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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsBibliography
Abramowitz, M., and I. Stegun. 1964. Handbook of mathematical functions. Washington, DC: National Bureau of Standards.
Balestra, P., and M. Nerlove. 1966. Pooling cross-section and time-series data in the estimation of a dynamic model. Econometrica 34: 585–612.
Belsley, D. 1974. Estimation of system of simultaneous equations and computational specifications of GREMLIN. Annals of Economic and Social Measurement 3: 551–614.
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.
Davis, P.J., and P. Rabinovitz. 1984. Methods of numerical integration. New York: Academic.
Dennis, J.E., and R.B. Schnabel. 1984. Unconstrained optimization and nonlinear equations. Englewood Cliffs: Prentice-Hall.
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.
Efron, B. 1982. The jackknife, the bootstrap, and other resampling plans, CBMS-NSF monographs No. 38. Philadelphia: SIAM.
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.
Geweke, J. 1988. Antithetic acceleration of Monte Carlo integration in Bayesian inference. Journal of Econometrics 38: 73–90.
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.
Goldfeld, S., and R. Quandt. 1972. Nonlinear methods in econometrics. Amsterdam: North-Holland.
Golub, G.H. 1969. Matrix decompositions and statistical calculations. In Statistical computation, ed. R.C. Milton and J.A. Milder. New York: Academic.
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.
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.
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.
Manski, C. 1975. Maximum score estimation of the stochastic utility model of choice. Journal of Econometrics 3: 205–228.
McFadden, D. 1989. A method of simulated moments for estimation of multinomial discrete response models. Econometrica 57: 995–1026.
Nagurney, A. 1996. Parallel computation. In Handbook of computational economics, vol. 1, ed. H. Amman, D. Kendrik, and J. Rust. Amsterdam: North-Holland.
Nelder, J.A., and R. Meade. 1965. A simplex method for function minimization. Computer Journal 7: 308–313.
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.
Quandt, R. 1983. Computational problems and methods. In Handbook of econometrics, vol. 1, ed. Z. Griliches and M. Intriligator. Amsterdam: North-Holland.
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.
Author information
Authors and Affiliations
Editor information
Copyright information
© 2018 Macmillan Publishers Ltd.
About this entry
Cite this entry
Hajivassiliou, V.A. (2018). Computational Methods in Econometrics. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_2725
Download citation
DOI: https://doi.org/10.1057/978-1-349-95189-5_2725
Published:
Publisher Name: Palgrave Macmillan, London
Print ISBN: 978-1-349-95188-8
Online ISBN: 978-1-349-95189-5
eBook Packages: Economics and FinanceReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences