Advertisement

Empirical Economics

, Volume 31, Issue 3, pp 631–643 | Cite as

Empirical size and power of some diagnostic tests applied to a distributed lag model

  • Dimitris Hatzinikolaou
  • Athanassios Stavrakoudis
original paper

Abstract

We produce Monte Carlo evidence on the size and power of the RESET, a heteroscedasticity test, and a test for autocorrelation applied to realistic distributed-lag models. We find that the autocorrelation test has the correct size and high power to detect not only autocorrelation (given a correct model), but also the erroneous omission of several lags of an explanatory variable, whereas the RESET and heteroscedasticity tests are oversized in the presence of positive disturbance autocorrelation, especially when the regressors are also positively autocorrelated, and have no power to detect such misspecification errors. In large samples, size distortion may be avoided by using autocorrelation-robust methods.

Keywords

Size Power Simulation RESET Diagnostic tests 

JEL Classification

C15 C22 C52 

References

  1. Godfrey LG (1988) Misspecification tests in econometrics: the Lagrange multiplier principle and other approaches. Cambridge University Press, New YorkGoogle Scholar
  2. Godfrey LG, McAleer M, McKenzie CR (1988) Variable addition and Lagrange multiplier tests for linear and logarithmic regression models. Rev Econ Stat 70:492–503CrossRefGoogle Scholar
  3. Godfrey LG, Orme CD (1994) The sensitivity of some general checks to omitted variables in the linear model. Int Econ Rev 35:489–506zbMATHCrossRefGoogle Scholar
  4. Johnston J (1972) Econometric methods. 2nd Ed. McGraw-Hill, TokyoGoogle Scholar
  5. Kiviet JF (1986) On the rigour of some misspecification tests for modelling dynamic relationships. Rev of Econ Stud 53:241–261zbMATHCrossRefGoogle Scholar
  6. Krämer W, Kiviet J, Breitung J (1990) The null distribution of the F-test in the linear regression model with autocorrelated disturbances. Statistica 50:503–509MathSciNetGoogle Scholar
  7. Krämer W, Sonnberger H, Maurer J, Havlik P (1985) Diagnostic checking in practice. Rev Econ Stat 67:118–123CrossRefGoogle Scholar
  8. Leung SF, Yu S (2001) The sensitivity of the RESET tests to disturbance autocorrelation in regression residuals. Empir Econ 26:721–726CrossRefGoogle Scholar
  9. Pagan AR, Hall AD (1983) Diagnostic tests as residual analysis. Econom Rev 2:159–218zbMATHMathSciNetGoogle Scholar
  10. Porter RD, Kashyap AK (1984) Autocorrelation and the sensitivity of RESET. Econ Lett 14:229–233CrossRefGoogle Scholar
  11. Ramsey JB (1969) Tests for specification errors in classical linear least-squares regression analysis. J R Stat Soc, B 31:350–371zbMATHMathSciNetGoogle Scholar
  12. Thursby JG (1979) Alternative specification error tests: A comparative study. J Am Stat Assoc 74:222–225CrossRefGoogle Scholar
  13. Thursby JG (1989) A comparison of several specification error tests for a general alternative. Int Econ Rev 30:217–230zbMATHMathSciNetCrossRefGoogle Scholar
  14. Thursby JG, Schmidt P (1977) Some properties of tests for specification error in a linear regression model. J Am Stat Assoc 72:635–641zbMATHCrossRefGoogle Scholar
  15. Wooldridge JM (2003) Introductory econometrics: a modern approach. 2nd Ed., South-Western, ThomsonGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Dimitris Hatzinikolaou
    • 1
  • Athanassios Stavrakoudis
    • 1
  1. 1.Department of EconomicsUniversity of IoanninaIoanninaGreece

Personalised recommendations