Assignment Task
Task – SECTION A
- Explain what is meant by an error term. What assumptions do we make about the error term when estimating an ordinary least squares (OLS) regression
- Give at least three examples from economics where you expect some nonlinearity in the relationship between variables. Interpret the slope in each case.
- How is the slope coefficient interpreted in a log-linear model, where the dependent variable is (i) in logarithms but the independent variable is not (i.e. a log-linear model), (ii) in a linear-log model and (iii) in a log-log model?
- Formulate the Gauss-Markov theorem. Discuss briefly its assumptions and the consequences for the OLS in case of their violation. Illustrate your answers with examples and graphs when appropriate.
- Explain the concept of non-stationarity of time series. Give examples of economic time series which are (1) stationary and (2) non-stationary. How does the autocorrelation function (ACF) help in detecting non-stationarity?
- Carefully discuss the advantages of using heteroskedasticity-robust standard errors over standard errors calculated under the assumption of homoskedasticity. Give at least two examples where it is very plausible to assume that the errors display heteroskedasticity.
Task – SECTION B
Question
Using a sample of 200 countries a researcher investigates the long-run determinants of growth. He knows that economic growth is determined by, among other factors, the rate of investment and the population growth. Regression realGDP=0.339(0.068)-12.894(3.177)×Pop+1.397(0.229)×InvR2=0.621, SER=0.177where realGDP is the real GDP per capita growth rate, Pop is the average population growth rate and Inv is the average share of investment in the GDP. All variables are averages for the period 1960-2010. The numbers in parentheses are standard errors.Interpret the results and comment on the signs of the coefficients. Calculate the t-statistics. Do you think the coefficients are significantly different from zero? Explain the meaning of the standard error of regression (SER).
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Get Help Now!- The overall F-statistic for the regression is 79.11. The critical values at the 5% and 1% level are respectively 3.00 and 4.61. What is your decision on the null hypothesis?
- In a second regression the researcher also includes for each country the average educational attainment (Educ) in the regression. He obtains the following result:
- realGDP=0.046(0.079)-5.869(2.238)×Pop+0.738(0.294)×Inv+0.055(0.010)×EducR2=0.775, SER=0.137Does the inclusion of the level of education affect the previous results? Do you think the first regression (without controlling for the lelvel of education) suffers from omitted variable bias?
- One country in the sample has the following values: realGDP=0.30, Pop=0.021, Inv=0.169 and Educ=3.5. Does the regression in point (c) overpredict or underpredict the real GDP growth rate for this country?
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