Simon Gilchrist | Macro Paper Warehouse

Chapter 21 The financial accelerator in a quantitative business cycle framework

This Handbook of Macroeconomics chapter develops a dynamic general equilibrium model – since widely known simply as “BGG” – that embeds a credit-market friction into an otherwise standard New Keynesian business-cycle framework in order to formalize and quantify the “financial accelerator”: the idea that endogenous developments in credit markets amplify and propagate, rather than merely reflect, shocks to the macroeconomy. The mechanism rests on a costly-state-verification (Townsend 1979) debt contract between risk-neutral entrepreneurs and financial intermediaries, in which lenders must pay an auditing cost to observe a borrower’s realized, privately known idiosyncratic return; this generates an external finance premium – the wedge between the cost of external and internal funds – that depends inversely on the borrower’s net worth relative to the capital being financed, since low-net-worth borrowers pose greater default risk and hence greater expected agency (monitoring) costs. Because net worth in the model is itself procyclical (rising with profits and asset prices in expansions), the external finance premium is countercyclical, so a positive shock raises net worth, lowers the finance premium, and further stimulates investment and asset prices, which raises net worth again – a feedback the authors formalize in the key relation QK = Ψ(s)N linking a firm’s capital expenditure to its net worth via a premium-dependent multiplier Ψ. Embedding this contracting problem in a Calvo-style sticky-price New Keynesian model with money and, in extensions, one-period investment decision lags and heterogeneous firms with differential credit access, the calibrated model shows the financial accelerator delivers economically large amplification and propagation: in response to an unanticipated 25-basis-point cut in the nominal interest rate, the initial output response is about 50% larger and the investment response nearly twice as large with the accelerator present than in an otherwise identical baseline with the finance premium held fixed at its steady-state level, and the real effects are far more persistent (output and investment four quarters after the shock, with the accelerator, are roughly where they stand after only two quarters without it); the external finance premium itself falls sharply and only slowly reverts to trend, consistent with the paper’s own VAR evidence that credit-spread variables rise ahead of the downturn following a contractionary policy shock. In the two-sector extension with heterogeneous credit access, investment by the more credit-constrained sector rises by nearly three times as much as investment by the better-credit-access sector after an expansionary shock, an “excess sensitivity” result the authors relate to net worth being endogenous in their model, in contrast to models such as Fisher (1996) in which borrowers’ equity positions are exogenously fixed.

Credit Spreads and Business Cycle Fluctuations

This 2012 American Economic Review paper by Simon Gilchrist and Egon Zakrajšek constructs a new corporate-bond credit spread index – the “GZ spread” – from a large panel of secondary-market bond prices (346,126 bond-month observations, 5,982 securities, 1,112 US nonfinancial firms, January 1973 to September 2010), matching each bond’s own cash flows to a hypothetical Treasury security with identical cash flows to compute the spread, which avoids the “duration mismatch” that afflicts conventional maturity-bucketed indexes like the Baa-Aaa spread or the paper-bill spread. The GZ spread substantially outperforms these standard indicators as a predictor of future economic activity: in monthly forecasting regressions it is “statistically a highly significant predictor” of payroll employment, unemployment, and industrial production at both short and long horizons, raising adjusted R-squared by 12-15 percentage points at the 12-month horizon, with a 100 basis point increase associated with about a 3 percentage point (annualized) decline in industrial production growth over the next three months; in quarterly regressions a 100 basis point increase in the GZ spread predicts real GDP growth more than 1.25 percentage points lower over the following four quarters. The authors then decompose the spread using a credit-spread pricing model that regresses individual bond spreads on a firm’s Merton/Bharath-Shumway distance-to-default and on bond characteristics (with additional term-structure and volatility controls for callable bonds), defining the “excess bond premium” (EBP) as the gap between the actual average spread and the value predicted by this regression – i.e., the component of pricing unexplained by measured default risk. The central finding is that the EBP, not the predicted (default-risk) component, carries essentially all of the GZ spread’s forecasting power for real activity: in the post-1985 subsample the predicted GZ spread has no forecasting power for real GDP (coefficient -0.023, t = 0.20) while the EBP remains highly significant (t = 6.80), with a 100 basis point EBP increase predicting roughly a 2 percentage point GDP decline over four quarters. In a recursively identified eight-variable quarterly VAR (consumption, business fixed investment, real GDP, GDP deflator inflation, EBP, equity excess return, 10-year Treasury yield, federal funds rate, ordered so EBP shocks affect the real economy only with a lag), a one-standard-deviation EBP shock (about 20 basis points) produces output that bottoms roughly 0.5 percentage points below trend five quarters out, a sharper and more persistent investment decline, a cumulative stock market decline of about 7 percentage points, appreciable disinflation, and monetary policy easing beginning about one quarter after the shock; EBP shocks account for more than 10 percent of output variance and more than 25 percent of business-fixed-investment variance at business-cycle frequencies, proportions the authors note exceed those typically attributed to monetary policy shocks. Additional evidence – a close correlation between the EBP and the Senior Loan Officer Opinion Survey measure of bank credit-standard tightening, an inverse relationship with financial-sector return on assets, and a broker-dealer VAR in which adverse shocks to broker-dealer profitability raise broker-dealer CDS spreads together with the EBP – supports interpreting the EBP as a measure of the financial sector’s risk-bearing capacity and credit-supply conditions, consistent with financial-accelerator and intermediary-asset-pricing theories, though the paper is careful to describe its forecasting results as evidence of predictive content rather than as establishing causality from credit spreads to economic activity, and its recursive VAR ordering is an identifying assumption checked only by limited robustness exercises (alternative orderings, monthly frequency) rather than by formal tests.

Monetary Policy, Business Cycles, and the Behavior of Small Manufacturing Firms

This 1994 Quarterly Journal of Economics paper by Mark Gertler and Simon Gilchrist asks whether small manufacturing firms bear a disproportionate share of the manufacturing decline that follows monetary tightening, and whether that disproportion reflects financial frictions (a balance-sheet channel and a bank-lending/credit channel) rather than nonfinancial differences such as industry composition or technological flexibility. Using quarterly, size-class-disaggregated U.S. manufacturing data from the Census Bureau’s Quarterly Financial Report (QFR), 1960:1-1991:4, with “small” firms defined as roughly the bottom 30 percent of sales at each date (a weighted blend of two nominal asset classes straddling that cutoff, adjusted for firms drifting across nominal classes over time), the authors proceed in three stages: descriptive plots of smoothed growth rates and log deviations around Romer-Romer narrative tight-money dates; reduced-form bivariate and multivariate (adding real GNP growth, inflation, and the federal funds rate) VARs estimated by SUR across size classes, with F- and t-tests for the significance and cross-size-class difference of the Romer-dummy coefficients; and structural GMM estimation of an inventory-adjustment (Euler-type) equation augmented with a balance-sheet proxy, the “coverage ratio” (cash flow relative to the short-term interest burden on short-term debt). The reduced-form results show Romer dates strongly and significantly predict declines in sales, inventories, and short-term debt for small firms but have no significant marginal predictive power for large firms; quantitatively, small firms account for an estimated 55 to 60 percent of the drop in total manufacturing sales four, eight, and twelve quarters after a Romer episode despite being by construction only about 30 percent of total sales, and large firms actually accumulate inventories (apparently borrowing to smooth production) while small firms shed them sharply, dragging total manufacturing inventories negative by twelve quarters out. The federal-funds-rate effect on small firms is also asymmetric across the business cycle: the coefficients switch significantly between high- and low-GNP-growth states for small-firm sales (p=0.03) and inventories (p=0.01), but show no significant switching for large firms, and the effect is concentrated in low-growth (recessionary) states. In the structural GMM equations the lagged coverage ratio enters significantly for small firms (coefficient around 1.4 to 1.8 depending on specification) but is insignificant and wrong-signed for large firms (-0.70), and its estimated effect on small firms roughly doubles moving from a high- to a low-growth state (1.21 versus 2.36), though the authors note this cyclical asymmetry is significant only at the 20 percent level; a one-standard-deviation fall in the coverage ratio is estimated to reduce small firms’ annual inventory growth rate by roughly 2.25 percentage points, against an annual inventory growth standard deviation of about 8 percent for small firms. The authors interpret the overall pattern as consistent with both a balance-sheet channel (weaker net worth and cash flow tighten the terms of external finance for small, informationally opaque firms) and a bank-lending/credit channel (small firms rely almost exclusively on bank loans for short-term debt, while large firms can substitute into commercial paper when banks contract lending), and offer evidence against purely nonfinancial explanations — stable durable-goods sales shares across size classes, the cyclical asymmetry itself, and no evidence that large firms subcontract output to small firms in downturns — while acknowledging that firm size is only a correlate of the underlying differences in capital-market access and that aggregate, size-class-level QFR data cannot fully rule out every nonfinancial alternative.