A Shadow Policy Rate to Calibrate U.S. Monetary Policy at the Zero Lower Bound

This 2018 International Journal of Central Banking paper by Marco J. Lombardi and Feng Zhu proposes a “shadow policy rate” for U.S. monetary policy that is purely statistical rather than derived from a term-structure model, addressing the problem that once the federal funds rate is stuck at the zero lower bound (ZLB) it stops reflecting the stance of policy. Using monthly U.S. data from January 1970 to June 2016 organized into four blocks – interest rates (the effective federal funds rate, Treasury bill rates at one, three, and six months, Treasury bond yields at one, two, five, ten, and twenty years, and the OIS-LIBOR spread), monetary aggregates (M0, M1, M2, MZM), Federal Reserve balance-sheet assets (total assets, securities held outright, and their maturity composition), and reserves (total, excess, required) – the authors estimate a dynamic factor model with an arbitrary pattern of missing data, using the EM/Kalman-filter algorithm of Bańbura and Modugno (2014). Eight factors, chosen by the Hallin-Liska (2007) information criterion, explain 90.5 percent of the total variance of the data set (the first three factors alone account for almost 70 percent, with the first factor – linked to the interest-rate block – explaining about 38 percent, the second – linked to the monetary base – about 20 percent, and the third – linked to the size of Fed securities holdings – about 11 percent); the lag order is set to two by the Schwarz information criterion. The shadow federal funds rate is then recovered by treating the FFR and other short rates as missing once they reach the ZLB (from December 2008 for the FFR and three- and six-month bills, November 2009 for one- and two-year yields) and letting the model’s Kalman smoother impute the latent rate from its historical co-movement with the rest of the still-observed monetary data, including the balance sheet. The shadow rate tracks the effective FFR closely before the crisis, turns negative in early 2009, and – because it is directly driven by Fed balance-sheet changes rather than only the Treasury yield curve – registers substantial stimulus from the first large-scale asset purchase program (LSAP1, November 2008) that term-structure shadow rates (Krippner 2013a; Wu and Xia 2016) largely miss, since LSAP1 targeted mortgage-backed securities rather than Treasuries. The authors report the shadow rate delivered its greatest stimulus over 2011, dropping below -5 percent in August before becoming less accommodative and then loosening again from October 2012; a formal stability test finds the historical relationship between the funds rate and macro variables (real GDP, inflation) is not disrupted at the ZLB when the shadow rate is used (LR = 1.94, p = 0.38) whereas it is rejected when the observed FFR is used (LR = 15.77, p = 0.00). Benchmarked against Taylor (1993) and Taylor (1999)/balanced-approach rules, unconventional measures are found to have filled a substantial part of the post-2009 policy gap, broadly in line with Taylor (1993) but not fully sufficient under Taylor (1999), especially using the CBO unemployment gap – bearing on Bullard’s (2012, 2013) claim, based on a term-structure shadow rate, that policy became excessively loose in 2012. Finally, substituting the shadow rate for the actual FFR in two standard recursive-Cholesky monetary VARs (Bernanke-Blinder 1992; Christiano-Eichenbaum-Evans 1996, quarterly data 1970-March 2016, four lags) produces monetary policy shocks that clearly show sizable post-2008 easing timed with LSAP2 and LSAP3 and even a tightening shock after the 2014 taper, whereas shocks identified from the actual FFR are small and only mildly negative and so understate the true extent of stimulus. The authors describe the Section 4 exercises throughout as purely illustrative and caution the results should not be taken as conclusive.

Feng Zhu | Macro Paper Warehouse

A Shadow Policy Rate to Calibrate U.S. Monetary Policy at the Zero Lower Bound