The Dynamic Impacts of Monetary Policy: An Exercise in Tentative Identification

This 1994 Journal of Political Economy paper by David B. Gordon and Eric M. Leeper proposes a structural vector autoregression (SVAR) that identifies monetary policy shocks by modeling supply and demand simultaneously in the reserves market and, separately, in the M2 market, rather than treating a single monetary aggregate or interest rate as predetermined the way standard Cholesky, nonborrowed-reserves, or federal-funds-rate identification schemes do. In each market a demand equation (the monetary aggregate as a function of its own interest rate, the price level, and output, with the interest elasticity a_1 restricted negative) and a supply equation (the interest rate as a function of the aggregate, the 10-year Treasury yield, and a commodity price index, with elasticity a_4 restricted positive) are estimated jointly by maximum likelihood, identified by two restrictions: private demanders of money observe only the current interest rate, prices, and output within the month (the long-term rate is excluded from demand), while the Federal Reserve observes current financial-market variables (funds rate, 10-year yield, commodity prices) but not current goods-market data (price level, output, unemployment) because of a roughly one-month reporting lag, so those goods-market variables are excluded from the supply/Fed-behavior equation; the goods-market block itself is treated as block-recursive, unaffected contemporaneously by either money market. Using monthly U.S. data over 1982:12-1992:4 with six lags, the estimated interest elasticity of reserves demand is -0.028 (se 0.014) and the reserves supply elasticity is +30.28 (se 10.95); for M2 the demand elasticity is -0.0082 (se 0.0007) and the supply elasticity is +253.84 (se 10.79); the reserves-market model narrowly fails an overidentification test (chi-squared(5)=11.864, p=.04, which the authors describe as “not… a strong statistical rejection”), while the M2-market model comfortably passes (chi-squared(5)=2.715, p=.74). A one-unit expansionary supply shock in the reserves market produces a liquidity effect – the funds rate falls sharply and reserves rise – that lasts about 8 months before the rate climbs back above baseline after roughly two years, with the price level and industrial production rising significantly within about six months and unemployment falling with roughly a three-month lag, responses the authors call “fully consistent with traditional analyses,” in contrast to the liquidity-puzzle and price-puzzle results the paper documents earlier from reduced-form and single-variable-innovation identifications. The analogous M2 supply shock produces qualitatively similar but “less significant” responses, and a regression linking the two shows the M2 supply shock loading on current and lagged reserves supply shocks with R-bar-squared only 0.19, implying that a substantial share of M2 supply-shock variation reflects nonpolicy behavior of financial institutions rather than monetary policy, so the authors caution that inference from M2-based studies “should be drawn cautiously.” Variance decompositions show policy shocks explaining a large share of reserves and funds-rate forecast-error variance on impact but declining quickly, while their share of price-level and output variance rises to roughly 30 percent by three years out. When the same reserves-market model is re-estimated on 1971:1-1979:9 data, the identifying restrictions “fail to identify demand and supply relationships for reserves in the 1970s”: the demand elasticity flips to the wrong sign (+0.0069) and neither elasticity differs significantly from zero, which the authors attribute to a different Federal Reserve operating procedure in that earlier period rather than to a flaw in the identification strategy itself, whereas the M2-market model still recovers sensible elasticities in the 1970s but now strongly rejects overidentification (chi-squared(5)=14.958, p=.01).

What Does Monetary Policy Do?

This 1996 Brookings Papers on Economic Activity paper by Eric Leeper, Christopher Sims, and Tao Zha asks what monetary policy actually does, and argues that the answer the identified-VAR literature had been giving was fragile because different papers used different samples, variable sets, and identification schemes; the authors instead estimate an escalating sequence of Bayesian structural VARs – first small 4- and 5-6-variable systems that reinterpret existing recursive and non-recursive identifications (Strongin’s NBR/TR system, Christiano-Eichenbaum-Evans’s NBR system), then integrated 13- and 18-variable systems – all on one common monthly U.S. data set (January 1960-March 1996, six lags, quarterly series interpolated via Chow-Lin) so that conflicting conclusions can be checked for robustness on equal footing. Identification proceeds through a mix of exact zero restrictions, probabilistic (“soft-zero”) priors, and informal plausibility screening on impulse responses, organized around a sectoral block structure that splits variables into slow-moving private (“P”), fast-moving auction-market information (“I”), Federal Reserve policy (“F”), and banking (“B”) blocks, with the Fed’s reaction function restricted to respond within the month only to fast-moving financial information, not to CPI or GDP (which are measured with a lag); estimation uses a Bayesian “reference prior” (Sims-Zha) that downweights explosive, poorly-identified dynamics, and published error bands are 68% (about one standard error) probability bands from posterior simulation, not classical confidence intervals. Across the model sequence, a policy-tightening shock in the integrated 13- and 18-variable systems produces a broadly coherent contractionary picture – short and long rates rise, reserves and M1 fall smoothly, output and its components fall, unemployment rises, commodity prices fall, and the dollar appreciates – but the CPI response is very small (briefly slightly positive, only weakly negative after several years), and policy shocks account for only a modest share of output and (in the 13-variable model) a “negligible” share of CPI variance, while a separate private-sector shock – one the model allocates to the private sector with nothing in its estimated form suggesting misallocation, and which the authors judge it “unlikely” is “mistakenly incorporat[ing] much of an expansionary monetary policy disturbance” – is nonetheless the dominant source of M1 and reserves variation, showing that most of the observed variation in these aggregates is genuinely nonpolicy in origin and so is unsatisfactory as a one-dimensional policy indicator. Specifications that imply large real effects of policy (notably Strongin’s NBR/TR system, where policy shocks explain about half of output variance at three-plus-year horizons) tend to come bundled with an implausible sustained price-level rise after a contraction (a severe price puzzle), which the authors read as a sign of policy misspecification rather than a genuine large policy effect; the CEE NBR system, by contrast, produces believable but small real effects. The authors’ robust cross-specification conclusions are that only a modest portion (sometimes essentially none) of U.S. output and price variance since 1960 traces to monetary policy shocks, that most of the observed variation in policy instruments and monetary aggregates is the systematic, endogenous response of policy to the state of the economy rather than exogenous disturbance, that treating reserves or a monetary aggregate as moving mainly in response to policy is therefore unreliable, and that specifications implying large real effects tend to be exactly the ones with implausible price responses. The authors caution that their identified policy shocks may still be absorbing some adverse-supply-shock variation (which would understate price effects and overstate output effects), that the published hard-zero restrictions are a computationally tractable substitute for soft-zero priors the authors initially found more plausible, and that with small changes in specification a shock formally labeled “monetary policy” can rotate into an information-sector shock and vice versa – a fragility warning central to the paper’s message.

Eric M. Leeper | Macro Paper Warehouse

The Dynamic Impacts of Monetary Policy: An Exercise in Tentative Identification

What Does Monetary Policy Do?