Uhlig (2004) What Are the Effects of Monetary Policy on Output? Results from an Agnostic Identification Procedure

sign-restrictionssvarvarmonetary-policyidentificationimpulse-responsebayesianfederal-funds-rateprice-puzzlemonetary-neutralityagnostic-identification

Summary

Uhlig proposes an agnostic identification strategy for monetary policy shocks in a vector autoregression (VAR): impose sign restrictions only on variables other than output (prices, nonborrowed reserves, federal funds rate must respond in the expected direction for KK months), and leave the output response completely unconstrained. Applying this to a six-variable monthly VAR (1965–2003), he finds that monetary policy shocks have an ambiguous effect on real GDP — within ±\pm0.2% with two-thirds probability — consistent with both the conventional negative view and near-neutrality. The key insight is that Cholesky identification's implicit zero-impact restriction on GDP is what drives conventional large-negative results, not the data itself.

Key Claims

Model Specification

VAR: yt=B1yt1++Byt+uty_t = B_1 y_{t-1} + \cdots + B_\ell y_{t-\ell} + u_t, E[utut]=ΣE[u_t u_t'] = \Sigma, with m=6m=6 variables (real GDP, GDP deflator, commodity price index, total reserves, nonborrowed reserves, federal funds rate), =12\ell=12 lags, monthly data January 1965–December 2003, variables in log levels (except the federal funds rate (FFR)).

Impulse vector: Any impulse vector can be written a=A~αa = \tilde{A}\alpha where A~\tilde{A} is the lower Cholesky factor of Σ\Sigma and αSm\alpha \in S^m is a unit-length vector. All structural shocks consistent with a given reduced-form are parameterized this way.

Sign restriction set: A(B,Σ,K)\mathcal{A}(B, \Sigma, K) = set of admissible impulse vectors satisfying, for each restricted variable jj and horizon k=0,,Kk=0,\ldots,K:

ιjra(k)0(or0)\iota_j' r_a(k) \geq 0 \quad (\text{or} \leq 0)

where ra(k)r_a(k) is the impulse response at horizon kk and ιj\iota_j selects variable jj. This set is the intersection of half-spaces in α\alpha-space and is convex.

Pure sign restriction approach: Use a Normal-Wishart prior with N0=0N_0=0, ν0=0\nu_0=0 (diffuse). Draw (B,Σ)(B, \Sigma) from posterior, draw α\alpha uniformly from SmS^m, retain draws where a=A~αA(B,Σ,K)a = \tilde{A}\alpha \in \mathcal{A}(B,\Sigma,K). Posterior bands are the quantiles of retained draws.

Penalty function approach: For each posterior draw of (B,Σ)(B,\Sigma), find:

α^=argminαSmΨ(a(α)),Ψ(a)=jk=0Kf ⁣(ιjra(k)σj)\hat{\alpha} = \arg\min_{\alpha \in S^m} \Psi(a(\alpha)), \quad \Psi(a) = \sum_j \sum_{k=0}^{K} f\!\left(\frac{\iota_j r_a(k)}{\sigma_j}\right)

where f(x)f(x) is asymmetric: slope 1 for x0x \geq 0 (reward), slope 100 for x<0x < 0 (penalty). This produces sharper bands that closely mimic point identification.

Variance share: ϕa,j,k=(ra,j(k))2/i=1m(ri,j(k))2\phi_{a,j,k} = (r_{a,j}(k))^2 \big/ \sum_{i=1}^{m}(r_{i,j}(k))^2, the fraction of jj's forecast variance at horizon kk attributable to shock aa.

Concepts Introduced or Extended

Entities Mentioned

Quotes

"The agnostic procedure leaves the response of output to a monetary policy shock unrestricted. Essentially, I find that this agnostic procedure results in an ambiguous response of output."

"The Cholesky decomposition implicitly imposes that output does not respond contemporaneously to monetary policy shocks. This restriction has considerable bite and drives the output results."

My Take

This paper is methodologically important: it shows that a widely cited finding (monetary contractions depress output) rests on an implicit zero-restriction that has nothing to do with economics. The sign restriction approach is elegant precisely because it avoids imposing restrictions on the object of interest. The main limitation is that "agnosticism" produces wide, nearly uninformative bands — the result is less "monetary policy is neutral" and more "we cannot tell." The K-sensitivity finding is underappreciated: requiring sign restrictions to hold for longer paradoxically weakens the negative-output evidence, which ought to concern users who treat K as a robustness check. The penalty function approach rescues point-like inference but at the cost of optimizing over a somewhat arbitrary loss function.