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Explaining Inflation

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

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Explaining Inflation

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  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 (%) • H0: 1 = 2 = 3 = 4 = 0 • HA: 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 R2 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 R2 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 variable’s value from the previous quarter.

  12. Results- Model 4 • Coefficient for productivity is negative and the Durbin-Watson statistic increased to 0.57 • R2 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 R2 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.

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