Econometric Methods Essay

get out A. Multiple choice questionsAnswer separately question by circling single and only one answer. Each question is worth 3 attach (total 30 marks).1. When estimating a linear fortune position using OLSa. The estimators ar strokeed because breaks argon necessarily heteroskedasticb. The slope coefficient accounts stinkpot non prise changes in the bided opportunity of Y=1c. The estimators can be asymptotically usually distri besidesed d. all of the above2. When internal validity is violateda. OLS coefficients no longer measure the partial correlation between the instructive unsettled star and the dependent inconsistent starb. The population wrongdoing destinations cannot be normally distri just nowed c. The dependent varying necessarily becomes skewedd. None of the above3. Which of the interest dependent variables is least like a limited dependent variable? a. Wagesb. lucre assets of a househ senile (total assets minus debts)c. Number of visits to the dent ist in a yeard. An index of happiness where happiness is rated 1 to 104. A variable Y is a Bernoulli variablea. Its distribution has the usual 2 independent parameters representing the mean and the varianceb. Its expected range equals the ratio of the opportunity of Y=0 to the luck of Y=1c. Its variance equals the reaping of the luck of Y=0 and the opportunity of Y=1d. solely of the above5. In the probit model seen in classa. The variance of the error term depends on the vector of informative variables b. The variance of the error term is assumed to be 1c. The variance of the error term does not need to be specified because of the normality assumptiond. The variance of the error term can be estimated from the variance of the estimated residual6. In control board entropy, the problem of attrition refers toa. The presence of large measurement error in key variablesb. The correlation of measurement errors with instructive variablesc. The misclassification of key dummy explanat ory variables out-of-pocket to measurement error d. None of the above7. In the probit modela. The partial cause of a single continuous explanatory variable X on the predicted probability has the same sign as the estimated coefficient on Xb. The test statistic constructed by the ratio of the estimated coefficient to its standard error is normally distributed because we are using the normal distribution to model the expected value of the dependent variablec. The partial set up of an explanatory variable are quantitatively close to naught when the standard error of the coefficient on this variable is very large.d. All of the above8. You dupe data on a savor of 95 managers working in large firms in Australia. You estimate a logit model of Y= 1 if recogniseing $500,000 per annum using as explanatory variables F=1 if the manager is female (0 otherwise) PHD=1 if the manager has a PHD (0 otherwise) an interaction variable FPHD=F*PHD TEN=tenure with the firm mensural in years (a cont inuous variable). You find the pastime estimatesIndexi = 0.053 0.095 Fi + 0.020 PHDi + 0.007 FPHDi + 0.0015 TENi (0.002) (0.011) (0.009) (0.003) (0.0005)where the standard errors are denoted in parenthesis. You want to test H0 tenure has no effect on the probability of earning $500,000 per annum versus H1 tenure has a positive effect on the probability of earning $500,000 per annum. You willing use a 5% level of significance to apportion this test. You get an asymptotic t-stat equal to 3.0. Using the tables provided at the end of the examen, choose one of the following as an appropriate critical value to communicate this testa. 1.662b. 1.645c. 1.987d. 1.960e. 5.02399. Refer to the model and estimates in the previous question. Ceteris paribus, according to these estimates (and ignoring statistical significance)a. Women without PHDs have a higher(prenominal) probability of earning $500,000 than men without PHDs.b. Men with PHDs have a lower probability of earning $500,000 than men without PHDs. c. Women with PHDs have higher probability of earning $500,000 than women without PHDs. d. Women with PHDs have higher probability of earning $500,000 than men with PHDs.10. Refer to the model and estimates in the previous question. You want to test that ceteris paribus, men and women have the same probability of earning $500,000. Under the null, the Wald test statistic is asymptotically chi-squared distributed with a. 1 degree of emancipationb. 90 degrees of freedomc. 93 degrees of freedomd. 2 degrees of freedome. 3 degrees of freedomPART A. Multiple Choice1. C2. D3. B4. C5. B6. D7. A8. B9. C10. DSOLUTIONS reveal B. Problem (Total 30 marks)Equity of retrieve is a primary ending of m both health systems. Determining whether Australias system (Medicare) meets this goal is an primary(prenominal) research question. Consider the case of access to general practitioners (GPs). The probit results presented below in dodge 4 are part of an analysis aimed at tell wheth er on that point is equitable access to GP services where access is delineate on the basis of health needs rather than ability to pay. The data consists of a sample of 3207 single females who were surveyed throughout Australia in 1995. The dependent variable for the study was VISIT, an indicator variable that was equal to one if the women had visited a GP in the last dickens weeks and zero otherwise. The sample has been divided into cardinal subsets depending on whether the women are less than 40 years sexagenarian (the puppylike sub-sample) or whether they are greater than 40 years honest-to-goodness (the old subsample). Table 4 presents estimation results (variable definitions follow the table).YoungOldTable 4 Probitestimates for visitto GP* VariableIntercept-0.7910 (0.1602)-1.1570 (0.2495)AGE-0.0060 (0.0064)0.0055 (0.0033)wellness0.3930 (0.0687)0.6131 (0.0746)KIDS0.1651 (0.0881)-0.1479 (0.1159)INCOME0.0003 (0.0032)-0.0052 (0.0037)TERTDUM0.0120 (0.1042)0.0844 (0.1509)TRADED UM0.1842 (0.0884)0.2399 (0.1013)DIPDUM0.0077 (0.1281)0.0478 (0.1422)PHI0.0258 (0.0783)0.1781 (0.0768)Observations17171490Log-likelihood-935.52-892.24PART B.i. (8 marks) Discuss the effects of PHI on the probability of visit a GP and compare these effects for the two subsamples of new-fangled and old women. reiterate the exercise for the KIDS variable. Do you think that these variables are likely to violate the zero conditional mean assumption? DiscussPHIIn both subsamples, the estimated coefficient on PHI is positive ceteris paribus the probability of visiting a GP is higher for those with PHI than without. The surface of coefficients may be discussed using the rule of thumb but these must(prenominal) not be confused with partial effects. The effect is statistically evidentiary among the old while the opposite is true for the juvenile. In the young subsample, the coefficient is unnoticeable at some(prenominal) formulaic level (t statistic for testing irrelevance of PHI agai nst the 2-sided alternating(a) is 0.3295 1.645) whereas in the old subsample it is significantly different from zero at the 5% significance level (t statistic = 2.319 1.96).The sign is as expected since PHI makes it cheaper to use GP services and women who expect to visit GPs to a greater extent than often are more likely to purchase PHI. The latter implies that ZCM may be violated due to a selection effect.KIDSIn the young subsample, the coefficient on KIDS is positive and statistically different from zero at the 10% level (t statistic = 1.874 1.645) the probability of GP visit is higher for those with dependent infantren.In the old subsample, the sign of the coefficient indicates that the effect is negative but the coefficient is statistically insignificant from zero at conventional levels (t stat = 1.276 1.645). A priori, the expected sign is ambiguous women may visit GPs for childrens medical care as well as their own (positive) but at the same time they may become busie r due to child rearing (negative). For the old sample, KIDS may be older and hence mothers no longer visit GPs for the childrens health. Other reasonable explanations are acceptable. You can indicate both ways on the ZCM assumption for example, you can argue that fertility determinations are exogenous to GP visits. You could also argue that there is an omitted variable bias (KIDS is picking up some unobserved part e.g. better health measurement than what is being captured by the existing explanatory variables). Also if the true underlying relationship depends on the number of nonmigratory dependent children, KIDS is top-coded at 1, causing the ZCM assumption to fail due to a measurement error correlated with this variable. supernumerary materialYou could also earn marks (lost elsewhere in the question) by discussing the size of the effects. For example, the effect among the young seems non-trivial in the sense that the coefficients magnitude is slightly over 40% of that of th e coefficient on the poor health indicator (HEALTH) while for the old, the variable seems far-off less economically relevant relative to HEALTH. ii. (5 marks) If there is equity of access then variables related to income, education and private health insurance should not affect visits to GPs. When the models are re-estimated without these variables (i.e. with only AGE, HEALTH and KIDS included) the log-likelihood set are 937.92 for the young sub-sample and 898.63 for the old. Using these results evaluate the null hypothesis of equity of access.Statement of the hypotheses figure statisticsLR test statisticsLLRYOUNG = 2(-935.52+937.92) = 4.8LLROLD = 2(-892.24+898.63) = 12.78.Distributions of the test statistics and critical valuesThey are asymptotically chi-squared distributed with 5 degrees of freedom under the null.The appropriate 10% and 5% critical values are 9.2364 and 11.0705 respectively. Decision rules and conclusionsSince LLRYOUNG 9.2364, we fail to recall the null at 10 % level in the young subsample there is not enough attest to conclude that income, education and PHI variables affect young womens GP visits.Since LLROLD 11.0705, we reject the null at the 5% significance level in the old subsample and conclude that there is some evidence against equity of access among the old women. iii. (4 marks) Consider two subjects of women type 1 where AGE = 20, HEALTH = 1, INCOME = 20 and all other variables = 0 type 2 is identical get out that AGE = 60. Write down the equation(s) you would use to compare the probability of visiting a GP for these two types of women. Using the probit results can you determine which of these two types of women are more likely to have visited a GP in the last two weeks? If your answer is yes then make the comparison, if your answer is no then explain what information you would need to make the comparison.One likely answer is to use the index and argue that the ranking by the probabilities will be the same as that provided by the indexIndex for type 1 = -.791 + -.006*20 +.3930 + 0.0003*20 = -.5120 -.51 Index for type 2 = -1.1570 + .0055*60 +.6131 0.0052*20 = -.3179 -0.32 Since the standard normal CDF increases in the probit index, type 2 womanhood is more likely to visit GPs than type 1 woman.Another realistic answer is to write down the normal CDF for the two types and argue that the equation for type 2 will be greater than type1.Additional materialYou could also earn marks (lost elsewhere in the question) by calculating the difference in the probabilities using the table on p.10 of the exam paper i.e. the difference in the predicted probabilities can be evaluated as (.5-.1255)-(0.5-.1950) = 0.0695 .07 higher for type 2. iv. (6 marks) In determining the sample to be used for estimation, any individual who did not report their income or reported zero income was deleted from the analysis. Do you see any real or potential problems with this modelling decision? Can you provide an alternative method to deal with this problem? apt(predicate) problems (one of the following or another sensible problem) -The potential selection bias which arises when the decision to report zero income or refuse reporting any is correlated with the decision to use GP services. For instant, top incomegroups may be more jealous of their income information and at the same time more likely to be health conscious and visit GPs in impression excluding the said individuals would affect all coefficient estimates as the model would have to predict a lower probability of GP visit on average.-The drop-off in the sample size and the resulting increase in standard errors. The incomplete cases may still provide useful information on the effects of other variables on GP visits and the researcher has discarded this information.Alternative solutions (one of the following or another sensible solution) Use other information to ascribe the missing information Use dummy variables for missing income. More ripe imput ation methods Estimate a selection model (this is covered in more detail later in the class but you may cope about it from reading or elsewhere)v. (7 marks) Explain how you would construct and use a hit and miss table to compare the performance of the models for the two subsamples of women (young and old). (You do not have to actually construct a table.) bill 1. cypher a predicted probability for each person in the relevant subsample. Step 2. Obtain a predicted binary outcome for each person using a classification rule if person is predicted probability exceeds c, the predicted outcome is 1 and otherwise 0. It is ok if you use 0.5 or the sample mean.Step 3. For each subsample, tabulate frequencies of predicted and actual binary outcomes in the following formPredicted01Observed 0 A B1 B AwhereA (A) = the total number of women whose predicted and observed outcomes are 0 (1) B (B) = the total number of women whose observed outcome is 0 (1) but the predicted outcome is 1 (0).Step 4. N ow, compare the relative frequencies of correct predictions for each subsamples i.e., compare (A+A) / (A+B+A+B) across the subsamples. This tells us how well one model performs relative to another in cost of predicting the observed outcomes. It is ok to describe the comparisons of the predicted 0s separately from the predicted 1s (ie the comparisons of A / (A+B) and A / (A+B) across subsamples) but this is not needed for full marks.