What is a Distributed Lag Model - Data Analytics Assignment Help

Assignment Task

1. What is a distributed lag model? Why is it preferable to a static time-series model? Explain.
2. What is a structural break in the data? Why is it important to control for a structural break if one occurs? How can you test and control for a structural break in your data? Explain.
3. What is spurious correlation? Why is it important to consider whether spurious correlation exists? How can you do so? Explain.
4. Suppose you estimate a static time-series model of U.S. Personal Consumption Expenditures (billions) on U.S. Personal Income (billions) using quarterly data for the period 1969Q1-2013Q3 and you get

a) How do you interpret the estimated sample regression function? Explain.
b) You wonder if previous values of income affect consumption. Explain how you would estimate a distributed lag model of order 2.
c) Using the model in part b, how would you test for a statistical relationship between past values of income and consumption?
d) If you thought there was a structural break in these data in 2006Q1, how would you control and test for it?

Q.2 These questions relate to the AUTO EXHIBIT
a. Which three variables in this data set are the most highly correlated?

b. According to REGRESSION 2A, approximately what is the rate of change of outstanding automobile loans per year?
c. Is there evidence of systematic "seasonal" variations in the level of AUTOCRED, according to REGRESSION 2B?
d. Based solely on REGRESSION 2B, does multicollinearity compromise our ability to discern the incremental effects on AUTOCRED of changes in any of the individual explanatory variables? Explain.

e. What is the purpose of REGRESSION 2C? What does it imply about the results obtained from REGRESSION 2B?
f. The ‘auto’ command is used before Regression 2D to fix the problems revealed by Regression 2C . This ‘auto’ command uses the Cochrane-Orcutt procedure. Is REGRESSION 2D likely to be adequate to correct the problems revealed by REGRESSION 2C? Why or y not? Explain.
g. Regression 2E uses the auto command to fix the problems revealed by Regression 2C using the Autoregressive error model procedure. Suppose the REGRESSION 2E was your preferred model. Does this specification suggest the presence of seasonal effects in AUTOCRED? Which months tend to have the highest amount of outstanding car loans?

Which months tend to have the lowest amount of outstanding car loans?
h. How do the implications of REGRESSION 2E differ from those of REGRESSION 2B concerning the effects on car loans of (a) nominal interest rates, and (b) car dealer inventories? Explain.
i. Compare the goodness-of-fit of REGRESSION 2B with that of REGRESSION 2E.

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