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Analyzing the Impact of Federal Funds Rate on Capacity Utilization: A VAR Approach

This lecture explores whether the Federal Funds Rate (FFR) influences economic activity in real terms through capacity utilization in total industry. It includes a preliminary analysis of time series data from January 1967 to April 2008, applying techniques like identification of TCU, Granger causality, and VAR estimation. The study examines mutual causality between changes in FFR and capacity utilization, analyzing autocorrelations and the impact of shocks on both series. The findings reveal complex interdependencies that challenge simplistic interpretations of their relationship.

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Analyzing the Impact of Federal Funds Rate on Capacity Utilization: A VAR Approach

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  1. Econ 240C Lecture 16

  2. Part I. VAR • Does the Federal Funds Rate Affect Capacity Utilization?

  3. The Federal Funds Rate is one of the principal monetary instruments of the Federal Reserve • Does it affect the economy in “real terms”, as measured by capacity utilization for total industry?

  4. Preliminary Analysis • The Time Series, Monthly, January 1967 through April 2008

  5. Capacity Utilization Total Industry: Jan. 1967- April 2008

  6. Identification of TCU • Trace • Histogram • Correlogram • Unit root test • Conclusion: probably evolutionary

  7. Identification of FFR • Trace • Histogram • Correlogram • Unit root test • Conclusion: unit root

  8. Pre-whiten both

  9. Changes in FFR & Capacity Utilization

  10. Contemporaneous Correlation

  11. Dynamics: Cross-correlation Two-Way Causality?

  12. In Levels Too much structure in each hides the relationship between them

  13. In differences

  14. Granger Causality: Four Lags

  15. Granger Causality: two lags

  16. Granger Causality: Twelve lags

  17. Estimate VAR

  18. Estimation of VAR

  19. Specification • Same number of lags in both equations • Use liklihood ratio tests to compare 12 lags versus 24 lags for example

  20. Estimation Results • OLS Estimation • each series is positively autocorrelated • lags 1, 18 and 24 for dtcu • lags 1, 2, 4, 7, 8, 9, 13, 16 for dffr • each series depends on the other • dtcu on dffr: negatively at lags 10, 12, 17, 21 • dffr on dtcu: positively at lags 1, 2, 9, 24 and negatively at lag 12

  21. We Have Mutual Causality, But We Already Knew That DTCU DFFR

  22. Correlogram of DFFR

  23. Correlogram of DTCU

  24. Interpretation • We need help • Rely on assumptions

  25. What If • What if there were a pure shock to dtcu • as in the primitive VAR, a shock that only affects dtcu immediately

  26. Primitive VAR (tcu Notation) dtcu(t) =1 + 1 dffr(t) + 11 dtcu(t-1) + 12 dffr(t-1) + 1 x(t) + edtcu (t) (2) dffr(t) = 2 + 2 dtcu(t) + 21 dtcu(t-1) + 22 dffr(t-1) + 2 x(t) + edffr (t)

  27. Primitive VAR (capu notation)

  28. The Logic of What If • A shock, edffr , to dffr affects dffr immediately, but if dcapu depends contemporaneously on dffr, then this shock will affect it immediately too • so assume b1 iszero, then dcapu depends only on its own shock, edcapu , first period • But we are not dealing with the primitive, but have substituted out for the contemporaneous terms • Consequently, the errors are no longer pure but have to be assumed pure

  29. DTCU shock DFFR

  30. Standard VAR • dcapu(t) = (a1 + b1 a2)/(1- b1 b2) +[ (g11+ b1 g21)/(1- b1 b2)] dcapu(t-1) + [ (g12+ b1 g22)/(1- b1 b2)] dffr(t-1) + [(d1+ b1 d2 )/(1- b1 b2)] x(t) + (edcapu(t) + b1 edffr(t))/(1- b1 b2) • But if we assume b1 =0, • thendcapu(t) = a1 +g11 dcapu(t-1) + g12 dffr(t-1) + d1 x(t) + edcapu(t) +

  31. Note that dffr still depends on both shocks • dffr(t) = (b2 a1 + a2)/(1- b1 b2) +[(b2 g11+ g21)/(1- b1 b2)] dcapu(t-1) + [ (b2 g12+ g22)/(1- b1 b2)] dffr(t-1) + [(b2 d1+ d2 )/(1- b1 b2)] x(t) + (b2edcapu(t) + edffr(t))/(1- b1 b2) • dffr(t) = (b2 a1 + a2)+[(b2 g11+ g21) dcapu(t-1) + (b2 g12+ g22) dffr(t-1) + (b2 d1+ d2 ) x(t) + (b2edcapu(t) + edffr(t))

  32. Reality edtcu(t) DTCU shock DFFR edffr(t)

  33. What If edtcu(t) DTCU shock DFFR edffr(t)

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