deadweight Loss," Econometrica, Econometric Society, vol. 63(6), pages 1445-1476, November.
    8. Amemiya, Takeshi, 1974. "The nonlinear two-stage least-squares estimator," Journal of Econometrics, Elsevier, vol. 2(2), pages 105-110, July.
    9. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    10. Whitney K. Newey & James L. Powell & Francis Vella, 1999. "Nonparametric Estimation of Triangular Simultaneous Equations Models," Econometrica, Econometric Society, vol. 67(3), pages 565-604, May.
    11. Kathryn Graddy, 1995. "Testing for Imperfect Competition at the Fulton Fish Market," RAND Journal of Economics, The RAND Corporation, vol. 26(1), pages 75-92, Spring.
    12. Powell, James L & Stock, James H & Stoker, Thomas M, 1989. "Semiparametric Estimation of Index Coefficients," Econometrica, Econometric Society, vol. 57(6), pages 1403-1430, November.
    13. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
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    Citations

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    Cited by:

    1. Joshua D. Angrist & Alan B. Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 69-85, Fall.
    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 2003:18, Stockholm University, Department of Economics.
    3. 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 0262, National Bureau of Economic Research, Inc.
    4. Corts, Kenneth S., 1998. "Conduct parameters and the measurement of market power," Journal of Econometrics, Elsevier, vol. 88(2), pages 227-250, November.
    5. Heckman, James J. & Vytlacil, Edward J., 2000. "The relationship between treatment parameters within a latent variable framework," Economics Letters, Elsevier, vol. 66(1), pages 33-39, January.
    6. Card, David, 2001. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," Econometrica, Econometric Society, vol. 69(5), pages 1127-1160, September.
    7. Daron Acemoglu & Joshua Angrist, 1999. "How Large are the Social Returns to Education? Evidence from Compulsory Schooling Laws," NBER Working Papers 7444, National Bureau of Economic Research, Inc.
    8. Behrman, Jere R., 1996. "Measuring the effectiveness of schooling policies in developing countries: Revisiting issues of methodology," Economics of Education Review, Elsevier, vol. 15(4), pages 345-364, October.

    More about this item

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • L66 - Industrial Organization - - Industry Studies: Manufacturing - - - Food; Beverages; Cosmetics; Tobacco

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