I need 2 projects done.

DescriptionChapter 5: Simple Regression
The workhorse of economic statistics is the regression. It is used primarily to test theories to
see whether the data supports a hypothesis and to forecast future values, like stock prices or inflation.
In this chapter we will look at a simple regression where we look at the relationship between Y the
dependent variable and X the independent variable. In general we say that X determines Y.
Y = f (X )
Y =  0 + 1 X + U
Where  0 and 1 are the y-intercept and slope respectively. U is the error term. In class we show how
to calculate these parameters and to determine if they are statistically significant. In this project we will
understand how Excel and SAS calculate the parameters. We will further see how to use the output
from these programs to do analysis.
Example: GDP and Oil
One theory we can test is whether increases in oil prices is related with lower GDP. The idea is
that the US is a net oil importer and that when the price of oil goes up it costs more to produce goods so
GDP will be lower. In its simplest form the model is:
GDP = 0 + 1Oil + U
We would expect the slope will be negative and the y intercept a positive number. We can get the data
from a number of sources. Once source is the Economic Report of the President where we got this data.
We can put the data into Excel for the years 1959 to 2015.
Regression in Excel
We can use what we learned in the last four projects to do a regression analysis. The first set is to get
the data into a useable form. Each of the variables need to have the same number of observations. The
data set we will use here will look like this.
The variables are:
1959 to 2015
Gross Domestic Product
Money Supply
Interest Rate (1 Year Treasury)
Unemployment Rate
Labor Force Participation Rate
Oil Price per barrel (Texas)
We will use a number of these variables in this and the next project. Notice that the labels are in row 1
and that there are no rows between the label and the data. This will be important later.
The simplest way to run a regression in Excel is to use the Data Analysis box under data.
This is the same box that we used in doing descriptive statistics and histograms in past projects. One of
the selections is regression. This is what we are going to use. Once you select regression and click ok.
You should see the following box:
In this case select the X and Y data by going to the data set and choosing GDP for Y and OIL for X.
Notice that the Label box is checked and that the data starts with the variable names in cells B1 and G1.
Also the New Worksheet button is checked which will put the results in a new worksheet, not with the
Once you click OK a new worksheet will appear with the results. There will be two tables one with
ANOVA and the other with the regression coefficients. These are the results we can use to test our
hypothesis that when oil price rise GDP will fall.
Let’s look at the results to see what they say. First, the Adjusted R2 is .638 (in blue) this says that the
change in oil prices explain 63.8% of the change in GDP. The F statistic (in red) is greater that the critical
value – rule of thumb is if it is greater than 5 then the Adjusted R2 is statistically significant.
Next turn to the coefficients (in orange), OIL is positive and says that for every dollar oil prices rise GDP
goes up by $128 billion. This is the opposite of what we expected. Next we see if this coefficient is
significant and it is with a t statistic of 10 (in green) – the rule of thumb for t statistics is anything greater
than 2 is significant.
So what is wrong with are theory? What might be another theory that would explain this? (Hint: Time)
Single Functions:
If all we wanted was the coefficient 1 Excel has a formula for that called linest:
Notice that data is first for Y and then for X separated by a comma. This will return 128.05.
Will give you  0 5458.06 just as the data analysis does.
You can also get the R2 use a formula called RSQ:
This returns .645 just as above. The only problem is we have no easy way to get a t value for the
Example of a regression using functions alone.
Here we have a calculation for the t test. In most cases using the Data Analysis Regression package will
be the easiest way to obtain regression results in Excel.
Regression in SAS
So the first step is to import the data using the import function. Remember if you are having
problems importing the data using the Excel workbook option try the Excel Workbook on PC files server
Then proceed as normal, click next, then give your data file a name like GDP.
Next you pick the worksheet with your data, remembering the first row are variable names and the
second row starts the data.
The last step is to give your dataset a name so SAS can find it.
Then click Finish. You should now have your data set ready to be used by SAS. If you look on the far left
under Explorer/Libraries/Work you should find your data with the name you gave it. If you click on it
you should see the data set. It is always good to check.
Next, turn to the Editor on the lower right of the program where again we will write the code that runs
the regression for us. Here we will do a simple regression, just as we did in Excel. We will look at the
relationship between GDP and Oil prices.
The first set is to create a new data set GDP1 from your original dataset GDP. (Note you do not have to
do this here but in the future if you want to create new variables this step will be helpful) The second
two lines give descriptive statistics, something we didn’t do in Excel but is always a good thing to do.
The last two lines run the regression for us.
Calls the regression program and tells SAS to run it.
The model statement tells SAS what the regression equation is. (Y variable = X variables). The run
commands just make sure each part of the program runs independently. Once you have typed in the
program as pictured above click on the running man button above in SAS. SAS will then take a few
seconds and then show you the output.
The first thing not reproduced here is the univariate output which tells you the mean, median, mode
and other statistics. The PROC REG output starts and gives the ANOVA with the R2 colored in purple and
the F stat in yellow. The parameter estimates (in green) and the t statistics (in red). Notice that they are
the same numbers we obtained in Excel.
SAS then gives a number of graphs to help you better understand the regression and the error terms.
These graphs allow us to see if the regression assumptions hold. You can see if there is a relationship
between the error terms, whether the errors are normally distributed and if the X (OIL) is independent
of the errors.
The last graph plots the trend line and the 95% confidence level. Again we can see the positive
relationship between OIL and GDP.
With just two lines all of these results are obtained in SAS. In the next chapter we will expand this
model to have multiple X variables and use other tests to ensure we have not violated any of our
So we have a problem with our hypothesis. We were expecting a negative relationship but
instead it was positive. We might be worried that over time everything goes up and this might be
messing up the real relationship. One way to see if this is true is to look at the differences in GDP,
(GDP70-GDP69) and OIL (OIL70-OIL69), so we are looking at the change in GDP and OIL not GDP and OIL
itself. We saw a similar problem before when we indexed the data in our Graphing Chapter.
There are a number of ways to create differences. First you could just do it in Excel as part of
the data set.
DGDP is for difference in GDP for 1960 it is 3078.4-3052.2 =26.2. You will do the same for DOIL
expect the function will be =G3-G2. Once you have put these into I3 and J3 you can copy this for the
rest of the years. Notice you lose one year (1959) which is blank.
You can also create the difference in SAS, here I will call the two new variables GDPD and OILD
so as not to confuse them with the variables you created in Excel.
The editor program will look like this:
It is important to have the run command before you add the proc reg because you need to
create the variables before you can use them. To create the variables we use the lag command and the
just do the subtraction to get the difference. If you look at the dataset GDP1 in explore you will see that
GDPD and DGDP are the same.
Now let’s look at the results from our new regression. What can we say about the parameters
and the test statistics?
The R2 has dropped to 4.36% and the F stat tells us this is not significantly different from zero,
i.e., there is no relationship between GDP and oil prices. The parameter is still positive but the t statistic
says that we cannot reject the hypothesis that beta1 is zero. So time is important and it might be that
the positive relationship is because everything goes up with inflation. This is a cautionary note, that
even when the regression looks good we must be careful!
1. Look at the relationship between GDP and Exports. Normally we would think there should be a
positive relationship. The more you export the more money you make and the higher GDP. Run
a regression in both Excel and SAS.
2. Explain the results you obtained, specifically explain:
a. R2
b. F
c. Parameters
d. T stat
3. What can you say about this relationship? Is our hypothesis correct? Why?
State University of New York
College at Buffalo
Department of Economics and Finance
ECO 424 W
Overview: The project involves four parts. The first part of the project is to find
the data you want to use. Second, you must produce a theory which can be tested
using a regression. The third thing is to run a SAS program which will estimate
the regression equation developed in the two parts of the project. The final part of
the project is to write up the theory and the statistical results in a 3 page paper.
Data Sets: During the semester we have found and used a number of data sets on
the internet. You can use any of these or pick one of your own. Note if you are in
senior seminar, double dipping is allowed so you might want to use the same
dataset. Some examples are:
American Factfinder:
Federal Reserve Fred:
Federal Reserve (Survey of Consumer Finance):
NBER Datasets (A large number of datasets):
CDC Surveys:
Economic Report of the President:
Developing a Theory: The theory which you develop will depend on which of
the data sets you use. After choosing a data set you must come up with a theory
which can be tested by running a regression. An example of a theory might be
that GDP (Y) is a function of the money supply (M), past GDP (Y-1) and real
interest rates (r), which can be written as:
Y = 0 + 1 M + 2 Y-1 + 3 r + U
For your theory you must have one dependant (Y) and three
independent (M, Y-1 , r) variables. You can create any theory which you
want. Do not worry about whether your theory is correct, you will not be
graded on the theory but on how you present the theory and the statistics.
Analyzing the Theory: Any theory which you develop must involve the
determination of the sign pattern and the magnitude for each of the
coefficients, for example 0 might be assumed to be positive and about the
same size as last year’s GDP.
3 on the other hand should be negative, or
that GDP goes down as interest rates go up. The signs of the betas are what
makes up your theory and is what you will test when you run your SAS
SAS Project: Copy the data set which you have chosen from my account, as you
have done in the other computer projects this semester. Then use the following
Proc Reg;
Model Y = X1 X2 X3 /DW WHITE VIF;
Proc Autoreg;
Model Y = X1 X2 X3 / nlag=1;
Proc Corr;
Var X1 X2 X3;
Proc Means;
Var Y X1 X2 X3;
These statements will produce the regression results or will help in explaining and
testing your theory. Notice that these statements are all part of projects 4 and 5
which you will have completed before you start your project. If you are having
problems running your computer program review what you did in your past
Statistical Results: The results which you obtain will test your theory. Did you
get the signs on the parameters, which you expected? If not can you explain why
you didn’t get the expected results. Are the size of the parameters what you
predicted and again if not why. You will also have to test to see if the parameters
are statistically significant by using a t-test. You will also want to test the
explanatory power of your theory using the R2 and the F-test. In order to make
sure that your model follows the standard assumptions, you must also use the
Durbin Watson test to rule out serial correlation, a plot of the residuals to check
for heteroscedasticity and the variance covariance matrix to check for
multicollinearity (VIF).
Paper Outline: The paper should follow this general outline.
Introduction and Explanation of Theory (1/2 page)
Analysis of Theory (1/2 page)
Sign of parameters
Size of parameters
Other aspects of theory
Statistical Results (1 page)
Regression Results
Test of parameters
Test of the model
Test for problems with the model
Explanation of statistical results
Other statistical results (such as mean of variables)
Interpreting Results (1/2 page)
Comparing Original Results with Actual Results
Reinterpreting the Theory with the Statistical
Forecasting using results
Concluding Statements ( 1/2 page)
a. What did you learn about your theory?
Note: if you want to add extra graphs and charts that is ok, the
minimum requirement is 3 pages. In the end you want to have a
report which is easy to understand and explains what you did.
The Paper is due at the exam, you may turn it in early and I will add comments so you
can rewrite the paper.

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