Explaining InflationObjectiveData CollectionExploratory AnalysisResults- Model 1Slide 6Results- Model 2Slide 8Results- Model 3Slide 10Results- Model 4Slide 12Results- Model 5 (The Last One!)Results- Model 5Slide 15ConclusionsExplaining InflationExplaining InflationProfessor PhillipsEcon 240A Final ProjectNicholas Burger John BurnettRyan CarlAnthony MaderElizabeth MallonMickey SunObjectiveObjectiveDetermine if inflation can be explained by changes in the M3 money supply, federal funds rate, productivity, and federal budget deficit/surplusRegression 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 ≠ 0Data CollectionData CollectionRelevant data obtained at http://research.stlouisfed.org/fredData analyzed quarterlyExploratory AnalysisExploratory AnalysisM3 and Output are directly proportional with CPIFFR and Federal Budget Deficit/Surplus are oscillatory while CPI increases 02000400060008000100000 50 100 150 200CPIM3-400-20002004000 50 100 150 200CPISURPLUS_DEFICIT051015200 50 100 150 200CPIFFR4060801001201400 50 100 150 200CPIOUTPUTResults- Model 1Results- Model 1T-statistic highly significant for all variables but FFRHigh R2 value (0.980) and high F-statistic (2781.589)Low Durbin-Watson statistic (0.07)Results- Model 1Results- Model 1Model follows data well up to 1990Increased deviation between actual and fitted coinciding with 1991-2001 expansion-60-40-2002005010015020025060 65 70 75 80 85 90 95 00Residual Actual FittedResults- Model 2Results- Model 2First Model t-statistic for FFR did not give evidence for a linear relationship between FFR and CPIWe ran the regression without this independent variable to see if it significantly improved the validity of our model.Results- Model 2Results- Model 2T-statistics are highly significant and R2 value unchanged at 98%F-statistic improved to 4161.575Durbin-Watson statistic still indicates auto-correlationResults- Model 3Results- Model 3We 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.Results- Model 3Results- Model 3The 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.Results- Model 4Results- Model 4We 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.Results- Model 4Results- Model 4Coefficient for productivity is negative and the Durbin-Watson statistic increased to 0.57R2 decreased dramatically to 0.139 and F-statistic dropped, although still significant at the 5% levelResults- Model 5 (The Last Results- Model 5 (The Last One!)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.Results- Model 5Results- Model 5All variables are linearly related to CPI at the 5% significance levelThe R2 value and f-statistic both increasedThe Durbin-Watson statistic increasedResults- Model 5Results- Model 5This final model follows the data most closely of all the regressions investigated as reflected by the actual-fitted-residual curves. -15-10-5051005010015020060 65 70 75 80 85 90 95 00Residual Actual FittedConclusionsConclusionsThe 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
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