Handbook of Macroeconomics | 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.

Macroeconomic shocks and their propagation

This 2016 Handbook of Macroeconomics chapter by Valerie Ramey is a survey and methodological stock-taking exercise on how macroeconomists identify structural shocks to monetary policy, fiscal policy (government spending and taxes), and technology, and how those shocks propagate through the economy — organized around the question Cochrane (1994) posed of whether researchers are “destined to remain forever ignorant of the fundamental causes of economic fluctuations,” to which Ramey’s answer is “no.” Rather than introducing a new identification scheme, the chapter sets out a common reduced-form/structural framework (reduced-form residuals e_t = Bepsilon_t, impulse responses C(L) = A(L)^-1B) and classifies the literature into roughly nine identification strategies — recursive/Cholesky orderings (with the policy variable ordered either last, as in Christiano-Eichenbaum-Evans, or first), non-recursive structural restrictions, narrative methods, high-frequency identification, external-instrument/proxy SVARs, long-run restrictions, sign restrictions, and factor-augmented VARs — and then re-estimates or directly compares many of them on overlapping or updated data. For monetary policy, a comparison of eleven leading published methods on their original samples finds trough effects of a 100-basis-point funds-rate increase on output/industrial production ranging from a positive, statistically insignificant response (Uhlig’s 2005 sign-restriction estimate) to declines of several percent (Romer-Romer’s 2004 narrative measure implies -4.3% at 24 months; Barakchian-Crowe’s 2013 high-frequency estimate implies -5% at 23 months), with no consensus on the “true” magnitude. Ramey’s own re-estimation of the Christiano-Eichenbaum-Evans-style recursive VAR shows the effect shrinking from a -0.48% trough (accounting for 6.6% of the output forecast-error variance at 24 months) over 1965-1996 to a -0.20% trough (0.5% of variance) over 1959-2007, and turning expansionary when the sample is restricted to 1983-2007. She then subjects the Gertler-Karadi (2015) high-frequency proxy instrument to validity checks and finds it has a nonzero mean (-0.013), is serially correlated (AR(1) coefficient of 0.31, robust SE 0.11), and is predictable from Federal Reserve Greenbook forecasts (R-squared of 0.21, p=0.027) — evidence that the instrument is contaminated by the Fed’s private information rather than being a clean exogenous surprise — leading her to conclude that what is now identified as a monetary policy shock is “really mostly the effects of superior information on the part of the Fed, foresight by agents, and noise.” For fiscal shocks, government-spending multiplier estimates cluster mostly between 0.6 and 1.5 once computed correctly as the integral of the output response over the integral of the spending response (rather than a peak-to-impact ratio), while tax-multiplier estimates vary more across method, from roughly -0.5 to -0.8 (SVAR-based estimates using outside output elasticities) up to about -3 (Romer-Romer 2010 narrative and Mertens-Ravn 2014 proxy-SVAR estimates). For technology shocks, the chapter documents low correlations between shocks extracted from different identification approaches and finds that investment-specific technology (IST) and marginal-efficiency-of-investment (MEI) shocks — including news about future IST change (Ben Zeev-Khan 2015, estimated to explain 73% of output variance at 8 quarters in one specification) — tend to matter more for business-cycle fluctuations than neutral total-factor-productivity shocks, whose estimated importance ranges from negligible (Galí 1999) to more than 40% of output variance depending on method. A combined VAR that Ramey estimates herself, incorporating leading fiscal, technology, and monetary shock measures together over 1954-2005, finds that the Ben Zeev-Khan IST-news shock is the single most important driver of both output and hours (accounting for up to 40% of the hours forecast-error variance at 8 quarters), that the Justiniano-Primiceri-Tambalotti MEI shock explains 42% of output variance on impact falling to 24% within a year, that the federal funds rate accounts for up to 8% of output and 18% of hours variance, and that all the included shocks together explain 63-79% of the variance of output and hours at horizons of 4-20 quarters — evidence Ramey reads as showing genuine, if incomplete, progress against the identification problem Cochrane posed two decades earlier, even as she stresses that none of the individual identification strategies commands full consensus.

Macroeconomics and Household Heterogeneity

This Handbook of Macroeconomics chapter studies how, and by how much, household income, wealth, and preference heterogeneity amplify and propagate a macroeconomic shock, focusing on the U.S. Great Recession of 2007-2009. Using Panel Study of Income Dynamics (PSID) household-level panel data, the authors first document that net worth is far more concentrated than earnings, income, or consumption, that the bottom two wealth quintiles hold essentially no net worth yet account for roughly a quarter of aggregate consumption expenditure, and that during the Great Recession these wealth-poor households cut their consumption growth by more than their income-growth slowdown alone would predict – evidence the authors read as increased precautionary saving rather than mechanical hand-to-mouth behavior. They then build and calibrate several variants of a canonical Krusell-Smith-style heterogeneous-household real business cycle model, adding preference heterogeneity, persistent idiosyncratic earnings risk, a stylized life-cycle/retirement structure, and unemployment insurance to match the empirical wealth distribution, including its low tail. The central finding is that wealth inequality significantly amplifies the aggregate consumption response to a Great Recession-sized shock – but does so specifically when the distribution features a sufficiently large mass of low-net-worth households who are not simply hand-to-mouth but instead sharply raise their saving rate as the recession hits, both because some of them do lose their jobs and because others, still employed, save against the now-elevated risk of future job loss. The benchmark economy with realistic wealth inequality generates a consumption decline about 0.5 percentage points larger than a representative-agent economy; the paper further shows that the generosity of unemployment insurance has a two-fold and partially offsetting effect on this amplification – softening the consumption drop of a given household while also shifting the long-run wealth distribution toward higher net worth – and that when output is allowed to be partially demand-determined through an aggregate-demand externality, larger wealth inequality also deepens the output decline itself, giving social insurance an additional macro-stabilization role.

Monetary policy shocks: What have we learned and to what end?

This chapter in the 1999 Handbook of Macroeconomics (Volume 1) by Lawrence Christiano, Martin Eichenbaum, and Charles Evans surveys and unifies the VAR-based literature on identifying monetary policy shocks and tracing their dynamic effects on the U.S. economy, comparing multiple identification schemes within a common recursive framework. Using quarterly U.S. data (1965:Q3-1995:Q2) and three benchmark recursive identification schemes — treating the federal funds rate, nonborrowed reserves, or the ratio of nonborrowed to total reserves as the policy instrument — the authors find a robust set of qualitative facts following a contractionary monetary policy shock: the federal funds rate rises persistently, monetary aggregates decline (some with a delay), the price level responds very little for roughly a year before declining, and real GDP falls in a hump-shaped pattern with its maximal decline about one to one-and-a-half years after the shock. They also document and help resolve the “price puzzle” — the anomalous finding that a contractionary shock appears to raise prices — showing it arises from omitting commodity prices (a leading indicator of inflation available to the Fed) from the policy reaction function’s information set, and that including them typically eliminates the puzzle. Comparing the recursive approach against a fully simultaneous (Sims-Zha) identification and against narrative-based measures of policy shocks (Romer-Romer episodes), the chapter concludes that “qualitative inference about the effects of a monetary policy shock is quite robust to the different shock measures,” even though quantitative estimates — particularly the fraction of output variance attributable to policy shocks (ranging from about 7% to 44% at the 4-to-8-quarter horizon depending on the shock measure) — are considerably more sensitive to the specific identification chosen.