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

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 Greenspan intimates at possibility of deflation (last week). Japanese Deflation and the Great Depression.

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.

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

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.

Trace of Variables

Trace of Stationary Variables

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 DLFFR E-06 DLFFR does not Granger Cause DLGDP DLSP does not Granger Cause DLFFR DLFFR does not Granger Cause DLSP DDLCPI does not Granger Cause DLFFR DLFFR does not Granger Cause DDLCPI DLSP does not Granger Cause DLGDP DLGDP does not Granger Cause DLSP DDLCPI does not Granger Cause DLGDP DLGDP does not Granger Cause DDLCPI DDLCPI does not Granger Cause DLSP DLSP does not Granger Cause DDLCPI Time Causality

Cross Correlogram 1

Cross Correlogram 2

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 e-07 DLGDP(-1) e-05 DLGDP(-3) DLSP(-1) AR(5) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) e-08 Inverted AR Roots i i i i Estimation Output DL Model

Residual Correlogram of the DL Model

Residual Diagnostics

Forecast

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.

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

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 AR(1) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) Estimation Output AR Model

Residual Correlogram AR Model

Residual of the AR Model