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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.
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nations in the main text (including tables and figures, if any), as well as an appendix with
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to figure out what the problem might be.
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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
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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: - 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. - 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? - 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. - 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. - 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.) - 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.
ECON0019 4 CONTINUED - 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. - 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. - 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. - 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? - 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?
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