ECON 5420 Lecture Notes - Lecture 6: Federal Reserve Economic Data, Prediction Interval

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Published on 5 Nov 2018
School
Ohio State University
Department
Economics
Course
ECON 5420
Professor
HOMEWORK 6 Econ 5420, Fall 2017
This homework requires the use of the data set forecast on Carmen. The data includes
the following variables from the Federal Reserve Economic Data base:
construction Seasonally adjusted construction Spending
manufacturing s2i Ratio of sales to inventory in Manufacturing
unemp Unemployment Rate
tbill3mo 3 Month Treasury Rate
month Month
year Year
t Created to set time
1. We want to estimate Unemployment as a function of each of the other variables
(not month, year, t). Begin by looking for obvious time trends and first difference
any variable with a time trend. Which variable(s) has a time trend? (There
should be no seasonal trends. The data is seasonally adjusted. )
2. Conduct Dickey-Fuller Tests on your variables. Which variables have unit roots?
Report a DF stat for each. Take first differences on those with unit roots.
3. Run a regression with your detrended, stationary variables only. Report the
results.
4. Use lagged values1of the variables used in number 3 to create a forecast for
unemployment in December of 2016. Find a confidence interval for that prediction.
(To find the values that you will use in the regression look in at the data editor.
August 2016 is the last month. ) Report the regression results.
5. Now create a forecast using the level values despite the fact that they contain
trends and unit roots. Add lagged values of each variable until the last lag is
no longer statistically significant, including lags of unemployment. Repeat the
exercise of finding a prediction and confidence intervals. Write out the forecasting
equation, report the regression results, and show the prediction interval. The true
value is 4.9%. How close was your estimate?(Note: The answer that you get will
depend on which variables you add in which order. All reasonable answers will be
accepted. You can even add lagged errors and time trends if they are significant.)
1The values must be lagged because we are predicting outside our sample. We do not know what
any variables are in December 2016.
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