Abstract
This paper reviews the matrix programminglanguage Ox from the viewpoint of an econometrician/statistician.We focus on scientific programming using Ox and discussexamples of possible interest to econometricians and statisticians, such as random number generation, maximum likelihood estimation, andMonte Carlo simulation. Ox is a remarkable matrix programming language which is well suited to research and teaching in econometrics and statistics.
Similar content being viewed by others
References
Beran, J. (1994). Statistics for Long-Memory Processes. Chapman and Hall, New York.
Cribari-Neto, F. (1997). Econometric programming environments: GAUSS, Ox and S-PLUS. Journal of Applied Econometrics, 12, 77-89.
Cribari-Neto, F. (1999). C for econometricians. Computational Economics, 14, 135-149.
Cribari-Neto, F. and Zarkos, S.G. (1999). R: Yet another econometric programming environment. Journal of Applied Econometrics, 14, 319-329.
Doornik, J.A. (1999). Object-oriented Matrix Programming Using Ox, 3rd edition. Timberlake Consultants, London.
Doornik, J.A. (2001). Object-oriented Matrix Programming Using Ox, 4th edition. Timberlake Consultants, London.
Doornik, J.A., Draisma, G. and Ooms, M. (1998). Introduction to Ox. Timberlake Consultants, London.
Fisher, N.I. (1993). Statistical Analysis of Circular Data. Cambridge University Press, New York.
Griffiths, W.E., Hill, R.C. and Judge, G.G. (1993). Learning and Practicing Econometrics. Wiley, New York.
Kendrick, D.A. and Amman, H.M. (1999). Programming languages in economics. Computational Economics, 14, 151-181.
Keng, T. and Orzag, J.M. (1997). Ox: an object-oriented matrix language. The Economic Journal, 107, 256-259.
Kerninghan, B.W. and Ritchie, D.M. (1988). The C Programming Language, 2nd edition. Prentice Hall, Englewood Cliffs.
Koopman, S.J., Shephard, N. and Doornik, J.A. (1999). Statistical algorithms for models in state space form using SsfPack 2.2 (with discussion), Econometrics Journal, 2, 113-166.
Kusters, U. and Steffen, J.P. (1996). Matrix programming languages for statistical computing: A detailed comparison of GAUSS, MATLAB, and Ox. Discussion Paper No. 75, Catholic University of Eichstatt.
Laurent, S. and Peters, J.P. (2001). A tutorial for G@RCH 2.0, an Ox package for estimating and forecasting various ARCH models. Working Paper, University of Liége.
Laurent, S. and Peters, J.P. (2001). G@RCH 2.0: an Ox package for estimating and forecasting various ARCH models, Proceedings of the 8th International Conference ‘Forecasting Financial Markets’, London.
MacKinnon, J. (1999). The Linux operating system: Debian GNU/Linux. Journal of Applied Econometrics, 14, 443-452.
Mittelhammer, R.C., Judge, G.G. and Miller, D.J. (2000). Econometric Foundations. New York: Cambridge University Press.
Ooms, M. (1999). Review of SsfPack 2.2: Statistical algorithms for models in state space. Econometrics Journal, 2, 161-166.
Podivinsky, J.M. (1999). Ox 2.10: Beast of burden or object of desire? Journal of Economic Surveys, 13, 491-502.
Portnoy, S. and Koenker, R. (1997). The Gaussian hare and the Laplacean tortoise: Computability of squared-error vs. absolute error. Statistical Science, 12, 279-300.
Press, W.H., Teukolsky, S.A., Vetterling, W.T. and Flannery, B.P. (1992). Numerical Recipes in C: The Art of Scientific Computing, 2nd edition. Cambridge University Press, New York.
Stallman, R.M. (1999). Using and Porting the GNU Compiler Collection. The Free Software Foundation, Boston.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Cribari-Neto, F., Zarkos, S.G. Econometric and Statistical Computing Using Ox. Computational Economics 21, 277–295 (2003). https://doi.org/10.1023/A:1023902027800
Issue Date:
DOI: https://doi.org/10.1023/A:1023902027800