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Smpirical Case #5 FORECASTING THE DEMAND 7OR DOMESTIC AIR TRAVEL -------------- Imagine that it is June 30, 1988, and you have just been hired as an...

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Smpirical Case #5 FORECASTING THE DEMAND 7OR DOMESTIC AIR TRAVEL -------------- Imagine that it is June 30, 1988, and you have just been hired as an airline industry analyst for Drexel, Bumham, Lambert. That's the good news. The bad news is, you have been informed by the &tor of your section that he needs a six-month forecast of do- mestic air travel by the end of the day. Ideally, you would approach this problem by relying on your extensive economic background and develop an appropriate econometric model of domestic airline travel. However, given the immediate deadline, you decide to estimate a trend line and adjust for seasonal variations. Your statistical model is as follows: Trend line: Y, = a. + a, Tie + u, Where Y, is a proxy for air travel quantity demanded; Time equals 1 for the first month's observation, 2 for the next month, etc.; q, and a, represent the intercept and slope param- eters of your trend line and will be estimated by least-squares analysis; and u, represents a well-behaved random error component. TABLE I. Year Month RPM Year Month RPM Year Month RPM 1986 1 18,244 1987 1 20,926 1988 1 21,773 2 11,486 - L-24&392------23202~ ----- 3 21,817 3 26,688 3 261641 4 20,318 4 24.757 4 24,768 5 20,980 5 25,385 5 25,123 6 22,027 6 25,330 6 26.225 7 23,679 7 27,290 8 25,349 8 28.204 9 19,671 9 21,585 10 21,049 10 23,129 11 19,916 11 21,717 12 22,174 12 22,993 RPM = Monthly revenue passenger miles (000). From Aerospace Md Air Transport, Industry Surveys, Stan- dord and Poor's. various issues. Preliminary investigation of the industry indicates that the conventjonal measure of quantity demanded is revenue passenger miles (RPM), which captures the quantity of m
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Part 2 1 Demand Theory, Analysis, and Estimation paying customers. You collect monthly data for the period from January 1986 to June 1988, which is given in Table 1. Use this data to estimate the trend line for domestic air travel. Then use your results to answer the following questions. QUESTIONS I. Plot the data to see if the? is any apparent trmd. Does the trend appear to be upward or downward sloping? 2. Does your regression analysis support your casual observation? Graph your estimated trend line on your plot. Why are there discrepancies between the actual observations and your trend line? 3. Using your estimated trend line, what are your forecasts for the period from July 1988 to December 1988? How confident are you with these forecasts? 4. Does there appear to be any seasonal variation in airline travel? What are sosome of the methods that can be used to adjust for these seasonal differences? Choose one such method and present an adjusted forecast. 5. Which forecast, trend or seasonally adjusted, are you going to present to your director? Ex- plain.
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Answer 1.docx

Answer 1:
Looking at the graph above, we note that the trend is upward sloping.
Answer 2:
The regression coefficient is positive, implying X and Y are positively related, supporting our

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