A Model of Monetary Policy Shocks for Financial Crises and Normal Conditions

This 2019 Journal of Money, Credit and Banking paper by John Keating, Logan Kelly, A. Lee Smith, and Victor Valcarcel asks whether replacing the federal funds rate with Divisia M4 (DM4) – a user-cost-weighted monetary aggregate – as the policy indicator in a Christiano-Eichenbaum-Evans (1999)-style block-recursive structural VAR can identify monetary policy shocks that behave sensibly in both normal times and financial-crisis/near-zero-lower-bound conditions, when standard funds-rate-based shocks are prone to implausible (“puzzle”) responses. The empirical model partitions macro variables into three ordered blocks – a slow-moving block (real GDP, the GDP deflator, a commodity price index), the policy indicator (DM4), and a fast-responding money-market block (the monetary base and DM4’s user cost) – estimated by Bayesian Gibbs sampling (following Sims and Zha 1999) with a noninformative prior and 90% probability intervals, at 5 lags quarterly (13 lags in a monthly robustness check), across three overlapping samples: 1967:Q1-1995:Q2 (replicating the original CEE funds-rate sample), 1967:Q1-2007:Q4 (precrisis), and 1967:Q1-2015:Q4 (full sample including the 2008-09 crisis and the ZLB period). A complementary small-scale New Keynesian model with money shows that when the Fed’s estimated interest-rate rule displays high inertia (rho approximately 0.94, estimated by nonlinear GMM on 1967-2007 quarterly data), its policy dynamics can be closely replicated by a non-inertial money-growth rule reacting to output and inflation – providing theoretical grounding for using DM4 in a Cholesky-ordered VAR even though the Fed never formally targeted it – whereas a low-inertia Taylor (1993) rule is not well replicated by a money-growth rule. Empirically, the DM4-based VAR produces no output, price, or liquidity puzzles in any of the three samples: GDP falls in a significant, hump-shaped (U-shaped) pattern for three years after a contractionary shock, the price level declines with a lag (becoming significantly negative after about two years), DM4 itself falls on impact with a significant liquidity effect, and its user cost rises on impact – a pattern the authors describe as “remarkably stable” across samples and broadly similar to the CEE funds-rate benchmark in the precrisis sample. By contrast, VARs built on three shadow short rates (Lombardi-Zhu, Krippner, Wu-Xia) all generate persistent, statistically significant price puzzles, with two of the three also generating liquidity puzzles. The cumulative identified DM4 shocks crest around QE1-QE3 and register the 2013:Q2 taper tantrum as a large negative shock, and a historical decomposition finds monetary policy shocks after 2008 were mildly net expansionary (real GDP 0.48% and the GDP deflator 0.50% higher than they otherwise would have been), with monetary policy shocks accounting for 8.44% (90% interval 1.73%-18.78%) of real GDP forecast-error variance at 8 quarters and 6.47% (0.66%-17.04%) of GDP-deflator variance at 20 quarters. A Great Depression counterfactual – applying the 1929:Q3-1937:Q3 pace of M2 collapse to DM4 starting in 2007:Q4 – implies a hypothetical 12.65% peak-to-trough GDP decline and 4.1% deflation, versus the actual -4.35% GDP decline and +0.26% inflation, which the authors interpret as evidence that post-2008 Fed actions, while not large departures from its historical policy rule, prevented a much deeper contraction. Long-run policy-rule coefficients on inflation are negative and significant in all three samples (-1.83, -2.26, and -0.88 respectively across the three samples) while the output-growth coefficient stays small and insignificant throughout, which the authors read as the Fed behaving “as if” it stabilized inflation via broad money growth. Results are robust to a monthly-frequency VAR (only 1 of 102 responses falls outside the quarterly model’s 90% bands, though the monthly liquidity effect is not statistically significant) and largely, though not entirely, to substituting DM2 for DM4 (DM2 shows a small, statistically insignificant price puzzle and a rising monetary base in the full crisis-inclusive sample). The authors attribute DM4’s puzzle-free performance in the crisis period to two features: its Divisia expenditure-weighting, which (per a DSGE decomposition) avoids the aggregation bias that afflicts simple-sum aggregates like M1 and M2 when asset substitutability is time-varying, and its breadth, since DM4 includes institutional money-market funds, repos, commercial paper, and Treasury bills – assets specifically targeted by the Fed’s 2008-09 liquidity facilities (PDCF, TSLF, CPFF, ABCPMMMFLF).

The financial market effects of unwinding the Federal Reserve's balance sheet

This 2023 Journal of Economic Dynamics and Control paper by A. Lee Smith and Victor J. Valcarcel asks whether the financial-market effects of shrinking (“unwinding”) the Federal Reserve’s balance sheet are simply quantitative easing (QE) in reverse, studying the Fed’s 2014-2019 balance-sheet normalization episode with U.S. data only and no DSGE model. The paper combines three empirical strategies: a reduced-form regression of the federal-funds/IOR spread on log reserves, where the liquidity-effect coefficient rises in magnitude from -0.086 (s.e. 0.009) during the 2009Q1-2014Q3 expansion to -0.299 (s.e. 0.015) during the 2014Q4-2019Q3 normalization – an effect roughly three-and-a-half times larger in magnitude, attributed to the zero lower bound having put a floor under the spread during QE while no symmetric ceiling constrained it during QT; a two-day event study around 11 quantitative-tightening announcements (January 2008-December 2018, 4,016 daily observations) that splits 2 “taper” announcements from 9 “unwind” announcements, finding that while 9 QE announcements produced large, statistically significant cumulative declines (-1.49 percentage points on the 10-year Treasury yield, -3.70pp on MBS, -1.00pp on 8-quarter Eurodollar futures), the 11 QT announcements produced no significant cumulative effect on any of these series, and within QT only the 2 taper announcements – not the 9 unwind announcements – had statistically significant effects across the yield curve and other assets; and a block-triangular structural VAR that orders reserves first, identifying “reserve supply shocks” tied to the Fed’s own balance-sheet decisions rather than to Treasury-bill supply (which affects reserves only with a one-week settlement lag), estimated both as a Bayesian time-varying-parameter VAR (October 2015-October 2019, following Cogley and Sargent 2005 and Primiceri 2005) and, over the Asset Runoff sample alone (October 2017-August 2019), as a constant-parameter Bayesian SVAR for each of several individual asset prices. The TVP-VAR shows that negative reserve-supply shocks produced no significant response in the Goldman Sachs Financial Conditions Index during the September 2014-September 2017 Full Reinvestment phase but a significant financial-tightening response emerging in early 2018 during the October 2017-August 2019 Asset Runoff phase, with the FF-IOR spread’s response to reserve shocks increasing roughly threefold in magnitude between the two phases; the granular asset-level SVAR finds that a negative one-standard-deviation reserve-supply shock raises the 10-year Treasury yield by roughly 4 basis points within 2-4 weeks, with the VAR-implied term premium accounting for nearly all of that increase (suggesting a term-premium channel rather than revised short-rate expectations), raises BBB corporate and MBS yields by comparable amounts (BBB remaining elevated near 2bp at 28 weeks), raises the broad dollar index by about 0.3% around week 8, and produces only a small, imprecisely estimated, statistically insignificant decline in the S&P 500; and a counterfactual simulation against continued full reinvestment implies the balance-sheet unwind raised the 10-year Treasury yield by a peak of approximately 40 basis points around mid-2018 and by approximately 8 basis points on average over the full Asset Runoff period. The paper’s central conclusion is that unwinding tightened financial conditions even in the absence of significant announcement effects, but transmitted through liquidity and implementation channels rather than through the signaling and duration-driven announcement channels that dominated QE – an asymmetry the authors attribute to the FOMC’s deliberate post-taper-tantrum strategy of communicating balance-sheet normalization as separate from the policy-rate path (Yellen’s 2017 “watching paint dry” characterization) and to the absence of a zero-lower-bound-style ceiling on rates during reserve drawdowns. The sample deliberately stops short of the September 2019 repo-rate spike because of endogeneity concerns about aggregate reserve dynamics, so the authors note the results may understate the ultimate tightening effect as reserves approached scarcity.

A. Lee Smith | Macro Paper Warehouse

A Model of Monetary Policy Shocks for Financial Crises and Normal Conditions

The financial market effects of unwinding the Federal Reserve's balance sheet