Least absolute deviations estimation is an alternative to OLS that is less

Least absolute deviations estimation is an

This preview shows page 32 - 35 out of 77 pages.

Least absolute deviations estimation is an alternative to OLS that is less sensitive to outliers and that delivers consistent estimates of conditional median parameters.
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332 Part 1 Regression Analysis with Cross-Sectional Data K E Y T E R M S Attenuation Bias Average Partial Effect (APE) Classical Errors-in-Variables (CEV) Conditional Median Davidson-MacKinnon Test Endogenous Explanatory Variable Endogenous Sample Selection Exogenous Sample Selection Functional Form Misspecification Influential Observations Lagged Dependent Variable Least Absolute Deviations (LAD) Measurement Error Missing Data Multiplicative Measurement Error Nonnested Models Nonrandom Sample Outliers Plug-In Solution to the Omitted Variables Problem Proxy Variable Random Coefficient (Slope) Model Regression Specification Error Test (RESET) Stratified Sampling Studentized Residuals P R O B L E M S 9.1 In Problem 4.11, the R -squared from estimating the model log( salary ) 0 1 log( sales ) 2 log( mktval ) 3 profmarg 4 ceoten 5 comten u , using the data in CEOSAL2.RAW, was R 2 .353 ( n 177). When ceoten 2 and comten 2 are added, R 2 .375. Is there evidence of functional form misspecification in this model? 9.2 Let us modify Computer Exercise C8.4 by using voting outcomes in 1990 for incumbents who were elected in 1988. Candidate A was elected in 1988 and was seeking reelection in 1990; voteA90 is Candidate A’s share of the two-party vote in 1990. The 1988 voting share of Candidate A is used as a proxy variable for quality of the candidate. All other variables are for the 1990 election. The following equations were estimated, using the data in VOTE2.RAW: 2 voteA90 75.71 .312 prtystrA 4.93 democA (9.25) (.046) (1.01) .929 log( expendA ) 1.950 log( expendB ) (.684) (.281) n 186, R 2 .495, - R 2 .483, and 2 voteA90 70.81 .282 prtystrA 4.52 democA (10.01) (.052) (1.06) .839 log( expendA ) 1.846 log( expendB ) .067 voteA88 (.687) (.292) (.053) n 186, R 2 .499, - R 2 .485. (i) Interpret the coefficient on voteA88 and discuss its statistical significance.(ii) Does adding voteA88 have much effect on the other coefficients?
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