Testing econometric software

Palgrave Handbook of Econometrics: Volume 2: Applied Econometrics

Volumn , Issue , 2009, Pages 1293-1320

  McCullough, B D ^a  

^a DREXEL UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77952420928     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1057/9780230244405_28     Document Type: Chapter

References (71)

1. Altman, M. and M. McDonald. (2002) Choosing reliable statistical software. PS: Political Science and Politics 24(3), 681-8.
2. Anderson, R., W.H. Greene, B.D. McCullough and H.D. Vinod. (2008) The role of data/code archives in the future of economic research. Journal of Economic Methodology 15(1), 99-119.
3. Bai, J. and P. Perron (2003) Computation and analysis of multiple structural change models. Journal of Applied Econometrics 18, 1-22.
4. Bailey, D.H. (1993) Algorithm 719: multiprecision translation and execution of FORTRAN programs. ACM TOMS 19(3), 288-319.
5. Bankhofer, U. and A. Hilbert (1997a) An application of two-mode classification to analyze the statistical software market. In R. Klar and O. Opitz (eds.), Classification and Knowledge Organisation, pp. 567-72. Heidelberg: Springer.
6. Bankhofer, U. and A. Hilbert. (1997b) Statistical software packages for windows: a market survey. Statistical Papers 38, 393-407.
7. Bernanke, B. (2004) Editorial statement. American Economic Review 94(1), 404.
8. Bollerslev, T. (1986) Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31, 307-27.
9. Box, G.E.P. and G. Jenkins (1976) Time Series Analysis: Forecasting and Control. Oakland, Calif.: Holden-Day.
10. Brooks, C., S.P. Burke and G. Persand. (2001) Benchmarks and the accuracy of GARCH model estimation. International Journal of Forecasting 17(1), 45-56.
11. Brooks, C., S.P. Burke and G. Persand. (2003) Multivariate GARCH models: software choice and estimation issues. Journal of Applied Econometrics 18(6), 725-34.
12. Bruno, G. and R. De Bonis (2004) A comparative study of alternative econometric packages with an application to Italian deposit interest rates. Journal of Economic and Social Measurement 29, 271-95.
13. Calzolari, G. and L. Panattoni. (1988) Alternative estimators of FIML covariance matrix: a Monte Carlo study. Econometrica 56(3), 701-14.
14. Choi, H.-S. and N.M. Kiefer. (2005) Software evaluation: EasyReg International. International Journal of Forecasting 21(3), 609-16.
15. Choudhury, A.-H., R. Hubata and R.D. St. Louis. (1999) Understanding time-series regression estimators. American Statistician 53(4), 342-8.
16. Coddington, P.D. (1994) Analysis of random number generators using Monte Carlo simulation. International Journal of Modern Physics C 3, 547-60.
17. Coddington, P.D. (1996) Tests of random number generators using Ising model simulations. International Journal of Modern Physics 7(3), 295-303.
18. Davidson, R. and J.G. MacKinnon (2004) Econometric Theory and Methods. New York: Oxford University Press.
19. Dewald, W.C., J. Thursby and R. Anderson. (1986) Replication in empirical economics: the Journal of Money, Credit and Banking project. American Economic Review 76(4), 587-603.
20. Drukker, D.M. and W. Guan. (2003) Replicating the results in “On efficient estimation with panel data: an empirical comparison and instrumental variables estimators." Journal of Applied Econometrics 18(1), 119-20.
21. Fiorentini G., G. Calzolari and L. Panattoni. (1996) Analytic derivatives and the computation of GARCH estimates. Journal of Applied Econometrics 11(4), 399-417.
22. Goldberg, D. (1991) What every computer scientist should know about floating point arithmetic. ACM Computing Surveys 23(1), 5-48.
23. Gourieroux, C. and A. Montfort (1997) Simulation-based Econometric Methods. New York: Oxford University Press.
24. Granger, C.W.J. and P. Newbold (1977) Forecasting Economic Time Series. Orlando, Flor.: Academic Press.
25. Keeling, K.B. and R.J. Pavur (2004) Numerical accuracy issues in using Excel for simulation studies. In R.G. Ingalls, M.D. Rossetti, J.S. Smith and B.A. Peters (eds.), Proceedings of the 2004 Winter Simulation Conference, Volume 2, pp. 1513-18. Piscataway, NJ: IEEE.
26. Keeling, K.B. and R.J. Pavur. (2007) A comparative study of the reliability of nine statistical software packages. Computational Statistics and Data Analysis 51(8), 3811-31.
27. Kennedy, W. and J. Gentle (1980) Statistical Computing. New York: Marcel-Dekker.
28. Kitchen, A.M., R. Drachenerg and J. Symanzik. (2003) Assessing the reliability of web-based statistical software. Computational Statistics 18(1), 107-22.
29. Knüsel, L. (1989) Computergestützte Berechnung statistischer Verteilungen. München-Wien: Oldenbourg (English version available at http://www.stat.uni-muenchen.de/knuesel/elv).
30. Knuth, D. (1998) The Art of Computer Programming, Volume 2: Seminumerical Algorithms, 3e. Reading, Mass.: Addison-Wesley.
31. L’Ecuyer, P. and R. Simard (2007) TestU01: a C library for empirical testing of random number generators. ACM TOMS 33(4), Article 22.
32. Ling, R.F. (1974) Comparison of several algorithms for computing sample means and variances. Journal of the American Statistical Association 69, 859-66.
33. Longley, J.W. (1967) An appraisal of least-squares programs from the point of view of the user. Journal of the American Statistical Association 62(319), 819-41.
34. Marsaglia, G. (1996) DIEHARD: a battery of tests of randomness, http://stat.fsu.edu/pub/diehard.
35. McCullough, B.D. (1998a) Assessing the reliability of statistical software: part I. American Statistician 52(4), 358-66.
36. McCullough, B.D. (1998b) Algorithm choice for (partial) autocorrelation functions. Journal of Economic and Social Measurement 24(3/4), 265-78.
37. McCullough, B.D. (1999a) Assessing the reliability of statistical software: part II. The American Statistician 53(2), 149-59.
38. McCullough, B.D. (1999b) Econometric software reliability: E-Views, LIMDEP, SHAZAM, and TSP. Journal of Applied Econometrics 14(2), 191-202.
39. McCullough, B.D.. (2000a) Is it safe to assume that software is accurate? International Journal of Forecasting 16(3), 349-57.
40. McCullough, B.D. (2000b) Experience with the StRD: application and interpretation. Computing Science and Statistics 31, 16-21.
41. McCullough, B.D. (2000c) The accuracy of Mathematica 4 as a statistical package. Computational Statistics 15(2), 279-99.
42. McCullough, B.D. (2001) Random number generators. In A. El-Shaarawi and W. Piegorsch (eds.), Encyclopedia of Environmetrics, Volume 3, pp. 1678-81. New York: Wiley.
43. McCullough, B.D. (2004a) Wilkinson’s tests and econometric software. Journal of Economic and Social Measurement 29(1-3), 261-70.
44. McCullough, B.D. (2004b) Some details of nonlinear estimation. In M. Altman, J. Gill and M.P. McDonald (eds.), Numerical Methods in Statistical Computing for the Social Sciences, pp. 199-218. New York: Wiley.
45. McCullough, B.D. (2006) A review of TESTU01. Journal of Applied Econometrics 21(5), 677-82.
46. McCullough, B.D., K.A. McGeary and T.D. Harrison. (2006) Lessons from the JMCB archive. Journal of Money, Credit and Banking 38(4), 1093-107.
47. McCullough, B.D., K.A. McGeary and T.D. Harrison (2008) Do economics journal archives promote replicable research? Canadian Journal of Economics. Forthcoming.
48. McCullough, B.D. and C.G. Renfro. (1999) Benchmarks and software standards: A case study of GARCH procedures. Journal of Economic and Social Measurement 25(2), 59-71.
49. McCullough, B.D. and C.G. Renfro. (2000) Some numerical aspects of nonlinear estimation. Journal of Economic and Social Measurement 26(1), 63-77.
50. McCullough, B.D. and H.D. Vinod. (1999) The numerical reliability of econometric software. Journal of Economic Literature 37(2), 633-55.
51. McCullough, B.D. and H.D. Vinod. (2003a) Verifying the solution from a nonlinear solver: A case study. American Economic Review 93(3), 873-92.
52. McCullough, B.D. and H.D. Vinod. (2003b) Comment: econometrics and software. Journal of Economic Perspectives 17(1), 223-4.
53. McCullough, B.D. and H.D. Vinod. (2004) Verifying the solution from a nonlinear solver: reply. American Economic Review 94(1), 400-3.
54. McCullough, B.D. and B.Wilson. (2005) On the accuracy of statistical procedures in Microsoft Excel 2003. Computational Statistics and Data Analysis 49(4), 1244-52.
55. Newbold, P., C. Agiakloglou and J. Miller. (1994) Adventures with ARIMA software. International Journal of Forecasting 10(4), 573-81.
56. Priestley, M.B. (1981) Spectral Analysis and Time Series. New York: Academic Press.
57. Sall, J. (2002) Comment. American Statistician 56, 160-1.
58. Sawitzki, G. (1994a) Testing numerical reliability in data analysis systems. Computational Statistics and Data Analysis 18(2), 269-85.
59. Sawitzki, G. (1994b) Report on the numerical reliability of data analysis systems. Computational Statistics and Data Analysis (SSN) 18(2), 289-301.
60. Silk, J. (1996) Systems estimation: a comparison of SAS, SHAZAM and TSP. Journal of Applied Econometrics 11(4), 437-50.
61. Stokes, H.H. (2004) On the advantage of using two or more econometric software systems to solve the same problem. Journal of Economic and Social Measurement 29, 307-320.
62. Teyssiere, G. (2005) Structural time series modelling with STAMP 6.02. Journal of Applied Econometrics 20(4), 571-7.
63. Thisted, R.A. (1988) Elements of Statistical Computing. New York: Chapman and Hall.
64. Tirler, G., P. Dalgaard, W.Hörmann and J. Leydold. (2004) An error in the Kinderman-Ramage method and how to fix it. Computational Statistics and Data Analysis 47(3), 433-40.
65. Vinod, H.D. (2000). Review of GAUSS for windows, including its numerical accuracy. Journal of Applied Econometrics 15, 211-22.
66. Wilkinson, L. (1985) Statistics Quiz. Evanston, Ill.: SYSTAT.
67. Yalta, A.T. (2007) The numerical reliability of GAUSS 8.0. American Statistician 61, 262-8.
68. Yalta, A.T. (2008) The reliability of statistical distributions in Microsoft Excel 2007. Computational Statistics and Data Analysis 52(10), 4579-86.
69. Yalta, A.T. and A.Y. Yalta. (2007) GRETL 1.6.0 and its numerical accuracy. Journal of Applied Econometrics 22(4), 849-54.
70. Yalta, A.T. and O. Jenal (2009) On the importance of verifying forecasting results. International Journal of Forecasting. Forthcoming.
71. Zeileis, A. and C. Kleiber (2005) Validating multiple structural change models-a case study. Journal of Applied Econometrics 20, 685-90.