Most economics departments require second- or third-year undergraduate students to complete an econometrics project and write a paper on their findings. Years later, I still remember how stressful my first econometrics project was, so I decided to write the guide to econometrics projects and term papers that I wish I had when I was a student. Before we begin, we must choose a topic.

### Choosing an Econometrics Project Topic

Many students find that choosing a research topic for their required econometrics project is just as difficult as the project itself.

To help, I wrote an article titled, "Econometrics Research Topics and Term Paper Ideas," that provides great starting points for an undergraduate econometrics project.

For this econometrics project guide, let's test whether Okun's law holds in America. This project guide will not include a multivariate regression, so if your class holds that as a requirement for your project, be sure to check out "How to Do a Painless Multivariate Econometrics Project."

### The Test

Now with our topic chosen, let's get some background information about the theory we're testing. The article "Instant Econometrics Project - Okun's Law" will provide the information we need to get started. From reading the article, we see that we want to see how close our data matches the following function: **Y _{t} = 1 - 0.4 X_{t}**

Where:

Yt is the change in the unemployment rate in percentage points

Xt is the change in the percentage growth rate in real output, as measured by real GNP

So we will be estimating the model: **Y _{t} = b_{1} + b_{2} X_{t}**

Where:

Y_{t} is the change in the unemployment rate in percentage points

X_{t} is the change in the percentage growth rate in real output, as measured by real GNP

b_{1} and b_{2} are the parameters we are trying to estimate.

To estimate our parameters, we'll need data.

The page Quarterly Economic Data has all the information we need. To use this information, I would suggest saving each of the files individually and following the instructions on the bottom of the screen. If you've done everything correctly, you should see something that looks like this.

Now that you've downloaded the data and opened Excel, we can go onto the next section where we'll find the data we need.

**Be Sure to Continue to Page 2 of "Your Guide to a Painless Undergraduate Econometrics Project. "**

### Finding the Y-Variable

Now that we've got the data file open, we can start to look for what we need. First, we need to locate the data for our Y variable. Recall that Y_{t} is the change in the unemployment rate in percentage points. The change in the unemployment rate in percentage points is in the column labeled UNRATE(chg), which is Column I. By looking at column A, we see that our quarterly unemployment rate change data runs from April 1948 to October 2002 in cells G24-G242.

### Finding the X-Variable

Now we need to find our X variables. In our model, we only have one X variable, X_{t}, which is the change in the percentage growth rate in real output as measured by real GNP. We see that this variable is in the column marked GNPC96(%chg), which is in Column E. This data runs from April 1947 to October 2002 in cells E20-E242.

### Setting Up Excel

We've identified the data we need so we can now compute the regression coefficients using Excel. If you are not restricted to using a particular program for your regression analysis, I'd recommend using Excel. Excel is missing a lot of the features a lot of the more sophisticated econometrics packages use, but for doing a simple linear regression, it is a useful tool. You're also much more likely to use Excel when you enter the "real world" than you are to use an econometrics package, so being proficient in Excel is a useful skill.

Our Y_{t} data is in cells G24-G242 and our X_{t} data is in cells E20-E242. When doing a linear regression we need to have an associated X entry for every Y_{t} entry and vice-versa. The X_{t}'s in cells E20-E23 do not have an associated Y_{t} entry so we will not use them. Instead, we will use only the Y_{t} data in cells G24-G242 and our X_{t} data in cells E24-E242.

Now that we have located the data we need, we can calculate our regression coefficients (our b_{1} and b_{2}). Before continuing I suggest saving your work under a different filename so at any time, you can revert back to your original data.

Now that you've downloaded the data and opened Excel, we can go onto the next section in which we'll calculate our regression coefficients.

**Be Sure to Continue to Page 3 of "Your Guide to a Painless Undergraduate Econometrics Project ."**

### Setting Excel Up for Data Analysis

Now onto the data analysis. Go to the **Tools** menu on the top of the screen. Then find **Data Analysis** in the **Tools** menu. If **Data Analysis** is not there, then you'll have to install it. To install the Data Analysis ToolPak see these instructions. You cannot do regression analysis in Excel without the Data Analysis ToolPak installed.

Once you've selected **Data Analysis** from the **Tools** menu, you'll see a menu of choices such as "Covariance" and "F-Test Two-Sample for Variances." On that menu, select **Regression**.

Once there, you'll see a form that looks like this. Now we need to fill this form in.

### Computing Regression Coefficients with Excel

The first field we'll need to fill in is the **Input Y Range**. This is our unemployment rate data in cells G24-G242. You can choose these cells by typing "$G$24:$G$242" into the little white box next to **Input Y Range** or by clicking on the icon next to that white box then selecting those cells with your mouse. The second field we'll need to fill in is the **Input X Range**. This is our % change in GDP data in cells E24-E242. You can choose these cells by typing "$E$24:$E$242" into the little white box next to **Input X Range** or by clicking on the icon next to that white box then selecting those cells with your mouse.

Lastly, we'll have to name the page our regression results will go on. Make sure you have **New Worksheet Ply** selected and in the white field beside it type in a name like "Regression." Now we're done, and your form should like this.

When that's completed, click on **OK**.

### Using the Regression Results

You should now see a tab on the bottom of your screen called **Regression** (or whatever you named it) and some regression results. Your regression results should look like this. I've x'ed out the actual results in this screenshot because you'll have to do your own work, but if you've gotten the intercept coefficient between 0 and 1, and the x variable coefficient between 0 and -1, you've likely done it correctly.

With this data, you now have all the data you need for analysis including R Square, coefficients, standard errors, etc.

We were looking to estimate our intercept coefficient b_{1} and our X coefficient b_{2}. Our intercept coefficient b_{1} is located in the row named **Intercept** and in the column named **Coefficient**. On my sheet, it has the value "AAA" and the associated standard error "CCC" (see here). Our slope coefficient b_{2} is located in the row named **X Variable 1** and in the column named **Coefficient**. On my sheet, it has the value "BBB" and the associated standard error "DDD." Make sure you jot these figures down (or print them out) as you will need them for analysis.

Now you've got the regression results you need, you'll need to analyze them for your term paper. You can see how to do so in the follow-up article: Hypothesis Testing Using One-Sample t-Tests.