Explaining Inflation

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 (1982100)
• 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.stlouisf
  ed.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
  variables 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.