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ECON 584

Fall 2022

Homework #3

1. The first part of this homework is to replicate the regression analysis for the admail demand model in Chapter 1. To begin, use SST or Excel (or another program) and the dataset from Chapter 1 “master.xlsx” to replicate the demand regression. You will use “newmaster.xlsx” for part #6 of this assignment.

2. Replicate the regression in Table 1.1 of the text. Note the estimation period is all periods ending in January 1996 (142 observations). Please double check the estimation period against the book. For the assignment, please copy and paste the regression output and highlight the regression coefficients (which should match the ones in the book). HINT: You’ll need to create some variables; you’ll also have to create other variables by dividing some of ones already there by CPI or taking the natural log of others.

3. Use the regression coefficients to create a model that predicts demand and express it like     “Y  = -20.1338 - 0.32071*ln(pi_mnppr/cpi) + …” Graph predicted and actual demand for the regression model in Step 1 using Excel.

4. Interpret the regression coefficients of ln(pi_mnppr/cpi), ln(all_ad_p/cpi),  ln(ret_m_al/cpi) from Question 2. Also interpret the meaning of R^2 in your regression result.

5. Consider an alternative model with only trend and seasonal dummy variables and higher order trend variables. Write out your model in the same format as used in part 3 (e.g. Y = -281.1470 - 5.1326*trend + …). Does your model fit better or worse? Show your model in a graph. Copy and paste regression output from this model. Do you prefer it? Why? HINT: to answer the “why” question, don’t just look at R^2.

6. Use your model and the model in Table 1.1 to forecast demand in the period June 1996 through July 1997. How do the forecasts compare? Graph the forecast and the actual data on in one plot. You will be doing an ex-poste forecast only i.e. pretending as if you knew the values in the period June 1996 through July 1997 before they actually occurred.