1. Forecasting Fed Funds Rate Group 4 Neelima Akkannapragada Chayaporn Lertrattanapaiboon Anthony Mak Joseph Singh Corinna Traumueller Hyo Joon You

2. Background Fed funds rate (FFR) as an instrument of control. FFR as sign of economic strength/weakness. FFR is at 1.25%, the lowest since 1961. Greenspan intimates at possibility of deflation (last week). Japanese Deflation and the Great Depression.

3. Objectives What will happen to the FFR given indicators such as GDP, CPI, stock market price levels, etc? – Create a distributed lag model with FFR as the dependent variable. – Provide one period ahead forecast of FFR. And what does this forecast mean to us? – Provide economic context for the forecast.

4. The Idea Supposing that the Fed made its decision solely on previous FFR would be naive. Fed’s decision on future FFR depends on existing information. We focus on these existing information to explain FFR. – GDP – CPI – SP500

5. Data Standardization All data from Fred II. Different time range and frequencies But same time range and frequencies necessary for DL model Lower bound set by data with the latest start (SP5000 Jan 1970) Upper bound set by data with the earliest end (GDP Jan 2003) Frequency set by data with lowest frequency (GDP quarterly). Result is a shorter and less frequent data set (120 obs). Still enough data.

6. Trace of Variables

7. Trace of Stationary Variables

8. Pairwise Granger Causality Tests Date: 05/27/03 Time: 14:23 Sample: 1970:1 2003:2 Lags: 2 Null Hypothesis:ObsF-StatisticProbability DLGDP does not Granger Cause DLFFR130 12.8145 8.7E-06 DLFFR does not Granger Cause DLGDP 1.75070 0.17788 DLSP does not Granger Cause DLFFR130 7.35499 0.00096 DLFFR does not Granger Cause DLSP 2.07473 0.12989 DDLCPI does not Granger Cause DLFFR129 0.61862 0.54034 DLFFR does not Granger Cause DDLCPI 7.36316 0.00095 DLSP does not Granger Cause DLGDP130 1.16482 0.31534 DLGDP does not Granger Cause DLSP 0.54295 0.58240 DDLCPI does not Granger Cause DLGDP129 3.40096 0.03648 DLGDP does not Granger Cause DDLCPI 2.80740 0.06420 DDLCPI does not Granger Cause DLSP129 1.48890 0.22963 DLSP does not Granger Cause DDLCPI 0.48034 0.61972 Time Causality

9. Cross Correlogram 1

10. Cross Correlogram 2

11. Dependent Variable: DLFFR Method: Least Squares Sample(adjusted): 1972:2 2003:1 Included observations: 124 after adjusting endpoints Convergence achieved after 8 iterations Variable Coefficient Std. Error t-Statistic Prob. C -0.17718962391 0.0332222708495 -5.33345913387 4.6565699798e-07 DLGDP(-1)5.742530598661.3480009163 4.26003464036 4.10881431448e-05 DLGDP(-3)3.127203928681.31542817995 2.37732776016 0.0190325621418 DLSP(-1)0.4295150535450.162069622027 2.65018853116 0.00913844465895 AR(5)0.2469531144840.0878659566173 2.8105665037 0.00578442215627 R-squared0.27792384769 Mean dependent var -0.00836815797483 Adjusted R-squared0.253652380385 S.D. dependent var 0.15270778165 S.E. of regression0.13192640994 Akaike info criterion -1.17365786942 Sum squared resid2.07114473911 Schwarz criterion -1.05993683855 Log likelihood 77.766787904 F-statistic 11.4506405485 Durbin-Watson stat1.79038035224 Prob(F-statistic) 6.75508422109e-08 Inverted AR Roots.76.23+.72i.23 -.72i -.61 -.44i -.61+.44i Estimation Output DL Model

12. Residual Correlogram of the DL Model

13. Residual Diagnostics

14. Forecast

15. Summary Standardization of data for DL modeling causes results in fewer observations. Granger test is useful in isolating independent variables. dlSP500 did not have AR structure. Creating the transformed dependent variable may have been more difficult. Result is more plausible than ARMA model. Fed funds rate will go down next quarter.

16. What Now? Assuming that fed funds will continue to go down, one can… – buy treasury bonds now and sell them later at a higher price when interest rate drops – simply try harder to find a job in the sluggish economy – start a business now in anticipation of next boom

17. Dependent Variable: DLFFR Method: Least Squares Sample(adjusted): 1970:3 2003:1 Included observations: 131 after adjusting endpoints Convergence achieved after 3 iterations VariableCoefficient Std. Error t-Statistic Prob. C-0.014695 0.016465 -0.892533 0.3738 AR(1) 0.166843 0.088232 1.890959 0.0609 R-squared 0.026971 Mean dependent var-0.014326 Adjusted R-squared 0.019428 S.D. dependent var 0.158538 S.E. of regression 0.156991 Akaike info criterion0.850112 Sum squared resid 3.179341 Schwarz criterion-0.806216 Log likelihood 57.68233 F-statistic 3.575725 Durbin-Watson stat 1.961340 Prob(F-statistic) 0.060873 Estimation Output AR Model

18. Residual Correlogram AR Model

19. Residual of the AR Model