Econometric Model

Use Eviews to analyze data in “golfdata.wf1” and choose an Econometric Model to analyze the problem in the “Golf Project”. At the same time, thoroughly read and comprehend all the all the other document that I uploaded. You can find the prompt in the documents. The class name is “Introduction to Econometrics”.

Description of Golf Data

The EViews workfile is arranged by golf course, and then by month, so that, for example, the 25th observation in the dataset is for the third golf course and the first month of the year (January). Description of the available data series follows:

ROUNDS: the dependent variable (the quantity variable in your demand function)…ROUNDS measures the number of paid rounds of golf played (at the course in question during the month in question)

FEE: a very important explanatory variable…FEE measures the average price charged by the course to play a round of golf during the month in question

FEESUB: the average price charged by the 21 other courses during the month in question

RAIN: inches of rain for the month

TEMP: average temperature for the month

RATING: a measure of difficulty for the course in question (particularly relevant for low handicap golfers)

SLOPE: another measure of course difficulty (more relevant for higher handicap golfers)

YARD: the length of the course (in yards)

DISTANCE: the minimum distance of the course to either downtown Seattle or Bellevue

CART: a dummy variable that takes on the value of 1 if the course has paved cart paths (important because courses will generally not rent motorized carts during the winter months unless there are paved cart paths)

RANGE: a dummy variable that takes on the value of 1 if the course has a driving range for practice

WINTER: a dummy variable that takes on the value of 1 during the months of Nov., Dec., Jan., and Feb.

MGS: a dummy variable that takes on the value of 1 for the three city-owned golf courses

The workfile contains several other data series, but you will not need them for your analysis.

Golf Project Q&A

Q: How can I find the best model? Which model should I choose?

A: There is no best model in the empirical studies. The tradeoff between the prediction

accuracy and the simplicity of the model is difficult to balance. Several statistics such as

t-statistics, F-statistics and (adjusted) R squared may help you to compare two models.

However, it is common to have the indications of two statistics contradict each other.

Therefore, in this project, we are not looking for the best model, which doesn’t exist at

all. As long as the model makes sense, you can proceed to the following exercise.

Q: What is the residual demand function?

A: The equation or the model that you are trying to estimate is the residual demand

function. On the left hand side of the equation, you have the demand in terms of the

number of rounds played by the golfers. On the right hand side of the equation, you

have the factors that influence the demand such as price, weather, etc.

Q: How to compute the elasticity?

A: The following link provides details about how to compute the elasticity.

https://en.wikipedia.org/wiki/Price_elasticity_of_demand

Q: Why is my formula for computing the price elasticity different from others?

A: The formula depends on the model you choose. Different models will have different

functional forms or different coefficients estimated. It is absolutely normal to have the

price elasticity different from others’.

Q: What is value that I should plug in for calculating the price elasticity?

A: Usually, we plug in the average. However, averages across different groups of

samples provide different implications. For instance, the price elasticity of a particular

golf course during winter can be obtained by plugging in the average of the data over

the course during winter; while the price elasticity of overall golf courses during the

whole year is computed by the average of the data over all courses during the year.

Thus, depending on the specific question that you want to answer, you may use

averages over different samples. It is worth mentioning that the broader sample you use

for the average, the more general and less accurate your result will be, and vice versa.

Q: How can I write the report?

A: Chapter 11.5 would provide you some details about how to write a research report.

However, keep in mind that these are just some guidelines. There is no official rule

about how to structure a report. Write down the thinking process that you have been

through; this would make a good report.

The term paper assignment is to carry out and write up an empirical investigation of an economic topic (very broadly defined) using the methods studied in class. It is highly recommended that you pick a topic of personal interest using original data. Stuff out of textbooks rarely turns out to be interesting. Think small – like can I do something which will get me promoted on the job – rather than large – can I estimate ISLM curves. Really good projects in the paper have involved estimating what kind of personals ads get a strong response, forecasting church attendence, modelling occupancy rates in UW residence halls, forecasting the demand for lift tickets at Snoqualmie, studying cable deregulation, forecasting box office revenues, and estimating the density of Douglas fir trees.

• Good papers usually do more than just describe how an outcome is different between two categories. There is no hard and fast rule about this, but in general think multiple regression.

• One way to frame your thinking is as follows: “I’m going to estimate the following parameters. If the parameters look like this, I will conclude A. In contrast, if the parameters look like that, I will conclude B.” In this way, the framework of your paper is set out before you begin. (You just have to do all the hard work to fill in the details.)

The paper must begin with a one-page Executive Summary, which explains everything one will learn from the paper. The paper proper can be no more than five pages. (The paper should be double-spaced and in a reasonable font size.) You may include technical appendices if you think it necessary – but you cannot assume that I will read past the five pages. The paper proper must be self-contained. You must include an appendix with computer output for all results presented in the paper so I can look at it if I’m curious. This doesn’t count against the five page limit. You must have an EViews file containing your data available to give me, although I don’t generally request them.

A word about the Executive Summary: an Executive Summary is just that – a summary. It is not an introduction. The summary should state what question you asked and tell the answer you found, briefly describing your method. (So it should be redundant to someone who reads the whole paper.) Think of the summary as one page going to your boss. Your boss trusts your work and wants a summary of what she needs to know and no more. Here are two rules about Executive Summaries:

1. It should be possible to understand the paper by reading the Executive Summary and never reading anything else in the paper.

2. Nothing should be lost if the reader skips the Executive Summary and reads the paper instead.

The proposal paragraph you turn in must identify the question you plan to work on AND identifiy specifically the data source you are going to use.

It is okay with me if this paper is part of a project for another class as well, provided you let me know (in advance and by email explaining the extent of overlap). Obviously, you have to clear it with the instructor of the other class as well.