1. Explaining Inflation Professor Phillips Econ 240A Final Project Nicholas Burger John Burnett Ryan Carl Anthony Mader Elizabeth Mallon Mickey Sun

2. Objective Determine if inflation can be explained by changes in the M3 money supply, federal funds rate, productivity, and federal budget deficit/surplus Regression model –Dependent variable CPI (1982=100) –Independent variables M3 money supply (billions of dollars) federal budget deficit/surplus (billions of dollars) productivity index (output/hour) federal funds rate (%) –H 0 : 1 = 2 = 3 = 4 = 0 –H A : At least one 0

3. Data Collection Relevant data obtained at http://research.stlouisfed.org/fred Data analyzed quarterly

4. Exploratory Analysis M3 and Output are directly proportional with CPI FFR and Federal Budget Deficit/Surplus are oscillatory while CPI increases

5. Results- Model 1 T-statistic highly significant for all variables but FFR High R 2 value (0.980) and high F- statistic (2781.589) Low Durbin- Watson statistic (0.07)

6. Results- Model 1 Model follows data well up to 1990 Increased deviation between actual and fitted coinciding with 1991-2001 expansion

7. Results- Model 2 First Model t-statistic for FFR did not give evidence for a linear relationship between FFR and CPI We ran the regression without this independent variable to see if it significantly improved the validity of our model.

8. Results- Model 2 T-statistics are highly significant and R 2 value unchanged at 98% F-statistic improved to 4161.575 Durbin-Watson statistic still indicates auto- correlation

9. Results- Model 3 We also attempted to correct for the apparent lack of correlation between CPI and FFR. Changes in the FFR take time to effect the economy (lag time of 9-18 months). Therefore, we shifted the FFR data forward by 9-18 months and regressed against CPI.

10. Results- Model 3 The 9, 12, and 18 month shifts produced t-statistics for FFR of 0.488, 0.412, and 0.3928 respectively. The regression failed to improve the explanatory power of FFR on the behavior of CPI.

11. Results- Model 4 We attempted to correct the auto- correlation present in our model. We ran the regression using the change in each variables value from the previous quarter.

12. Results- Model 4 Coefficient for productivity is negative and the Durbin-Watson statistic increased to 0.57 R 2 decreased dramatically to 0.139 and F- statistic dropped, although still significant at the 5% level

13. Results- Model 5 (The Last One!) In order to correct autocorrelation, we developed another regression model. We added an independent variable to the model that has a time-ordered effect on the dependent variable.

14. Results- Model 5 All variables are linearly related to CPI at the 5% significance level The R 2 value and f-statistic both increased The Durbin- Watson statistic increased

15. Results- Model 5 This final model follows the data most closely of all the regressions investigated as reflected by the actual-fitted- residual curves.

16. Conclusions The CPI is negatively correlated with the federal funds rate and productivity, while the CPI is positively correlated with the government budget deficit/surplus and M3 money supply. In order to achieve an accurate model for the relationship between the dependent and independent variables, a time- ordering variable must be introduced into the regression.