The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Computational Methods in Econometrics

  • Vassilis A. Hajivassiliou
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_2725

Abstract

The computational properties of an econometric method are fundamental determinants of its importance and practical usefulness, in conjunction with the method’s statistical properties. Computational methods in econometrics are advanced through successfully combining ideas and methods in econometric theory, computer science, numerical analysis, and applied mathematics. The leading classes of computational methods particularly useful for econometrics are matrix computation, numerical optimization, sorting, numerical approximation and integration, and computer simulation. A computational approach that holds considerable promise for econometrics is parallel computation, either on a single computer with multiple processors, or on separate computers networked in an intranet or over the internet.

Keywords

Bayesian inference Bootstrap Classical inference Computational methods Generalized least squares Generalized method of moments Importance sampling simulation Jackknife Least absolute deviations Maximum likelihood Numerical integration Optimal control Ordinary least squares Random effects models Simulation-based estimation Stone, J. R. N Markov chain Monte Carlo methods Parallel computation 

JEL Classification

C15 
This is a preview of subscription content, log in to check access.

Bibliography

  1. Abramowitz, M., and I. Stegun. 1964. Handbook of mathematical functions. Washington, DC: National Bureau of Standards.Google Scholar
  2. Balestra, P., and M. Nerlove. 1966. Pooling cross-section and time-series data in the estimation of a dynamic model. Econometrica 34: 585–612.CrossRefGoogle Scholar
  3. Belsley, D. 1974. Estimation of system of simultaneous equations and computational specifications of GREMLIN. Annals of Economic and Social Measurement 3: 551–614.Google Scholar
  4. Berndt, E.K., B.H. Hall, R.E. Hall, and J.A. Hausman. 1974. Estimation and inference in nonlinear structural models. Annals of Economic and Social Measurement 3: 653–666.Google Scholar
  5. Davis, P.J., and P. Rabinovitz. 1984. Methods of numerical integration. New York: Academic.Google Scholar
  6. Dennis, J.E., and R.B. Schnabel. 1984. Unconstrained optimization and nonlinear equations. Englewood Cliffs: Prentice-Hall.Google Scholar
  7. Drud, A. 1977. An optimization code for nonlinear econometric models based on sparse matrix techniques and reduced grades. Annals of Economic and Social Measurement 6: 563–580.Google Scholar
  8. Efron, B. 1982. The jackknife, the bootstrap, and other resampling plans, CBMS-NSF monographs No. 38. Philadelphia: SIAM.CrossRefGoogle Scholar
  9. Fuller, W.A., and G.E. Battese. 1973. Transformations for estimation of linear models with nested-error structure. Journal of the American Statistical Association 68: 626–632.CrossRefGoogle Scholar
  10. Geweke, J. 1988. Antithetic acceleration of Monte Carlo integration in Bayesian inference. Journal of Econometrics 38: 73–90.CrossRefGoogle Scholar
  11. Geweke, J. 1996. Monte Carlo simulation and numerical integration. In Handbook of computational economics, vol. 1, ed. H. Amman, D. Kendrik, and J. Rust. Amsterdam: North-Holland.Google Scholar
  12. Goldfeld, S., and R. Quandt. 1972. Nonlinear methods in econometrics. Amsterdam: North-Holland.Google Scholar
  13. Golub, G.H. 1969. Matrix decompositions and statistical calculations. In Statistical computation, ed. R.C. Milton and J.A. Milder. New York: Academic.Google Scholar
  14. Hajivassiliou, V.A., and P.A. Ruud. 1994. Classical estimation methods using simulation. In Handbook of econometrics, vol. 4, ed. R. Engle and D. McFadden. Amsterdam: North-Holland.Google Scholar
  15. Hajivassiliou, V.A., D.L. McFadden, and P.A. Ruud. 1996. Simulation of multivariate normal rectangle probabilities and derivatives: Theoretical and computational results. Journal of Econometrics 72(1, 2): 85–134.CrossRefGoogle Scholar
  16. Judd, K. 1996. Approximation, perturbation, and projection methods in economic analysis. In Handbook of computational economics, vol. 1, ed. H. Amman, D. Kendrik, and J. Rust. Amsterdam: North-Holland.Google Scholar
  17. Manski, C. 1975. Maximum score estimation of the stochastic utility model of choice. Journal of Econometrics 3: 205–228.CrossRefGoogle Scholar
  18. McFadden, D. 1989. A method of simulated moments for estimation of multinomial discrete response models. Econometrica 57: 995–1026.CrossRefGoogle Scholar
  19. Nagurney, A. 1996. Parallel computation. In Handbook of computational economics, vol. 1, ed. H. Amman, D. Kendrik, and J. Rust. Amsterdam: North-Holland.Google Scholar
  20. Nelder, J.A., and R. Meade. 1965. A simplex method for function minimization. Computer Journal 7: 308–313.CrossRefGoogle Scholar
  21. Press, W.H., B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling. 2001. Numerical recipes in Fortran 77: The art of scientific computing. Cambridge: Cambridge University Press.Google Scholar
  22. Quandt, R. 1983. Computational problems and methods. In Handbook of econometrics, vol. 1, ed. Z. Griliches and M. Intriligator. Amsterdam: North-Holland.Google Scholar
  23. Zellner, A., L. Bauwens, and H. VanDijk. 1988. Bayesian specification analysis and estimation of simultaneous equation models using Monte Carlo methods. Journal of Econometrics 38: 73–90.CrossRefGoogle Scholar

Copyright information

© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Vassilis A. Hajivassiliou
    • 1
  1. 1.