Basic Statistical Concepts

  • Badi H. Baltagi


One chapter cannot possibly review what one learned in one or two pre-requisite courses in statistics. This is an econometrics book, and it is imperative that the student have taken at least one solid course in statistics. The concepts of a random variable, whether discrete or continuous, and the associated probability function or probability density function (p.d.f.) are assumed known. Similarly, the reader should know the following statistical terms: Cumulative distribution function, marginal, conditional and joint p.d.f.’s. The reader should be comfortable with computing mathematical expectations, and familiar with the concepts of independence, Bayes Theorem and several continuous and discrete probability distributions. These distributions include: the Bernoulli, Binomial, Poisson, Geometric, Uniform, Normal, Gamma, Chi-squared (χ2), Exponential, Beta, t and F distributions.


Maximum Likelihood Estimation Central Limit Theorem Critical Region Unbiased Estimator Moment Generate Function 
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  1. More detailed treatment of the material in this chapter may be found in:Google Scholar
  2. Amemiya, T. (1994), Introduction to Statistics and Econometrics (Harvard University Press: Cambridge).Google Scholar
  3. Baltagi, B.H. (1994), “The Wald, LR, and LM Inequality,” Econometric Theory, Problem 94.1.2, 10: 223–224.CrossRefGoogle Scholar
  4. Baltagi, B.H. (2000), “Conflict Among Critera for Testing Hypotheses: Examples from Non-Normal Distributions,” Econometric Theory, Problem 00.2.4, 16: 288.CrossRefGoogle Scholar
  5. Bera A.K. and G. Permaratne (2001), “General Hypothesis Testing,” Chapter 2 in Baltagi, B.H. (ed.), A Companion to Theoretical Econometrics (Blackwell: Massachusetts).Google Scholar
  6. Berndt, E.R. and N.E. Savin (1977), “Conflict Among Criteria for Testing Hypotheses in the Multivariate Linear Regression Model,” Econometrica, 45: 1263–1278.CrossRefGoogle Scholar
  7. Breusch, T.S. (1979), “Conflict Among Criteria for Testing Hypotheses: Extensions and Comments,” Econometrica, 47: 203–207.CrossRefGoogle Scholar
  8. Buse, A. (1982), “The Likelihood Ratio, Wald, and Lagrange Multiplier Tests: An Expository Note,” The American Statistician, 36:153–157.Google Scholar
  9. DeGroot, M.H. (1986), Probability and Statistics (Addison-Wesley: Mass.).Google Scholar
  10. Freedman, D., R. Pisani, R. Purves and A. Adhikari (1991), Statistics (Norton: New York).Google Scholar
  11. Freund, J.E. (1992), Mathematical Statistics (Prentice-Hall: New Jersey).Google Scholar
  12. Hogg, R.V. and A.T. Craig (1995), Introduction to Mathematical Statistics (Prentice Hall: New Jersey).Google Scholar
  13. Jollife, I.T. (1995), “Sample Sizes and the Central Limit Theorem: The Poisson Distribution as an Illustration,” The American Statistician, 49: 269.Google Scholar
  14. Kennedy, P. (1992), A Guide to Econometrics (MIT Press: Cambridge).Google Scholar
  15. Mood, A.M., F.A. Graybill and D.C. Boes (1974), Introduction to the Theory of Statistics (McGraw-Hill: New York).Google Scholar
  16. Spanos, A. (1986), Statistical Foundations of Econometric Modelling (Cambridge University Press: Cambridge).CrossRefGoogle Scholar
  17. Ullah, A. and V. Zinde-Walsh (1984), “On the Robustness of LM, LR and W Tests in Regression Models,” Econometrica, 52: 1055–1065.CrossRefGoogle Scholar
  18. Zellner, A. (1971), An Introduction to Bayesian Inference in Econometrics (Wiley: New York).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Badi H. Baltagi
    • 1
  1. 1.Department of EconomicsTexas A&M UniversityCollege StationUSA

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