Federal Reserve Bank of St. Louis Review | Macro Paper Warehouse

Distinguishing Theories of the Monetary Transmission Mechanism

This 1995 Federal Reserve Bank of St. Louis Review paper by Stephen Cecchetti surveys the empirical literature attempting to distinguish two competing theories of how monetary policy affects the real economy: the “money view,” in which policy works only through its aggregate effect on the required rate of return on investment (with no distributional consequences, since only the least socially productive projects go unfunded), and the “lending view,” which stresses credit-market imperfections — both balance-sheet/financial-accelerator effects (policy-induced interest-rate increases erode borrower net worth, raising external finance premia) and a direct bank-lending channel (policy tightens reserves, forcing loan-dependent banks to cut loan supply) — implying that monetary policy’s incidence differs systematically across borrowers depending on their access to non-bank finance. Cecchetti argues that reduced-form aggregate evidence (relative forecasting power of money versus credit, VAR-based impulse responses of loans versus securities to funds-rate shocks, and timing comparisons of bank loans against commercial paper issuance) is fundamentally incapable of discriminating between the two views, both because monetary policy shocks cannot be measured cleanly (Bernanke-Blinder VAR innovations look implausibly noisy and generate a “price puzzle” in which contractionary shocks raise prices; Romer-Romer narrative dates are neither continuous nor plausibly exogenous) and because aggregate loan-versus-security responses are estimated too imprecisely to reject equal responses. He concludes instead that cross-sectional, firm-level evidence — differential sensitivity of investment or inventories to cash flow across firms grouped by size, dividend policy, or institutional bank-dependence (e.g., Kashyap-Lamont-Stein 1992, Gertler-Gilchrist 1994, Kashyap-Stein 1994b, Calomiris-Hubbard 1993, Fazzari-Hubbard-Petersen 1988) — has convincingly established that credit-market imperfections are quantitatively important and fall disproportionately on smaller, faster-growing, bank-dependent firms, but that this literature has not yet cleanly separated financial-accelerator (balance-sheet) effects from a distinct bank-loan-supply channel, since both mechanisms predict the same qualitative cross-sectional pattern.

Empirical Evidence on the Recent Behavior and Usefulness of Simple-Sum and Weighted Measures of the Money Stock

This 1994 Federal Reserve Bank of St. Louis Review paper by K. Alec Chrystal and Ronald MacDonald asks whether replacing simple-sum monetary aggregates with expenditure-weighted Divisia (or Rotemberg Currency Equivalent) aggregates actually improves money’s empirical usefulness for predicting nominal income and for detecting causal links with real activity, and it tests this question reduced-form, with no structural identification, across seven countries: the United States, United Kingdom, Australia, Germany, Switzerland, Canada, and Japan. The comparison runs on two tracks: modified St. Louis equations (regressing quarterly log-differenced GNP/GDP on current and four lags of log-differenced government spending and money, plus the Treasury bill rate for the US), scored with three non-nested test statistics (the Akaike Information Criterion, the Davidson-MacKinnon J-test, and the Fisher-McAleer JA-test) and exclusion F-tests; and a time-series causality track using Augmented Dickey-Fuller unit-root tests, Johansen cointegration analysis, and heteroskedasticity-robust Granger-type VECM exclusion tests, with country-specific quarterly samples spanning roughly the late 1960s/1970s through 1987-1992. The results are country-specific rather than uniform: for the US, Divisia dominates simple-sum for M2 and broader aggregates (Divisia M2’s F-test for exclusion is F(5,107)=4.73, p=0.001, versus simple-sum M2’s F(5,107)=4.43, p=0.001) but simple-sum still beats Divisia for the narrow M1/M1A aggregates; the UK and Australia show clearer, broader Divisia superiority (Australia is called “probably the clearest case” of Divisia dominance, especially for broad money); Germany and Switzerland show weak informational content for money generally; Canada shows strong Divisia dominance for M2, M3, and L; and Japan is an explicit outlier where simple-sum is favored by the information criterion yet no aggregate, weighted or unweighted, is significant in the F-tests. A US pre-1980 sub-sample (1960:1-1979:3) shows the Divisia advantage present but markedly weaker, consistent with the authors’ interpretation that Divisia’s edge stems from measurement error in simple-sum aggregates that is largest during the post-1980 period of rapid financial innovation. The authors are explicit that their St. Louis-equation results speak only to relative, not absolute, performance of competing money measures, and they conclude that the evidence for Divisia, while real in several countries, is not yet robust enough to recommend it as a direct policy-targeting variable.

Expectations, Open Market Operations, and Changes in the Federal Funds Rate

This paper develops a simple daily model of the U.S. federal funds market to explain how a Federal Open Market Committee (FOMC) announcement of a new target for the federal funds rate can move the actual rate almost the full distance to the new target on the same day, even though the New York Fed’s Trading Desk typically conducts no open market operation designed to bring that change about until the following day. The model has two parts: a “Trading Desk reaction function” (drawing on the supply-side literature) in which the Desk adjusts the supply of Fed balances the day after the effective funds rate deviates from target, and a demand for Fed balances (building on recent microeconomic work by Furfine 2000a and by Guthrie and Wright 2000) in which banks’ demand today depends in part on their rational expectation of tomorrow’s funds rate. Because traders know the Desk will act tomorrow if today’s rate has not yet converged to target, the anticipation of that future action shifts today’s demand for balances and moves today’s rate immediately – a mechanism Taylor labels, following Guthrie and Wright’s New Zealand terminology, an “open mouth operation.” Simulating the calibrated model, Taylor shows a 50-basis-point target increase can move the effective rate by roughly 42 basis points on the announcement day alone, with the residual gap closing geometrically over the following days as the Desk’s actual (lagged) reserve adjustments catch up; the same model, applied to a demand shock rather than a target change, reproduces the observed speed with which funds-rate deviations from target revert to zero. Using daily 1998-2000 U.S. data, Taylor also documents that the volatility of funds-rate deviations from target declined and their day-to-day persistence rose over this period, particularly around actual target changes, and that the target rate Granger-causes the funds rate far more consistently than the reverse – patterns broadly consistent with the model’s predictions, though Taylor cautions the model’s timing is sharper than what the data actually show and that some anticipated target changes visible in fed funds futures markets produce little detectable movement in the funds rate itself.

Monetary and Fiscal Actions: A Test of Their Relative Importance in Economic Stabilization

This 1968 Federal Reserve Bank of St. Louis Review article by Leonall Andersen and Jerry Jordan — later known as the “St. Louis equation” — tests three commonly held propositions that fiscal actions have a larger, more predictable, and faster influence on economic activity than monetary actions, using reduced-form regressions of quarterly changes in GNP (1952:Q1-1968:Q2) on changes in the money stock or monetary base and on high-employment government expenditures and receipts. None of the three propositions is confirmed by the evidence: coefficients on money and the monetary base are consistently larger, more statistically reliable (higher t-values and partial coefficients of determination, ranging .38-.53 versus near-zero for expenditures), and no slower to appear than those on fiscal measures, while high-employment expenditure and tax-receipt coefficients are mostly small and statistically insignificant. In an illustrative simulation suggested by Milton Friedman, a $1 billion increase in government spending financed by borrowing or taxation raises GNP by only $170 million after four quarters, whereas an equal $1 billion increase in the money stock (holding the budget position fixed) raises GNP by $5.8 billion — and financing the same $1 billion spending increase entirely through money creation produces the identical $5.8 billion permanent GNP increase, which the authors attribute entirely to the monetary expansion. The paper explicitly frames these findings as “not proven true” in a strict scientific sense — only “not refuted” by the test period’s evidence — but argues they nonetheless support placing substantially greater reliance on monetary rather than fiscal actions for economic stabilization, including a set of GNP projections under alternative money-growth-rate assumptions for 1968-69.

Structural Approaches to Vector Autoregressions

This 1992 Federal Reserve Bank of St. Louis Review paper (a policy review, not peer-reviewed) by John W. Keating is an expository survey of structural vector autoregression (VAR) methods that shows how a reduced-form VAR, x_t = β(L)x_{t-1} + e_t, can be derived from an underlying structural simultaneous-equations model, Ax_t = C(L)x_{t-1} + Dz_t, and then develops two families of identifying restrictions needed to recover the structural parameters in A and D from the estimated reduced-form residual covariance matrix Σ_e = A^{-1}DΣ_εD’A^{-1’}: contemporaneous restrictions on the impact matrix A (of which the Choleski, or recursive, decomposition is a special, atheoretical case) and long-run restrictions on the cumulative multiplier matrix θ(1), following the Blanchard-Quah (1989) approach, which let the data determine short-run dynamics while theory constrains only shocks’ permanent effects. Keating illustrates both strategies with a 4-variable quarterly U.S. system — the GNP deflator, real GNP, the 3-month Treasury bill rate, and M1, all first-differenced, over 1959:Q1-1991:Q3 with 4 lags — specifying a contemporaneous model (a predetermined price/aggregate-supply equation, an IS equation, a money-supply reaction function, and a buffer-stock money-demand equation) and a long-run model that restricts aggregate-supply shocks to be the sole source of permanent output movements. For the long-run model only, the paper reports estimated structural shock standard deviations (aggregate supply 0.0144, IS 0.0092, money demand 0.0149, money supply 0.0172, all significant at 5%), finds the long-run money-demand and money-supply coefficients statistically significant at 5% while the long-run IS coefficient on output is not, and shows via impulse responses (with Runkle 1987 Monte Carlo confidence bands) that supply shocks raise output permanently and lower prices, IS shocks raise output only temporarily but raise prices and the interest rate permanently, money-demand shocks have essentially no effect on output, prices, or rates, and money-supply shocks have a “relatively small” effect on output that peaks at 13 percent of its variance about two years out; variance decompositions show supply shocks explaining 90 percent of output variance at a 48-quarter horizon. For the contemporaneous model, by contrast, all ten estimated structural coefficients (Table 3) are statistically insignificant even though several carry theory-consistent signs (money-demand coefficients of roughly one-half on nominal spending and almost -1.0 on the interest rate; a positive coefficient on money in the money-supply/interest-rate equation, read as evidence the Fed “attempts to stabilize money growth by raising interest rates”), and its impulse responses (Figures 5-8) and variance decomposition (Table 4) mix expected patterns — supply shocks dominating output and price variance, IS shocks dominating short-run output movement and long-run interest-rate variance, much as in the long-run model — with patterns Keating calls “inconsistent with most macroeconomic theories,” namely the money-demand shock’s effects concentrated on long-run output and short-run interest-rate variance rather than money itself, and the money-supply shock’s negligible effect on output paired with a large, persistent effect on real money. Keating’s own comparison concludes that “wherever a significant discrepancy exists between the two models, the model with long-run restrictions yields sensible results, while the results from the contemporaneous model are inconsistent with standard economic theories,” and that long-run parameters are estimated more precisely; he attributes this to macro theories often sharing long-run properties while differing in short-run dynamics, and to long-run identification avoiding contemporaneous exclusion restrictions that his 1990 paper argues can be invalid under rational expectations, while flagging that further research is needed to know whether this result generalizes beyond the paper’s illustrative system.