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Econometrics-I-18

495.0000 female |.31954440.00217499 146.918.0000

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Unformatted text preview: 495 .0000 FEMALE | .31954440 .00217499 146.918 .0000 HHNINC | -.52475878 .00733328 -71.559 .0000 EDUC | -.04986696 .00062283 -80.065 .0000 Why are they so different? Model failure. This is a panel. There is autocorrelation. &#152;&#152;&#152;&#152;™™ ™ 27/47 Part 18: Maximum Likelihood Estimation MLE vs. Nonlinear LS &#152;&#152;&#152;&#152;™™ ™ 28/47 Part 18: Maximum Likelihood Estimation Testing Hypotheses – A Trinity of Tests The likelihood ratio test: Based on the proposition (Greene’s) that restrictions always “make life worse” Is the reduction in the criterion (log-likelihood) large? Leads to the LR test. The Wald test: The usual. The Lagrange multiplier test: Underlying basis: Reexamine the first order conditions. Form a test of whether the gradient is significantly “nonzero” at the restricted estimator. &#152;&#152;&#152;&#152;™™ ™ 29/47 Part 18: Maximum Likelihood Estimation Testing Hypotheses Wald tests, using the familiar distance measure Likelihood ratio tests: LogLU= log likelihood without restrictions LogLR= log likelihood with restrictions LogLU > logLR for any nested restrictions 2(LogLU – logLR) chi-squared [J] &#152;&#152;&#152;&#152;™™ ™ 30/47 Part 18: Maximum Likelihood Estimation Testing the Model +---------------------------------------------+ | Poisson Regression | | Maximum Likelihood Estimates | | Dependent variable DOCVIS | | Number of observations 27326 | | Iterations completed 7 | | Log likelihood function -106215.1 | Log likelihood | Number of parameters 4 | | Restricted log likelihood -108662.1 | Log Likelihood with only a | McFadden Pseudo R-squared .0225193 | constant term. | Chi squared 4893.983 | 2*[logL – logL(0)] | Degrees of freedom 3 | | Prob[ChiSqd > value] = .0000000 | +---------------------------------------------+ Likelihood ratio test that all three slopes are zero. &#152;&#152;&#152;&#152;™™ ™ 31/47 Part 18: Maximum Likelihood Estimation Wald Test--> MATRIX ; List ; b1 = b(2:4) ; v11 = varb(2:4,2:4) ; B1'<V11>B1$ Matrix B1 Matrix V11 has 3 rows and 1 columns. has 3 rows and 3 columns 1 1 2 3 +-------------- +------------------------------------------ 1| .31954 1| .4856275D-04 -.4556076D-06 .2169925D-05 2| -.52476 2| -.4556076D-06 .00048 -.9160558D-05 3| -.04987 3| .2169925D-05 -.9160558D-05 .2988465D-05 Matrix Result has 1 rows and 1 columns. 1 +-------------- 1| 4682.38779 LR statistic was 4893.983 &#152;&#152;&#152;&#152;™ ™ 32/47 Part 18: Maximum Likelihood Estimation Chow Style Test for Structural Change &#152;&#152;&#152;&#152;™ ™ 33/47 Does the same model apply to 2 (G) groups?...
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495.0000 FEMALE |.31954440.00217499 146.918.0000 HHNINC...

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