代写代考 ECON0019: QUANTITATIVE ECONOMICS AND ECONOMETRICS EMPIRICAL PROJECT 2021

ECON0019: QUANTITATIVE ECONOMICS AND ECONOMETRICS EMPIRICAL PROJECT 2021
Instructions
The mark for the empirical project is worth 20% of your total mark for the module.
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1. Please elect one group member to submit the project for the group.
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uploading the document, get in touch with ISD as soon as possible to figure out what the problem might be.
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In “Something to Talk About: Social Spillovers in Movie Consumption” (Journal of Political Econ- omy, 2016), D. Gilchrist and E. Sands study whether higher movie attendance leads to subsequent increases in viewership. In other words, “does the demand for a movie depend on whether others have seen it?” One difficulty in examining this question is that a large initial box office followed by large audiences in subsequent weeks may be due to factors such as the intrinsic quality of the film and the apparent momentum is unrelated to the phenomenon above. To address this issue, the authors use weather shocks as an instrumental variable for cinema viewership in the opening weekend.
The authors restrict their analysis to Friday, Saturday and Sunday (“weekend”) box office measures for the first 6 weekends since the release. The Stata data file ECON00192021.dta contains observations on daily national attendance to selected movies between Jan 2002 and Dec 2012. All movies released on the same weekend are grouped together, such that one observation corresponds to a pair of the current date and the opening weekend.
The main variables in the dataset are:
• date — the current date;
• dow — the day of the week (5 = Friday, 6 = Saturday, 0 = Sunday) corresponding to date; • week — the week of the year, from 1 to 52, corresponding to the current date;
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• year — the year from 2002 to 2012, corresponding to the current date;
• sat date – the Saturday of the weekend to which the current date belongs
• opening sat date — Saturday of the opening weekend for the movies (thus date and opening sat date jointly identify an observation);
• wkintheaters — the number of week, from 1 to 6, since the release. The value of 1 corresponds to the opening weekend (opening sat date = sat date);
• tickets – the box office on the date day, in USD million, of all movies released in the weekend defined by opening sat date;
• ltickets = log(tickets);
• temperature75 — higher number means that temperature was nicer on the current date (specif- ically, it is the fraction of the movie theaters nationally where the weather was between 75 and 80 Farenheit, i.e. 24–27 Celcius);
• rain — how much rain there was on the current date;
• open ∗ — variables for the opening weekend instead of the current date;
• h∗ – dummy variables marking holidays (see Appendix A in the paper for a complete description).
Answer the following questions:
For the questions 1–4 you will need to collapse the data to the current weekend level and focus on the sum of box offices for that weekend. To do so you need to use the following line:
collapse (sum) tickets, by(sat date week year)
1. Plot the time series for ticket sales. Do you see any seasonal patterns or trends?
HINT: Though there are other ways of achieving this, you might want to declare we are working with a time series observed at a weekly frequency so you can use commands like tsline. If so, this is how you do it: tsset sat date, daily delta(7).
2. To examine the possibility of a trend, regress tickets on a constant and a set of year dummies. What are the F-statistic and its p-value? What does this suggest regarding the existence of a trend? HINT: The commands tabulate year, generate(d year) and tabulate week, generate(d week) generate year and week dummy variables, respectively, to include as regres- sors. Alternatively you can add i.year and i.week directly as regressors. These will auto- matically omit a base year or week. If you prefer to include all of them and omit the constant, use instead ibn.year and ibn.week and the option noconstant in the regression.)
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3. Now, to investigate any (within year) seasonality in ticket sales, regress tickets on week-of- the-year dummies. Is there evidence of seasonality? What is the p-value for the null hypothesis that all coefficients are equal?
4. Estimate an AR(1) model for the residuals obtained after removing weekly seasonal effects. In other words, estimate
tickets residualt = γ0 + γ1tickets residualt−1 + εt.
Is there any evidence of higher viewership in one week correlating with higher attendance in the subsequent week? (Hint: The lag of variable tickets residual is obtained as L.tickets residual in Stata.)
For questions 5–8 please use the original sample in ECON00192021.dta.
5. Does nice temperature (as measured by temperature75) predict the box office (ltickets) dur- ing the opening weekend? Check this while controlling for all holiday variables, day-of-week dummies, week-of-year dummies, and year dummies, and using robust standard errors in your regression.
6. Does the opening weekend’s nice temperature predict the box office in the weekend that fol- lows the opening one? Use the same controls, but additionally control for the current nice temperature. Why is this additional control a good idea?
7. Estimate the elasticity of the second-weekend box office with respect to the opening weekend’s one, using opening weekend’s nice temperature as an IV. Keep the same controls from question 6. Interpret the coefficient magnitude. Does your IV coefficient equal to the ratio of the coefficients in questions 6 and 5? Why or why not?
8. Estimate the elasticity from question 7 while using two instrumental variables from the opening weekend: nice temperature and rain. (Add a controls for the current rain on top of all previous controls.) You should find a coefficient similar to that from question 7. What do you learn from this finding? (Hint: pay attention to the first stage.)
For questions 9–11 please use the sample ECON00192021.dta but keep the data for opening Saturdays only. You should have 557 observations.
9. Generate successful as a dummy that the box office is at least USD 3 million. Estimate the average partial effect of nice temperature on successful using logit, probit, and the linear probability models, and interpret their magnitudes. Control for the holiday variables, week-of- year dummies, and the year dummies.
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10. For how many observations does each of these three models predict a probability of success which is outside the [0, 1] interval?
11. Compute the sample mean of successful. We say that each of the models makes a mistake if it predicts a probability of success above the sample mean while success = 0, or the other way round. (Note that we use the sample mean, rather than 0.5, as a threshold.) Report the number of mistakes each of the three models makes.
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