C for Econometricians

被引：3

作者：

Cribari-Neto, Francisco ^{[1
]}

机构：

[1] Departamento de Estatística, Universidade Federal de Pernambuco, Cidade Universitária, Recife/PE, 50740-540, Brazil

来源：

Computational Economics | 1999年 / 14卷 / 1-2期

关键词：

C programming language; Fortran; matrix programming languages; Monte Carlo simulation;

D O I：

10.1023/A:1008643511645

中图分类号：

学科分类号：

摘要：

Fortran has long been the mother-tongue of the scientific community. This note looks at the C programming language from the viewpoint of numerical computing with the emphasis placed on econometric applications. C can be used as a replacement for other lower level programming languages, and also a replacement or as a complement to higher level matrix programming languages such as GAUSS, MATLAB, Ox, and S-PLUS.

引用

页码：135 / 149

页数：14

共 19 条

[1]

Cary J.R.(1997)Comparison of C++ and Fortran 90 for object-oriented scientific programming Computer Physics Communications 105 20-36

[2]

Shasharina S.G.(1997)Econometric programming environments: GAUSS, Ox and S-PLUS Journal of Applied Econometrics 12 77-89

[3]

Cummings J.C.(1995)An improved Lagrange multiplier test for heteroskedasticity Communications in Statistics, Simulation and Computation 24 31-44

[4]

Rynders J.V.W.(1997)MATLAB as an econometric programming environment Journal of Applied Econometrics 12 735-744

[5]

Cribari-Neto F.(1996)Object-oriented econometrics: matrix programming in C++ using GCC and newmat Journal of Applied Econometrics 11 199-209

[6]

Cribari-Neto F.(1998)On bootstrap and analytical bias corrections Economics Letters 58 7-15

[7]

Ferrari S.L.P.(1994)Is C++ fast enough for scientific computing? Computers in Physics 8 690-694

[8]

Cribari-Neto F.(1996)Beating the abstraction penalty in C++ using expression templates Computers in Physics 10 552-557

[9]

Jensen M.J.(1988)A size correction for the Lagrange multiplier test for heteroskedasticity Journal of Econometrics 38 375-386

[10]

Eddelbüttel D.(1995)On the accuracy of statistical distributions in GAUSS Computational Statistics and Data Analysis 20 699-702

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