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**Unformatted text preview: **Brock University
Faculty of Social Sciences
Department of Economics Economics 3P92: Forecasting
Fall 2011 Midterm 1 I ' n : .- :s- “f T" Question 1: What sorts of forecasts would be useful in the following decision-making situations? ( d {I
6* L Why? What sorts of data might you need to produce such forecasts? 3—{‘ I Eff]
' "‘ e 2.. 9; a, Shop-AllAThe-Time Network (SATTN) needs to schedule operators to receive incomiﬁg‘callsr The
volume of calls Varies depending on the time of day, the quality of the TV advertisement, and the price
of the good being sold. SATTN must schedule staff to minimize the loss of sales [too few operators
leads to long hold times, and people hang up if put on hold) while also considering the loss associated with hiring excess employees. , - _ . _ . _ ,
/‘- 1W “Is’rrd :‘L_ -- 4-" w; r , - . rr‘. . . ,
b. You run BUCO, a British utility suiﬂying electricity to the London metropolitan area. "You - need to decide how much capacity to have on line, and two conﬂicting goals must be resolved in
order to make an appropriate decision. You obviously want to have enough capacity to meet average
demand, but that’s not enough, because demand is uneven throughout the year. In particular, demand ‘
skyrockets during summer heat waves - which occur randomly — as more and more people run their
air conditioners constantly. If you don‘t have sufﬁcient capacity to meet peak demand, you get bad
press. On the other hand, if you have a large amount of excess capacity over most of the year, you ,_ also get bad press. f.“ c“ ' ’ _.,1 Question 2: For each of the diagnostic statisticsé listed below, indicate whether, other things the
same, “bigger is better,” “smaller is better,” or neither. Explain your reasoning. (Hint: Be careful, think before you answer, and be sure to qualify your answers as appropriate.) ’ ___-_r::s(- « ,, Q . Coefﬁcient [to 13, ' l— ' -‘ I aw." - 5,“ __
‘b- WW c. tstatistic T‘S’Utt d‘ Probability value (p value) of the t-statistic P, {Mug e. R squared if f. Adjusted R squared i?- g. Standard error of the regression h. Sum of squared residuals a) 5 {e -— dim-L Chaﬁng a1». i. Mean of the dependent variable 1 Standard deviation of the dependent variable k. Akaike information criterion L “piper. {D 5, TM l. Schwarz criterion F—aa In. F statistic
11, Probability value [p value) of the F statistic Question 3: After estimating a forecasting model, we often make use of graphical techniques to
provide important diagnostic information lgardmgjhe adeguacy of__th_e____o_del. Often the graphical
techniques involve the residuals from the model. a. What can be achieved with superimposed time series plots of actual and ﬁtted values? 6.. What about a tithhe residuals (a sacalled residual plot)? c. What summary statistics on the residuals might you look at?{Explain what they mean.
. I,- Question 4: For the following scenario, discuss the fiatmeof the object t_o_btithe {precast ' '_ hoggmpn, thepqssible costs_pfmaking-a-wrong forecast and what sorts of Simple or tie-triplex Wing approaches you might entertain. J l) lélzou work for the Ofﬁce of Management and Budget in Washington DC and must forecast tax reirenues Ii-
.. --"'_t3? the upcoming ﬁscal year. You work for a president who wants to maintain funding for his pilot Social programs, and high revenue forecasts ensure that the programs keep their funding. However, if the forecast is too high, and the president runs a large deﬁcit at the end of the year, he will be seen
as ﬁscally irresponsible, which will lessen his probability of reelection. Furthermore, your forecast will
be scrutinized by the more conservative members of Congress; if they find fault with your procedures,
they might have ﬁscal grounds to undermine the President‘s planned budget. Question 5: Consider the modelling of US. labor force participation in the, by gender
55 50- ’SC! (_ _ 55‘ 50— L_.. 45 35——'—.—-.-5-v-—-—'—v—r—r-+ I i 1957 1960 1953 1955 9" "" 19?5 19TH 1931 19?” 1937 1990 1993
Labor Force Participation Rat ...

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- Fall '11
- JeanFrancisLamarche
- Economics, Statistics, Standard Deviation, Akaike information criterion, Faculty Of Social Sciences