Which estimation method is appropriate? Answer with the test results. Estimate the effect of age on smoking. Is it significant? If the age is up to two years, how does it affect smoking?

The following are the quetions that need to be answered. There are 5 sets of data and for each there are some questions attached. Answer ALL questions. I have also attached a PDF file (titled “Questions”) with the questions that need to be answered. All of the datasets that are needed are also attached, they are excel files. You need SAS On Demand. You just need to download SAS On Demand using this link and it is FREE. Here is the link: https://www.sas.com/en_us/software/on-demand-for-academics.html Baltagi and Griffin considered the following gasoline demand function: Log(Yit) = β0 + β1logX1it + β2logX2it + β3logX3it+ uit. Where Y = gasoline consumption, X1 = real income per capita, X2 = real gasoline price and X3 = numbers of cars per capita, i = number of countries and t = number of time periods A priori, what is the expected relationship between Y and X2. Explain why this is so? Estimate the above demand function pooling the data for 18 countries .

Explain the estimates and test whether this demand function is inelastic (Note the definition of elasticity and test about the value of β2) Estimate a fixed effect model and redo the same test and interpret your results. Is the fixed effect model better than pooled regression estimate?

Explain with your computer results. Estimate a random effect model. Is the random effect model appropriate here? Why and why not? Are the results similar? Explain from computer results. From your analysis which model (OLS, FE and RE) best describes the gasoline demand in the 18 OECD countries? Justify your answer. Do you think that there might be a time fixed effect also here? Why so and estimate the model using time fixed effect and identify any specific year that has significant effect on the demand function. Use the data in file Smoke.xls for this problem.

Variables are educ, cigpric, white, age, income, cigs, restaurants, lincome, agesq, lcigpric and Obs: 807 It is assumed that income, educ, and cigs are possibly endogenous and rest are exogenous variables. A model to estimate the effects of smoking on annual income is: log(income) = β0+β1cigs+β2educ+β3age+β4age2 + β5white + u1 How do you interpret the parameters β1 and β2 and what are the expected signs of these two parameters? To reflect on the fact that cigarette consumption might be jointly determined with income, a demand equation for cigarette consumption is set up: Cigs = α + α1log(income) + α2educ + α3age + α4age2 + α5log(cigprice) + α6restaurn + u2 Which sign do you expect for α5 and α6? Explain how many ways endogeneity can occur in a regression model Estimate the income equation by OLS and discuss the estimate of β1 and its properties.

Do the test of endogeneity of cigs in the income equation. (You may use all exogenous variables as instruments here) Now estimate the income equation by 2SLS and compare the estimate β1 with the OLS estimate. Which estimation method is appropriate? Answer with the test results. Estimate the effect of age on smoking. Is it significant? If the age is up to two years, how does it affect smoking?

3. Use the data for Phillips curve in phillips.xls Define the Phillips curve equation, the Static Phillips curve model (relation between inflation and unemployment). Estimate the model with OLS. Report the results. Do the results make sense? Do the test for autocorrelation and explain your findings. If there is autocorrelation, estimate the model after correcting for it and explain the results. Now estimate an expectation augmented Phillips Curve: Δinf = β0 + β1unem + et and estimate the tradeoff between rate of inflation and unemployment rate. Is it significant? Collect the data for the interest rate (any annual rate of your choice) for these years and use it and the time trend (t) as the other exogenous variables (or instrumental variables). Now re-estimate the model with these additional explanatory variables and test whether there is a tradeoff between inflation and unemployment? Now estimate the model using the correction for this endogeneity (if it exists), i.e., use 2SLS or LIML and report the results. Is the trade off different from part c.

4. Use the data Beveridge.csv for this question. What is the Beveridge Curve? Explain. Set up and estimate the regression model: uratet = α + βvratet + ut. Does the sign and magnitude of the slope parameter make sense? Explain the estimated coefficients. What are the possible problems with this estimation? You may guess both variables are nonstationary. Apply the appropriate test for unit roots here. Write out the appropriate equations and describe the null hypotheses and the alternative hypotheses for this test. (I need the equations for this set up here) Now compute the regression residual from part b and test for unit root for the regression residual again. If there is no unit root then urate and vrate are cointegrated. Explain what it means?

5. Use the data in Beauty for this question. Using the data pooledm for men and women, estimate the equation: lwage = β0+β1abvavg+β3female+β4educ+β5exper+β6exper2+u and report the results using typical OLS estimation and heteroskedasticity consistent errors estimation. Are any of the coefficients surprising in either signs or magnitudes? Is the coefficient of female particularly large and significant? Do the test of heteroskedasticity and draw your conclusions. Correct for heteroskedasticity as we discussed in class. Perform a Chow test for structural difference between male and female. Do you see significant differences in estimated coefficients of the model for male and the model for female?