Christopher Dougherty EC220 - Introduction to econometrics (chapter 8) Slideshow: stochastic...

Christopher Dougherty

EC220 - Introduction to econometrics (chapter 8)Slideshow: stochastic regressors and assumptions for model b

Original citation:

Dougherty, C. (2012) EC220 - Introduction to econometrics (chapter 8). [Teaching Resource]

© 2012 The Author

This version available at: http://learningresources.lse.ac.uk/134/

Available in LSE Learning Resources Online: May 2012

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Until now we have assumed that the explanatory variables in a regression model are nonstochastic, that is, that they do not have random components. We have done this to simplify the analysis of the properties of the regression estimators.

ASSUMPTIONS FOR MODEL B

B.1 The model is linear in parameters and correctly specified.

Y = 1 + 2X2 + … + kXk + u

B.2 The values of the regressors are drawn randomly from fixed populations

B.3 There does not exist an exact linear relationship among the regressors

B.4 The disturbance term has zero expectation

STOCHASTIC REGRESSORS AND ASSUMPTIONS FOR MODEL B



Y = 1 + 2X2 + … + kXk + u





We will now progress to Model B and the case of stochastic regressors, where the values of the regressors are assumed to be drawn randomly from defined populations. This is a much more realistic framework for regressions with cross-sectional data.

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Y = 1 + 2X2 + … + kXk + u





We will begin by re-stating the regression model assumptions and we will then review the properties of the modified model.

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Y = 1 + 2X2 + … + kXk + u





Assumption B.1 is the same as A.1.

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Y = 1 + 2X2 + … + kXk + u





The values of the regressors in the observations in the sample are drawn randomly from fixed populations with finite means and finite population variances. In Model A we simply assumed that their values were fixed and given, with no further explanation.

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Y = 1 + 2X2 + … + kXk + u





Note that we do not assume that the regressors are independent of each other. On the contrary, we allow their populations to have joint probability distributions and as a consequence their sample values may be correlated.

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Y = 1 + 2X2 + … + kXk + u





This is the counterpart of Assumption A.2. Note that if there is a constant in the model, the assumption includes a requirement that there be some variation in each of the regressors.

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Y = 1 + 2X2 + … + kXk + u





Strictly speaking, this is a population assumption. However, as a practical matter, it must also be satisfied by the data in a sample. Otherwise there would be exact multicollinearity and it would be impossible to obtain estimates of the parameters of the model.

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Y = 1 + 2X2 + … + kXk + u





Note that if there is a constant in the model, the assumption includes a requirement that there be some variation in each of the regressors.

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Y = 1 + 2X2 + … + kXk + u





This is the same as Assumption A.3.

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B.5 The disturbance term is homoscedastic.

B.6 The values of the disturbance term have independent distributions

B.7 The disturbance term is distributed independently of the regressors

B.8 The disturbance term has a normal distribution


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A new assumption. In Model A, the fact that the regressors had fixed nonstochastic values meant that it was impossible for there to be a distributional relationship between them and the disturbance term. Now that they have random components, the possibility arises.







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We will see that a violation of this assumption has adverse consequences for the properties of the regression coefficients. Note that, as stated, it means that ui is distributed independently of the value of every regressor in every observation, not just observation i.







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In practice with cross-sectional data it is usually reasonable to suppose that ui is distributed independently of Xji' for all i' ≠ i and so the condition reduces to ui being distributed independently of Xji.







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B.7' The disturbance term has zero conditional expectation

E(ui │values of all the regressors in all observations) = 0


The condition can be weakened to the disturbance term having zero expectation conditional on the values of all the regressors in all the observations.

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Again, in practice with cross-sectional data it is usually reasonable to suppose that it is sufficient to condition on the values of the regressors in observation i only.





B.7' The disturbance term has zero conditional expectation

E(ui │values of all the regressors in all observations) = 0


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As with Model A, we need this assumption when we perform tests.







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Copyright Christopher Dougherty 2011.

These slideshows may be downloaded by anyone, anywhere for personal use.Subject to respect for copyright and, where appropriate, attribution, they may be used as a resource for teaching an econometrics course. There is no need to refer to the author.

The content of this slideshow comes from Section 8.1 of C. Dougherty, Introduction to Econometrics, fourth edition 2011, Oxford University Press. Additional (free) resources for both students and instructors may be downloaded from the OUP Online Resource Centre http://www.oup.com/uk/orc/bin/9780199567089/.

Individuals studying econometrics on their own and who feel that they might benefit from participation in a formal course should consider the London School of Economics summer school courseEC212 Introduction to Econometrics http://www2.lse.ac.uk/study/summerSchools/summerSchool/Home.aspxor the University of London International Programmes distance learning course20 Elements of Econometricswww.londoninternational.ac.uk/lse.

11.07.24

Christopher Dougherty EC220 - Introduction to econometrics (chapter 8) Slideshow: stochastic...

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