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Post-simulation analysis of Monte Carlo experiments: interpreting Pesaran's (1974) study of non-nested hypothesis test statistics

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  • Neil R. Ericsson

Abstract

"Monte Carlo experimentation in econometrics helps 'solve' deterministic problems by simulating stochastic analogues in which the analytical unknowns are reformulated as parameters to be estimated." (Hendry (1980) With that in mind, Monte Carlo studies may be divided operationally into three phases: design, simulation, and post-simulation analysis. This paper provides a guide to the last of those three, post-simulation analysis, given the design and simulation of a Monte Carlo study, and uses Pesaran's (1974) study of statistics for testing non-nested hypotheses to illustrate the techniques described. A statistic is derived for testing for significant deviations between the asymptotic and (observed) finite sample properties. Further, that statistic provides the basis for analyzing discrepancies between the finite sample and asymptotic properties using response surfaces. The results for Pesaran's study indicate the value of asymptotic theory in interpreting finite sample properties and certain limitations for doing so. Finally, a method is proposed for adjusting the finite sample sizes of different test statistics so that comparisons of their power may be made. Extensions to other finite sample properties are indicated.

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Bibliographic Info

Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series International Finance Discussion Papers with number 276.

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Date of creation: 1986
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Handle: RePEc:fip:fedgif:276

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Keywords: Econometrics ; Statistics;

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  1. Mizon, Grayham E & Hendry, David F, 1980. "An Empirical Application and Monte Carlo Analysis of Tests of Dynamic Specification," Review of Economic Studies, Wiley Blackwell, vol. 47(1), pages 21-45, January.
  2. Maasoumi, Esfandiar & Phillips, Peter C. B., 1982. "On the behavior of inconsistent instrumental variable estimators," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 183-201, August.
  3. Sargan, J D, 1976. "Econometric Estimators and the Edgeworth Approximation," Econometrica, Econometric Society, vol. 44(3), pages 421-48, May.
  4. Hendry, D F, 1973. "On Asymptotic Theory and Finite Sample Experiments," Economica, London School of Economics and Political Science, vol. 40(158), pages 210-17, May.
  5. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
  6. Robert F. Engle & David F. Hendry & David Trumble, 1985. "Small-Sample Properties of ARCH Estimators and Tests," Canadian Journal of Economics, Canadian Economics Association, vol. 18(1), pages 66-93, February.
  7. Aneuryn-Evans, Gwyn & Deaton, Angus, 1980. "Testing Linear versus Logarithmic Regression Models," Review of Economic Studies, Wiley Blackwell, vol. 47(1), pages 275-91, January.
  8. Hendry, David F. & Srba, Frank, 1980. "Autoreg: a computer program library for dynamic econometric models with autoregressive errors," Journal of Econometrics, Elsevier, vol. 12(1), pages 85-102, January.
  9. Ericsson, Neil R, 1983. "Asymptotic Properties of Instrumental Variables Statistics for Testing Non-Nested Hypotheses," Review of Economic Studies, Wiley Blackwell, vol. 50(2), pages 287-304, April.
  10. Godfrey, L. G. & Pesaran, M. H., 1983. "Tests of non-nested regression models: Small sample adjustments and Monte Carlo evidence," Journal of Econometrics, Elsevier, vol. 21(1), pages 133-154, January.
  11. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
  12. Hendry, David F., 1982. "A reply to Professors Maasoumi and Phillips," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 203-213, August.
  13. Sargan, J D, 1980. "Some Approximations to the Distribution of Econometric Criteria Which are Asymptotically Distributed as Chi-Squared," Econometrica, Econometric Society, vol. 48(5), pages 1107-38, July.
  14. Sowey, Eric R., 1973. "A classified bibliography of Monte Carlo studies in econometrics," Journal of Econometrics, Elsevier, vol. 1(4), pages 377-395, December.
  15. Pesaran, M H, 1982. "Comparison of Local Power of Alternative Tests of Non-Nested Regression Models," Econometrica, Econometric Society, vol. 50(5), pages 1287-1305, September.
  16. Godfrey, Leslie G, 1978. "Testing against General Autoregressive and Moving Average Error Models When the Regressors Include Lagged Dependent Variables," Econometrica, Econometric Society, vol. 46(6), pages 1293-1301, November.
  17. Nicholls, D F & Pagan, A R, 1983. "Heteroscedasticity in Models with Lagged Dependent Variables," Econometrica, Econometric Society, vol. 51(4), pages 1233-42, July.
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Cited by:
  1. Neil R. Ericsson, 1987. "Monte Carlo methodology and the finite sample properties of statistics for testing nested and non-nested hypotheses," International Finance Discussion Papers 317, Board of Governors of the Federal Reserve System (U.S.).
  2. Harvey, David I. & van Dijk, Dick, 2006. "Sample size, lag order and critical values of seasonal unit root tests," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2734-2751, June.
  3. Claudio Lupi, . "Unit Root CADF Testing with R," Journal of Statistical Software, American Statistical Association, vol. 32(i02).
  4. McAleer, Michael, 1995. "The significance of testing empirical non-nested models," Journal of Econometrics, Elsevier, vol. 67(1), pages 149-171, May.

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