Examining the Causes of Inflation

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. Linear Model with Dummy Variables • 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. Partial Logarithmic Model with Dummy Variables • 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!