Regression Model and Housing Prices

You must choose between exercise 1 – exercise 1 uses data on housing prices and its determinants — and exercise 2 – exercise 2 uses data on the business cycle and its determinants.
Do either exercise. Write a 2000 words essay.
Exercise 1 – Regression Model and Housing Prices
The exercise requires that you look at issues that are important for applying Multiple Regression to an empirical problem. Make sure that you define and explain the key terms.
In your essay, you must address the following questions:
Question 1: Describe and explain the multiple linear regression model. The model allows for proportional relationships between dependent and independent variables. Discuss carefully at least three textbook examples of successful applications of the model. For each example, give reasons that justifies the usage of model that is linear in the parameters and give reasons that would lead a researcher to question its validity.
Question 2: Describe three devices in econometrics that allow nonlinear relationships between the dependent variable and independent variables. For each of the three devices, discuss carefully at least two plausible applications.
Question 3: Use the housing price data HPRICE1 — the sample of 506 communities in the Boston area. Estimate a model relating median housing price, PRICE, in the community to various determinants. NOX is the amount of nitrogen oxide in the air – in parts per million. DIST is the weighted distance of the community from five employment centres – in miles. ROOMS is the average number of rooms in houses in the community. STRATIO is the average student-teacher ratio of schools in the community.
The model is
3a) What is What is What is What is
3b) For explaining variation in PRICE, decide whether you prefer the model in part 3a) or the model
3c) Estimate the model Report your results in the usual – textbook — form. Test against the alternative . Do the slope estimates have the anticipated signs? Are the coefficients statistically different from zero? Is there evidence that the elasticity is different from 1. By how much does one more room increase the price? If STRATIO increases by one, the price decrease by how much?
Question 4: What are the benefits and limitations of using logs?
Exercise 2 – Regression Model and Business Cycle
The exercise requires that you look at issues that are important for applying Multiple Regression to an empirical problem. Make sure that you define and explain the usual key terms of regression analysis. You will find that the handout STATISTICS – COMPUTER EXERCISE – INVESTMENT SPENDING from December 2009 in the course content section is useful.
In your essay, you must address the following questions:
Question 1: What is economic growth? What is the business cycle? What is the relation between the two concepts? What is a recession? What causes recessions?
Define and explain the accelerator principle. Define and explain the marginal propensity to consume. Define and explain the following terms: GNP, investment spending, CPI, inflation rate and the interest rate.
Question 2: Use the data set “businesscycle”. The data is 15 yearly observations. YEAR is the date. GNP is the nominal gnp in billions of U.S. dollars. INVEST is nominal investment spending in billions of U.S. dollars. CPI is the consumer price index. INTEREST is the interest rate – the average yearly discount rate at the New York Federal Reserve Bank.
Convert the investment and GNP series in real terms; obtain the series GNPREAL and INVESTREAL. Generate a series time trend, TIME. Generate a series, INFLATION.
Plot the time-series of GNPREAL. Is GNPREAL increasing over time? Has GNPREAL experienced swings?
Plot the time-series of INVESTREAL. Is INVESTREAL increasing over time? Has INVESTREAL experienced swings?
For explaining variation in INVESTREAL, use the model Plot the “actual, fitted and residual” graph. Report the regression output in the usual – textbook – format. Asses the quality of the regression; do you conclude that the time trend, TIME, has a significant effect in INVESTREAL and do you conclude that the GNPREAL has a significant effect in INVESTREAL?
Question 3: Use the data set “businesscycle”.
For explaining variation in INVESTREAL, use the model
Report the regression output in the usual – textbook – format. Is there an upward trend in the data?
Question 4: Use the data set “businesscycle”.
For explaining variation in INVESTREAL, use the model
Report the regression output in the usual – textbook – format. Explain your results. Interpret the meaning of the estimated coefficients.
Question 5: Use the data set “businesscycle”.
For explaining variation in INVEST, use the model
Report the regression output in the usual – textbook – format. Explain your results. Interpret the meaning of the estimated coefficients.
Question 6: Describe and explain the Durbin-Watson test.
Question 7: For the model and data of Question 5, conduct the Durbin-Watson test for positive first-order autocorrelation and conduct the Durbin-Watson test for negative first-order autocorrelation. Use the University of Notre Dame Significance table at

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