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Estimating Distributed Lags in Short Panels with an Application to the Specification of Depreciation Patterns and Capital Stock Constructs

  • Ariel Pakes
  • Zvi Griliches

In this paper, we investigate the problem of estimating distributed lags in short panels. Estimates of the parameter of distributed lag relationships based on single time-series of observations have been usually rather imprecise. The promise of panel data is in the N repetitions of the time-series that it contains which should allow one to estimate the identified lag parameters with greater precision. On the other hand, panels tend to track their observations only over a relatively short time interval. Thus, some assumptions will have to be made on the contribution of the unobserved presample x's to the current values of y before any lag parameters can be identified from such data. In this paper we suggest two such assumptions; both of which are, at least in part, testable, and outline appropriate estimation techniques. The first places reasonable restrictions on the relationship between the presample and in sample x's while the second imposes conventional functional form constraints on the lag coefficients. The paper concludes with an example which investigates empirically how to construct a "capital stock" for profit or rate of return regressions.

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File URL: http://www.nber.org/papers/w0933.pdf
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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 0933.

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Date of creation: Jul 1982
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Publication status: published as Pakes, Ariel and Zvi Griliches. "Estimating Distributed Lags in Short Panels with an Application to the Specification of Depreciation Patterns and Capital Stock Constructs." Review of Economic Studies, No. LI(2), No. 165, (April 1984), pp. 243-262.
Handle: RePEc:nbr:nberwo:0933
Note: PR
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  1. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
  2. Lawrence R. Klein, 1957. "The Estimation of Distributed Lags," Cowles Foundation Discussion Papers 34, Cowles Foundation for Research in Economics, Yale University.
  3. Geweke, John F & Meese, Richard, 1981. "Estimating Regression Models of Finite but Unknown Order," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 22(1), pages 55-70, February.
  4. White, Halbert, 1980. "Using Least Squares to Approximate Unknown Regression Functions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 21(1), pages 149-70, February.
  5. Maddala, G S & Rao, A S, 1971. "Maximum Likelihood Estimation of Solow's and Jorgenson's Distributed Lag Models," The Review of Economics and Statistics, MIT Press, vol. 53(1), pages 80-88, February.
  6. MaCurdy, Thomas E., 1982. "The use of time series processes to model the error structure of earnings in a longitudinal data analysis," Journal of Econometrics, Elsevier, vol. 18(1), pages 83-114, January.
  7. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
  8. Martin Feldstein & Lawrence Summers, 1977. "Is the Rate of Profit Falling?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 8(1), pages 211-228.
  9. Nerlove, Marc, 1972. "Lags in Economic Behavior," Econometrica, Econometric Society, vol. 40(2), pages 221-51, March.
  10. White, Halbert, 1980. "Nonlinear Regression on Cross-Section Data," Econometrica, Econometric Society, vol. 48(3), pages 721-46, April.
  11. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
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