1. Examining the Causes of Inflation Robert Kelchen Student Research Conference April 20, 2006

2. Building the Model Data used: Time series from Q1 1980-Q3 2005 Total of 103 observations Data obtained from Federal Reserve and Bureau of Labor Statistics website

3. Variables Examined GDP=Gross Domestic Product ($bil) I=Prime interest rate M=M2 money supply ($bil) W=Employment cost index (1980=100) DEF=National debt ($bil) OIL=Price of a barrel of crude oil

4. Dummy Variables UN=Unemployment rate 0=Rate below seven percent 1=Rate above seven percent EXC=Trade-weighted exchange rate 0=Trade-weighted rate below 100 1=Trade-weighted rate above 100

5. Linear Model with Dummy Variables

6. Concerns: Coefficient for employment cost index is negative Coefficient for exchange rate dummy variable is negative Shows some collinearity among several of the variables

7. Partial Logarithmic Model with Dummy Variables

8. Concerns: Coefficient for employment cost index is still negative Coefficient for exchange rate dummy variable is negative Also shows some collinearity among several of the variables

9. Testing the Models Q4 2005 CPI Inflation: 3.19% Linear Model Prediction: 7.79% Partial Log Model Prediction: 5.83% Look at exogenous variables and model parameters to explain the difference

10. Conclusion Although both models explain a fair amount of the regression, the partial logarithmic model is the better model. The partial logarithmic model consistently has less error in regression, especially in recent years. No model can predict inflation with perfect accuracy!