ECON0019代写、代写Python/C++程序

日期：2023-03-11 12:09

ECON0019: QUANTITATIVE ECONOMICS AND ECONOMETRICS

EMPIRICAL PROJECT 2023

Instructions

The mark for the empirical project is worth 20% of your total mark for the module.

Please follow these instructions so that we can ensure anonymity in marking and ensure compliance

with UCL assessment policies. We will only be able to give you credit for your project if you follow

these instructions. If the instructions are not followed, you will receive a mark of zero.

1. Please elect one group member to submit the project for the group.

2. All answers must be uploaded via Turnitin by 12pm on March 27, 2023.

3. All marking on Turnitin is anonymised. Do not put your name or student number or

group name anywhere on your submitted answer — either in the document or in the file name.

4. Put the candidate numbers for all group members at the top of the first page. Candidate

numbers are NOT student numbers! Use the candidate number from this year—it is not the

same as last year.

5. You should submit one PDF or Word document that includes: you answers and expla-

nations in the main text (including tables and figures, if any), as well as an appendix with

your code producing these results. If you use software other than Stata, you should state which

programme was used. You may optionally include raw statistical output (e.g. Stata log-file) after

the code but such output does NOT substitute for your answers and explanations.

6. Your answers should be no more than 800 words, including footnotes but excluding tables,

figures, the code appendix, and the raw statistical output. State the number of words at the top

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uploading the document, get in touch with ISD (servicedesk@ucl.ac.uk) as soon as possible

to figure out what the problem might be.

You will be awarded a mark of 0% or Grade F if you (1) do not attempt the summative assessment

component or (2) attempt so little of the summative assessment component that it cannot be assessed.

Please check the UCL Academic Manual (Section 3.11) for information on the consequences of not

submitting or engaging with any of your assessment components.

If you have extenuating circumstances that affect your ability to engage with any of the module assess-

ment components, please apply for alternative arrangements to the Economics Department as soon as

possible. See details in Section 6 of the Academic Manual and send your request to economics.ug@ucl.ac.uk.

If you have a disability or long-term medical condition, you may be entitled to adjustments for as-

sessments. This may include an extension for this essay. Please see Section 5 of the Academic

Manual for information on how to apply for adjustments. Contact the Departmental Tutor, Dr Frank

Witte (f.witte@ucl.ac.uk) and the UG Admin team (economics.ug@ucl.ac.uk). Do not contact

the course lecturers about this.

QUESTION:

In “Testing for Imperfect Competition at the Fulton Fish Market” (RAND Journal of Economics, 1995)

and later work, Kathryn Graddy studies demand and competition in the main market for whiting —

a type of fish — in New York City in 1992. The author spent a lot of time at the market and hand-

collected daily observations on the quantity sold and the average price, as well as the quantities and

prices separately for Asian and white buyers. The data include 97 daily observations.

The Stata data file FISH.dta contains observations on the variables of interest. Specifically:

t: day of observation, excluding weekends (and running from 1 to 100, with three days excluded

because the data are missing);

totqty, ltotqty: total quantity sold (to Asian + white buyers) and its log;

avgprc, ltotprc: average per-unit price and its log;

mon, tues, wed, thurs: indicator variables for the day of the week of the observation (with

Friday as an omitted category);

wave2: average max wave height at sea over last 2 days, measured in feet;

wave3: lagged average wave height (two days prior to those in wave2);

prca: price paid by Asian buyers;

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prcw: price paid by white buyers.

The Stata data file FISH panel.dta is a panel version of FISH.dta, with separate observations for

each day for both Asian and white buyers (with 2× 97 observations in total):

t: day of observation;

asian: indicator equal to 1 if observation is observation is for Asian buyers, 0 for white buyers;

lprc: log of price for given group of buyers;

lqty: log of quantity for given group of buyers;

mon, tues, wed, thurs, wave2, wave3: described above.

Some Stata hints:

Command test allows you to compute F -statistics and perform two-sided tests on (single or

multiple) coefficients or their linear combinations.

Type help command to get more details on how a particular command works, e.g. help test.

Type gen varname = f(x) to generate a new variable equal to the function f(x).

Type tsline varname1 varname2 ... to plot the time series of the selected variables. Before

running, type tsset varname to use varname as the time variable.

Type predict varname after a regression to generate predicted values and name them varname.

Type predict varname, resid after a regression to generate predict residuals and name them

varname.

L.varname is the first lag of varname.

ac varname plots the autocorrelation function of varname.

c(pi) is the π = 3.14 . . . constant.

To compute Newey-West standard errors with the ivreg2 command, replace the r for robust in

the syntax with bw(auto).

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Answer the following questions:

1. Run a regression to test whether log total quantity depends on the day of the week. (Allow for

heteroskedasticity in all of your analyses, and assume for now that there is no serial correlation

in the errors.) Report the F-statistic and p-value testing the null hypothesis that the log total

quantity is the same on all days of the week, on average. What do you conclude? Describe any

seasonal pattern you find.

2. Recall that another way to account for seasonality is to use trigonometric functions. Generate

two new deterministic season variables as a function of time, t, with a weekly (i.e., 5-day)

frequency:

Regress log total quantity on these two variables (and a constant). Compute the estimated

seasonal “trend” in this regression and that in the regression of question 1 and plot them together.

What do you conclude about the two approaches?

3. Estimate an OLS regression of log total quantity on log average price, controlling for day-of-the-

week dummies. (Keep using these controls in all regressions below.) Report the slope coefficient

with 3 significant digits. Under which (strong) condition is this estimate consistent for the

demand elasticity?

To deal with simultaneity of demand and supply, Graddy uses instrumental variables which measure

the conditions at sea. Specifically, she uses lagged wave heights (wave2 and wave3).1 Winds above

4.5 feet make fishing more difficult.

4. Estimate the demand elasticity, using wave2 as a single excluded instrument. Report the elas-

ticity estimate and its standard error with 3 significant digits. Test whether the instrument is

strong; report which test statistic you used, which value it takes, and which critical value you

are comparing it to. Provide an argument for the exogeneity of this instrument.

5. Looking for stronger instruments for log price, you recall that waves are supposed to be bad

for fishing only when they exceed 4.5 feet. You therefore conjecture that a dummy wave2high,

indicating that wave2 > 4.5, may better predict log price than wave2 itself. Test this conjecture

in the data. Should one use wave2high as an additional instrument when estimating the demand

elasticity? (You need to generate the wave2high dummy.)

6. To estimate the inverse demand elasticity, swap log price and log quantity variables in your IV

regression from question 4. Report the inverse demand elasticity estimate and its standard error

with 3 significant digits. Relate the estimate to the IV estimate of demand elasticity. Which

concerns may you have about this estimate, relative to the one in question 4?

1She also used lagged wind speeds (speed2 and speed3) but we won’t.

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7. Coming back to the demand elasticity in question 4, use both wave2 and wave3 as instruments for

price. Report the elasticity and its standard error with 3 significant digits. Test the exogeneity

of the two instruments; report which test statistic you used, which value it takes, and how you

make the conclusion.

8. Are bad weather conditions persistent? Estimate a probit regression of the indicator variable

wave2high from question 5 on its first lag (with the standard controls). What is the estimated

coefficient and its statistical significance? What is the average partial effect of wave2high

yesterday on the probability that wave2high = 1 today? Explain the intuition for your finding.

9. We have so far assumed that heteroskedasticity-robust standard errors were valid, implicitly

assuming no autocorrelation in the errors. To assess this assumption, first generate residuals

from the model you estimated in question 4. Plot the autocorrelation function for the residuals.

What do you observe? Test whether the errors are serially correlated in an AR(1) model. Report

an appropriate test statistic and p-value. For this question, you can assume strict exogeneity.

10. Re-estimate the model in question 4 with heteroskedasticity and autocorrelation robust standard

errors (using the default Newey-West bandwidth). How does the estimated elasticity compare

to that in question 4? How does the p-value compare?

11. How much does the mean (non-logged) price paid by Asian and white buyers differ? Compute

the means of prca and prcw and interpret their difference. Now load the panel version of the

dataset, FISH panel. Rerun the 2SLS regression in question 7 adding the ethnicity indicator as

an exogenous regressor and the interaction of asian with lprc as a second endogenous regressor.

You should also interact the instruments with asian to allow the first-stage coefficients to differ

by ethnicity. Cluster standard errors at the day level. Is the price elasticity significantly different

for Asian and white buyers?