程序代写代做 graph ECON0019: QUANTITATIVE ECONOMICS AND ECONOMETRICS EMPIRICAL PROJECT 2020
ECON0019: QUANTITATIVE ECONOMICS AND ECONOMETRICS EMPIRICAL PROJECT 2020
Instructions
The mark for this essay is worth 5% of your total mark for the module.
You will be awarded a mark of 0% or Grade F if you (1) do not attempt the summative assess- ment 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 are a re-sitting student or taking deferred assessment the academic regulations for 2017/18 apply to you. In this case if you do not complete or take an assessment component that is worth more than 20% of the total assessment you will be considered incomplete. This means that you cannot pass the module. If this is your first attempt, you may be entitled to LSA in the component. Please dis- cuss with the Departmental Tutor (f.witte@ucl.ac.uk) if you are unsure of the consequences for you.
If you have extenuating circumstances that affect your ability to engage with any of the module assessment 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 assessments. 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 lecturer about this.
Please follow these instructions so that we can ensure anonymity in marking and ensure compli- ance 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 1pm on Mar 30, 2020.
3. All marking on Turnitin is anonymised. Do not put your name or student number anywhere on your submitted answer – either in the document or in the file name.
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4. Submit the project with the submission cover sheet on the first page. Put the Candidate Numbers for all group members on the submission coversheet. Your Candidate Number is not you student number!
5. You should include your Stata commands in the appendix. If you use a different software for the project, you should state which programme was used and present your code and results in the appendix. Your essay should be no more than 800 words in length, including footnotes but not including bibliographies, tables or figures, but excluding the appendix which should include your Stata commands. You must state the number of words at the top of the first page of your essay.
6. Upload your document on Turnitin as a word document or pdf. PLEASE make sure to allow sufficient time should problems arise with Turnitin.
Here is some additional information about submissions and marking:
1. If your essay is longer than 800 words the following Faculty guidelines on penalties for over-long work will be applied:
• For work that exceeds a specified maximum length by less than 10% the mark will be reduced by five percentage marks, but the penalised mark will not be reduced below the pass mark, assuming the work merited a Pass.
• For work that exceeds a specified maximum length by 10% or more the mark will be reduced by ten percentage marks, but the penalised mark will not be reduced below the pass mark, assuming the work merited a Pass.
2. Late work will be marked but will be subject to UCL rules as set out in Section 13.12 of the Aca- demic Manual: https://www.ucl.ac.uk/academic-manual/chapters/chapter-4-assessment -framework-taught-programmes/section-3-module-assessment#3.12. For the avoidance of doubt, a working day means a 24-hour period from the 1pm deadline.
3. It is your responsibility to ensure that your work is your own. Action will be taken if there is any plagiarism concern, including failure to provide a complete reference list with your work. See Section 13.14 of the Academic Manual for more information on the consequences of the work not being your own: https://www.ucl.ac.uk/academic-manual/chapters/chapter-4 -assessment-framework-taught-programmes/section-3-module-assessment#3.12
4. If you are normally entitled to Reasonable Adjustments, such as extra time in exams, you may be entitled to extra time for this Assessed Essay. You will need to have a SORA in place for this to be taken into account. Please see Section 5 of the Academic Manual for the process to follow if you have not already done so: https://www.ucl.ac.uk/academic-manual/chapters/chapter-4- assessment-framework-taught-programmes/section-5-reasonable-adjustments. Make sure to fol- low the process as early as possible in Term 1. The responsible marker will know which candidate
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numbers have a SORA and this will be taken into account when reviewing the timing of sub- missions. 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 lecturer about this.
5. Check the submission inbox for confirmation that your essay has been submitted. Once your submission has been accepted you will return to the ’My Submissions’ tab where you will be able to see the details of your submission. If your submission is not confirmed for some reason, or you are having issues uploading the document, get in touch with ISD (servicedesk@ucl.ac.uk) as soon as possible to figure out what the problem might be.
Any matters affecting your ability to submit on time should be directed to the Departmental Tutor (f.witte@ucl.ac.uk) rather than the module lecturer to ensure anonymity is retained.
QUESTION:
A cartel known as the Joint Executive Committee (JEC) controlled the rail transport of grain from the Midwest to Eastern cities in the United States during the 1880s. The cartel preceded the Sherman Antitrust Act of 1890, and it legally operated to increase the price of grain above what would have been the competitive price. From time to time, cheating by members of the cartel brought about a temporary collapse of the collusive price setting agreement. In this exercise, you will use variations in supply associated with the cartels collapses to estimate the elasticity of demand for rail transport of grain. The Stata data file ECON00192020.dta contains weekly observations on the rail shipping price and other factors from 1880 to 1886.
The main variables in the dataset are:
week – the week of observation. week = 1 if 1/1/1880-1/7/1880, week = 2 if 1/8/1880-1/14/1880, , week = 328 for final week
price – weekly index of price of shipping a ton of grain by rail.
cartel – dummy variable, = 1 railroad cartel is operative, = 0 otherwise.
quantity – total tonnage of grain shipped in the week
seas1,….,seas12 – twelve month dummy variables. To match the weekly data, the calendar has been divided into 13 periods, each approximately 4 weeks long. Thus: seas1 = 1 if date is January 1 through January 28, =0 otherwise; seas2 = 1 if date is January 29 through February 25, =0 other- wise; seas13 = 1 if date is December 4 through December 31, =0 otherwise. Since it is redundant, the last dummy variable, seas13, has been left out from the data set.
To manage the data in Stata you will need to declare that we are working with a time series ob- served at a weekly frequency starting in the first week of 1880. The following lines will achieve that:
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generate time=tw(1880w1)+ n-1 format time %tw
sort time
tsset time
Answer the following questions:
1. Plot the two time series of log(price) and log(quantity). Do you see any seasonal patterns?
2. De-seasonalise log(price) and log(quantity). That is, regress log-prices and log-quantities on the month dummy variables and save the resulting residuals in logp and logq, respectively. Do the original log-price and log-quantity time series have significant seasonal effects?
3. Plot the de-seasonalised price and quantity time series and compare each of them with the time series plot of the corresponding raw time series.
4. Estimate an AR(1) model for logp and an AR model for logq. In other words, estimate logpt = γ0 + γ1logpt−1 + εt
and similarly for logq. Report the first 10 sample autocorrelations for logp and logq, respectively. In other words, report the sample correlations between logp in week t and logp in week t − k for k = 1,…,10 (and analogously for logq). Comment on how the first autocorrelation compare with the slope coefficient in the regression above. Are the autocorrelations consistent with an AR(1) model for these variables? (Hint: The lag of variable logp is obtained as L.logp in Stata and analogously for logq. Use the command corrgram for the correlogram.)
5. Let Yt be 1 if quantityt is above 23,000 in week t and zero otherwise. Estimate the following model:
Yt =1[α0 +α1logpt +vt ≥0]
where vt follows a standard normal distribution. Compute the Average Partial Effect and Partial
Effect at the Average and discuss their interpretation. Hint: This is a Probit model.
6. Suppose that the demand curve for rail transport of grain is specified as
logqt = β0 + β1logpt + ut Estimate the coefficients above via OLS.
7. The interaction between supply and demand may bias the OLS estimator. Consider using the variable cartel as an instrumental variable for logpt. Provide first-stage estimates for this regression. Is cartel a weak instrument? What are the TSLS estimates for the coefficients above?
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