eco pp4

ECO 309

Economic Forecasting

Project Part 4

TURN IN ONLY A SINGLE WORD DOCUMENT AND WRITE YOUR NAME ON EACH PAGE

1. Remind me if Xs are highly correlated (scatter plots, VIF, correlations). If you have sign switch, correct the

Situation by throwing one of the variables out of the model? Consider R-squared or adj R-squared when making the decision.

2. Using the scatter plots you generated, identify any nonlinear relationships between Y and X variables.

Try to correct nonlinearity through transformation (page 233-237). If it works, keep the transformed version of the variable. Otherwise, use the original variable, acknowledge the nonlinearity and move on to the next test. Use 2 different transformations (ex: Log X, 1/X, X^2 or SQRT(X)).

3. Once you correct for nonlinearity and multicollinearity, check for autocorrelation using DW test. Do you have autocorrelation? Correct for autocorrelation if you have any.

4. Incorporate seasonal dummies and trend into your model. Identify if you have seasonality, trend by checking their significance? Is that consistent with your previous findings?

5. Once you corrected for all possible problems, rewrite your final equation, INTERPRET the equation, and forecast y, for 35th in sample observation.

6. Analyze the resulting residuals (4-in-11 plot in MINITAB)

7. How does regression analysis perform compared to univariate methods you have learned? Create a table that includes the MSD for the univariate models (Trend, Smoothing, Decomposition) and MSE of regression model. (HINT 1: You don’t have to try ALL univariate models. Use your knowledge of your data. For example: If your revenue variable is trending, no need to run single smoothing. Or, if it is linear, no need to run nonlinear trend models etc. HINT 2:Your final is around the corner, no harm in reviewing the previous material ahead of time, either.)

You have an extra credit opportunity for PP4 on the heterokedasticity  (non-constant error variance) discussion.  You can pick option #1 and use scatter plot and residual analysis to diagnose heterokedasticity. OR, you can choose option #2 where you need to do the White's heterokedasticity test in addition to everything in #1. Correct execution of Whites test will give you extra credit points.

Part #7 of PP4 has been modified to give you more direction. You will need to create a table to compare the MSE from regression analysis to MSD from univariate methods to see if regression outperformed the univariate methods or not.  This will involve running the trend, smoothing, and decomposition methods on your revenue variable and presenting them in a table