伍德里奇計(jì)量經(jīng)濟(jì)學(xué)講義2.ppt

1、1,Multiple Regression Analysis,y = b0 + b1x1 + b2x2 + . . . bkxk + u 7. Specification and Data Problems,2,Functional Form,Weve seen that a linear regression can really fit nonlinear relationships Can use logs on RHS, LHS or both Can use quadratic forms of xs Can use interactions of xs How do we know

2、 if weve gotten the right functional form for our model?,3,Functional Form (continued),First, use economic theory to guide you Think about the interpretation Does it make more sense for x to affect y in percentage (use logs) or absolute terms? Does it make more sense for the derivative of x1 to vary

3、 with x1 (quadratic) or with x2 (interactions) or to be fixed?,4,Functional Form (continued),We already know how to test joint exclusion restrictions to see if higher order terms or interactions belong in the model It can be tedious to add and test extra terms, plus may find a square term matters wh

4、en really using logs would be even better A test of functional form is Ramseys regression specification error test (RESET),5,Ramseys RESET,RESET relies on a trick similar to the special form of the White test Instead of adding functions of the xs directly, we add and test functions of So, estimate y

5、 = b0 + b1x1 + + bkxk + d12 + d13 +error and test H0: d1 = 0, d2 = 0 using FF2,n-k-3 or LM22,6,Nonnested Alternative Tests,If the models have the same dependent variables, but nonnested xs could still just make a giant model with the xs from both and test joint exclusion restrictions that lead to on

6、e model or the other An alternative, the Davidson-MacKinnon test, uses from one model as regressor in the second model and tests for significance,7,Nonnested Alternatives (cont),More difficult if one model uses y and the other uses ln(y) Can follow same basic logic and transform predicted ln(y) to g

7、et for the second step In any case, Davidson-MacKinnon test may reject neither or both models rather than clearly preferring one specification,8,Proxy Variables,What if model is misspecified because no data is available on an important x variable? It may be possible to avoid omitted variable bias by

8、 using a proxy variable A proxy variable must be related to the unobservable variable for example: x3* = d0 + d3x3 + v3, where * implies unobserved Now suppose we just substitute x3 for x3*,9,Proxy Variables (continued),What do we need for for this solution to give us consistent estimates of b1 and

9、b2? E(x3* | x1, x2, x3) = E(x3* | x3) = d0 + d3x3 That is, u is uncorrelated with x1, x2 and x3* and v3 is uncorrelated with x1, x2 and x3 So really running y = (b0 + b3d0) + b1x1+ b2x2 + b3d3x3 + (u + b3v3) and have just redefined intercept, error term x3 coefficient,10,Proxy Variables (continued),

10、Without out assumptions, can end up with biased estimates Say x3* = d0 + d1x1 + d2x2 + d3x3 + v3 Then really running y = (b0 + b3d0) + (b1 + b3d1) x1+ (b2 + b3d2) x2 + b3d3x3 + (u + b3v3) Bias will depend on signs of b3 and dj This bias may still be smaller than omitted variable bias, though,11,Lagg

11、ed Dependent Variables,What if there are unobserved variables, and you cant find reasonable proxy variables? May be possible to include a lagged dependent variable to account for omitted variables that contribute to both past and current levels of y Obviously, you must think past and current y are r

12、elated for this to make sense,12,Measurement Error,Sometimes we have the variable we want, but we think it is measured with error Examples: A survey asks how many hours did you work over the last year, or how many weeks you used child care when your child was young Measurement error in y different f

13、rom measurement error in x,13,Measurement Error in a Dependent Variable,Define measurement error as e0 = y y* Thus, really estimating y = b0 + b1x1 + + bkxk + u + e0 When will OLS produce unbiased results? If e0 and xj, u are uncorrelated is unbiased If E(e0) 0 then b0 will be biased, though While u

14、nbiased, larger variances than with no measurement error,14,Measurement Error in an Explanatory Variable,Define measurement error as e1 = x1 x1* Assume E(e1) = 0 , E(y| x1*, x1) = E(y| x1*) Really estimating y = b0 + b1x1 + (u b1e1) The effect of measurement error on OLS estimates depends on our ass

15、umption about the correlation between e1 and x1 Suppose Cov(x1, e1) = 0 OLS remains unbiased, variances larger,15,Measurement Error in an Explanatory Variable (cont),Suppose Cov(x1*, e1) = 0, known as the classical errors-in-variables assumption, then Cov(x1, e1) = E(x1e1) = E(x1*e1) + E(e12) = 0 +

16、se2 x1 is correlated with the error so estimate is biased,16,Measurement Error in an Explanatory Variable (cont),Notice that the multiplicative error is just Var(x1*)/Var(x1) Since Var(x1*)/Var(x1) 1, the estimate is biased toward zero called attenuation bias Its more complicated with a multiple reg

17、ression, but can still expect attenuation bias with classical errors in variables,17,Missing Data Is it a Problem?,If any observation is missing data on one of the variables in the model, it cant be used If data is missing at random, using a sample restricted to observations with no missing values w

18、ill be fine A problem can arise if the data is missing systematically say high income individuals refuse to provide income data,18,Nonrandom Samples,If the sample is chosen on the basis of an x variable, then estimates are unbiased If the sample is chosen on the basis of the y variable, then we have sample selection bias Sample selection can be more subtle Say looking at wages for workers since people choose to work this isnt the same as wage offers,19,Outliers,Sometimes an individual observation can be very di