Disentangling the Channels of the 2007-09 Recession

This 2012 Brookings Papers on Economic Activity paper by James Stock and Mark Watson asks whether the unusual severity of the 2007-09 recession and the weakness of the recovery that followed required some new economic mechanism – a “financial crisis factor” – or whether the episode can be understood as a larger, historically typical draw of familiar shocks propagating through the economy in its usual way. They address this with a high-dimensional dynamic factor model (DFM) fit to 200 quarterly U.S. macroeconomic series (1959Q1-2011Q2, 132 of them used to estimate the factors), extracting six common factors – a choice consistent with Bai-Ng (2002) information-criteria tests (which themselves gave mixed guidance, ranging from 3-4 to 12 factors depending on the criterion), visual inspection of a scree plot, and the number of distinct structural shocks examined – with factor loadings and dynamics estimated by principal components over the pre-crisis 1959Q1-2007Q3 subsample (the “old” factors) and then extended into the crisis period using a four-lag factor VAR fit over the full 1959Q1-2011Q2 sample. To identify structural shocks (oil, monetary policy, productivity, uncertainty, liquidity/financial risk, and fiscal), the paper develops what it treats as its most-cited contribution: a general “external-instruments” (proxy SVAR) identification strategy in which a shock is recovered as the population regression of an outside instrument – correlated with that shock and, under an exogeneity condition, uncorrelated with the other structural shocks – onto the reduced-form factor innovations, applied one instrument at a time across 18 candidate instruments spanning the six shock categories. The paper’s three headline findings are: (1) a DFM estimated only on pre-2007Q4 data, when fed the actual post-2007Q4 realizations of the “old” factors, tracks the 2007-09 downturn well, and formal tests find little evidence of a break in the factor loadings (rejected at the 5 percent level for only 15 percent of series against full-sample loadings, 12 percent against 1984Q1-2007Q3 loadings) or of a missing factor (a subsampling test gives p=0.59 at an 8-quarter post-2007Q3 horizon and p=0.90 at 15 quarters) – so the crisis reflects unusually large innovations to the same six factors that drove earlier postwar recessions, not a new factor or new dynamics; (2) those innovations were concentrated in financial and uncertainty-related series – the TED spread, VIX, and housing starts saw roughly 8-standard-deviation innovations in 2008Q4, while oil prices moved 1.7 standard deviations in 2007Q1 and 3.4 in 2008Q2 – and a composite uncertainty-liquidity shock (the first principal component of five estimated uncertainty and financial-risk shocks) is attributed roughly two-thirds of the 2007Q4-2009Q2 decline: 6.2 of a 9.2-percentage-point GDP shortfall and 4.5 of a 7.3-point employment shortfall, with oil and monetary-policy shocks contributing moderately and productivity and fiscal shocks contributing little; and (3) the slow recovery is mostly a story of secular trend decline rather than an unusually weak cyclical response – of the roughly 3-point shortfall in post-trough GDP growth relative to pre-1984 recoveries, about four-fifths (2.4 points) reflects slower trend growth rather than a weak cyclical rebound, and for employment the larger part of a roughly 6-point shortfall (3.3 points of trend versus 2.7 points cyclical) is trend-driven, traced to a decades-long decline in trend employment growth (trend GDP growth itself fell by roughly 1.2 percentage points from 1965 to 2005) attributed mainly to the plateauing of female labor-force participation and an aging-driven decline in male participation. Two scope conditions travel with these results and qualify how they should be used: the external-instrument shock estimates are frequently weakly identified (first-stage F-statistics below 5 in 10 of the 18 instrument cases, below 10 in all but 3) and are often substantially correlated with one another both within and across nominally distinct shock categories – most strikingly a -0.93 correlation between two ostensibly separate fiscal shocks whose underlying instruments correlate only -0.06 – which the authors say precludes treating the individual named shocks as a clean, mutually orthogonal decomposition; and because the DFM is linear and does not impose a zero lower bound, its monetary-policy shock estimates counterfactually permit negative interest rates and do not capture unconventional monetary policy, so the finding that monetary policy was “neutral or contractionary” during the crisis and recovery must be read subject to that caveat rather than as a claim about the effect of the actual, unconventional monetary-policy response.

Vector Autoregressions

This 2001 Journal of Economic Perspectives paper by James Stock and Mark Watson reviews how vector autoregressions (VARs) have performed at the four core tasks of applied macroeconometrics — data description, forecasting, structural inference, and policy analysis — roughly twenty years after Christopher Sims’s original 1980 proposal, illustrated throughout with a simple three-variable system (inflation, unemployment, and the federal funds rate) estimated on quarterly U.S. data, 1960-2000. The authors find VARs perform strongly at data description (Granger-causality tests, impulse responses, and variance decompositions reveal, for example, that inflation and unemployment shocks jointly account for about 75% of the federal funds rate’s forecast-error variance at a three-year horizon) and provide a solid forecasting benchmark (a small VAR modestly outperforms both a univariate autoregression and a random walk at most horizons in a pseudo out-of-sample exercise), but they are considerably more skeptical about structural inference and policy analysis. Structural VAR identification is criticized on three grounds — omitted-variable bias (illustrated by the “price puzzle,” which arises when variables the Fed actually used to forecast inflation, like commodity prices, are left out of the model), parameter instability in monetary policy rules over long samples, and implausible zero-restriction timing conventions that are sometimes dressed up as “structural” theory without real economic content — and the paper shows that structural impulse responses can be “very sensitive” to seemingly minor changes in the assumed policy rule (switching from a backward-looking to a forward-looking Taylor rule roughly doubles the estimated inflation and unemployment responses to a funds-rate shock). The authors conclude that VARs’ “structural implications are only as sound as their identification schemes,” and that combining good economic theory and institutional detail with flexible statistical methods like VARs remains the central ongoing challenge for the field.

James H. Stock | Macro Paper Warehouse

Disentangling the Channels of the 2007-09 Recession

Vector Autoregressions