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

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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

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

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

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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)

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

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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.

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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

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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.

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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.

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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.

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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

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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.

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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

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

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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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