Assessing Changes in the Monetary Transmission Mechanism: A VAR Approach

This 2002 FRBNY Economic Policy Review paper by Jean Boivin and Marc Giannoni asks whether the transmission of monetary policy shocks to U.S. output and inflation changed after the early 1980s, and if so, whether the change reflects a shift in the propagation mechanism (how shocks work their way through the economy), a change in the size of shocks hitting the economy, or specifically a change in how the Federal Reserve systematically conducts policy. Using quarterly U.S. data from 1963:Q1 to 1997:Q4 on detrended output (a high-pass filter approximating the output gap), GDP-deflator inflation, commodity-price inflation (added to limit the price puzzle), and the federal funds rate, the authors estimate a four-variable, four-lag recursive (Cholesky) VAR in the Christiano-Eichenbaum-Evans (1999) style, ordering the funds rate last so it does not affect the other variables contemporaneously, and split the sample into pre-1980 (1963:Q1-1979:Q3), post-1980 (1980:Q1-1997:Q4), and post-1984 (1984:Q1-1997:Q4, excluding the 1979:Q4-1983:Q4 nonborrowed-reserves-targeting episode) sub-periods around the Volcker (1979:Q4) and McConnell-Perez-Quiros Great Moderation (1984:Q1) break dates. Andrews sup-Wald tests reject stability of the reduced-form VAR coefficients in 6 of 16 tests (38%), and chi-squared tests reject stability of the innovation covariance matrix in 5 of 8 tests overall (63%, pooling both break dates) — rejecting in all 4 of 4 variables at the 1979:Q3 break but only 1 of 4 at the 1984:Q1 break, indicating pervasive instability in both the propagation mechanism and shock variances. A reduced-form counterfactual variance decomposition finds that changed propagation coefficients account for roughly 38% (pre- vs. post-1980) or 22% (pre- vs. post-1984) of the decline in detrended-output variance, and roughly 58% (pre- vs. post-1980) or 56% (pre- vs. post-1984) of the decline in inflation variance, with the remainder attributed to reduced shock variance; the authors’ own rounded headline figures (“roughly 40 percent” for output, “60 percent” for inflation) refer specifically to the pre- vs. post-1980 comparison. Structural variance decompositions show monetary policy shocks’ contribution to output variance falling from 19% (pre-1980) to 7% (post-1980) to 3% (post-1984), and to inflation variance from 14% to 10% to 6%, even though the estimated variance of the policy shock itself is larger in the post-1980 sample (0.61 vs. 0.30 pre-1980, attributed to the 1979-83 nonborrowed-reserves episode) before falling to 0.14 post-1984 – suggesting the change concerns how policy shocks propagate, not merely their size. Impulse responses to an unexpected 1-percentage-point (100-basis-point) funds-rate increase are “much less pronounced and persistent” after 1980, with the output response trough “more than twice as large” pre-1980 as in either later sample. The paper’s central counterfactual then separates the model’s coefficients into a systematic-policy-rule block (denoted Phi) and a rest-of-economy block (denoted Omega), finding that replacing only the pre-1980 policy rule with the post-period rule (holding Omega fixed) reproduces the post-period impulse responses closely, whereas replacing only Omega “changes only little” – leading the authors to conclude that most of the reduction in output and inflation responses is accounted for by a change in the parameters characterizing systematic monetary policy. Via a simple IS-curve model they interpret this as more consistent with a more aggressive, stabilizing policy rule than with a genuine weakening of the interest-rate transmission channel, while explicitly noting that the VAR counterfactual alone cannot separately identify the two.

Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach

This 2005 Quarterly Journal of Economics paper by Ben Bernanke, Jean Boivin, and Piotr Eliasz proposes the Factor-Augmented VAR (FAVAR) to address a specific problem with standard small monetary VARs: because Fed policymakers actually condition on a wide range of economic and financial data, a VAR built from only a handful of observable series omits conditioning information, which biases the identified policy shock (producing the “price puzzle,” a spurious rise in prices after a tightening), makes the choice of which few variables to include arbitrary, and limits impulse responses to only the included series. The FAVAR augments a standard VAR in observable variables (in the benchmark specification, just the federal funds rate) with a small number of latent factors extracted by principal components from a large panel of 120 monthly U.S. macroeconomic series (DRI/McGraw Hill Basic Economics Database, January 1959-August 2001); the factors and the observable are then modeled jointly in a VAR (13 lags, 3 factors in the benchmark), with the policy shock identified recursively (Cholesky) by ordering the federal funds rate last, and with the factors themselves estimated in a first step using only “slow-moving” variables that do not respond contemporaneously to policy shocks, so as not to contaminate the factor estimates with the policy shock itself. Two estimation approaches are compared: a computationally simple two-step principal-components procedure (following Stock and Watson 2002), which the authors prefer for its “greater plausibility,” and a one-step Bayesian likelihood approach via Gibbs sampling (10,000 draws, 2,000 discarded as burn-in) that is more rigorous but yields qualitatively similar, quantitatively more imprecise results. Moving from a standard three-variable VAR (industrial production, CPI, funds rate), which produces a strong and persistent price puzzle, to a FAVAR with even a single additional factor considerably reduces the puzzle; the preferred three-factor, two-step-PC FAVAR generates impulse responses for many more series that are broadly consistent with standard theory (real activity and prices eventually decline, money aggregates fall, the dollar appreciates on impact), and a five-factor version leaves these qualitative conclusions unchanged. At a 60-month horizon the identified policy shock accounts for a sizeable share of the variance of interest rates (45.4% of the funds rate itself, 43.3% of the 3-month T-bill, 40.3% of the 5-year bond) but a much smaller share of real activity and prices (5.4% of industrial production, 3.8% of CPI) and of monetary aggregates (0.5% of both the monetary base and M2); the authors note that IRFs for the monetary aggregates should be interpreted with caution given the low R² of their common components (10.4% and 5.2%, respectively), and they hedge their central claim, describing the price-puzzle result as offering only “some support” for the view that the puzzle stems from omitted conditioning information rather than a definitive resolution. The sample ends in August 2001, before the financial crisis and the zero lower bound, so the paper does not speak to whether the FAVAR’s resolution of the price puzzle extends to that later, constrained-policy environment.

Jean Boivin | Macro Paper Warehouse

Assessing Changes in the Monetary Transmission Mechanism: A VAR Approach

Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach