Structural-Var | Macro Paper Warehouse

Political Pressure on the Fed

Mon, 01 Jan 0001 00:00:00 +0000

This paper combines a hand-collected archival data set of over 800 personal interactions between U.S. Presidents and Federal Reserve officials from 1933 to 2016 with a narrative structural VAR to identify shocks to political pressure on the Fed and quantify their macroeconomic effects. The identification strategy exploits the well-documented Nixon-Burns episode of 1971—corroborated by Nixon Tapes recordings and Burns’s personal diary—as a narrative restriction that the spike in personal interactions that year was driven primarily by a political pressure shock rather than by economic conditions. Political pressure shocks are found to (i) increase inflation strongly and persistently, (ii) lead to statistically weak negative effects on activity, (iii) contribute to inflationary episodes outside the Nixon era, and (iv) transmit differently from standard expansionary monetary policy shocks because political pressure can be publicly observed, generating a stronger direct effect on inflation expectations. Quantitatively, increasing political pressure by half as much as Nixon, sustained for six months, is estimated to raise the price level by more than 8%.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. What is the narrative identification strategy and how is the Nixon-Burns episode exploited?

The identification strategy imposes that the spike in President-Fed personal interactions in 1971 is mainly driven by a political pressure shock, exploiting the well-documented fact that Nixon pressured Burns to ease monetary policy in the run-up to his 1972 re-election. Recordings from the “Nixon Tapes” and Burns’s personal diary corroborate this interpretation: Burns wrote that “the President will do anything to be reelected” and that Nixon urged him to “start expanding the money supply.” Romer and Romer (2004) estimated large easing shocks to monetary policy prior to Nixon’s re-election, contrasting with a large systematic tightening after it, further supporting that Burns eased in response to the pressure. Narrative evidence from Johnson’s pressure in the 1960s is additionally used to strengthen the identification.

Q2. What does the new data on President-Fed personal interactions show?

The paper hand-collects over 800 personal interactions between U.S. Presidents and Fed officials from the historical daily schedules made available by the Presidential Libraries from Franklin D. Roosevelt (1933) through Barack Obama (2016). The average interaction lasts 53 minutes; 36% are one-on-one; 11% occur on weekends; 16% are in social settings such as dinners; 92% involve the Fed Chair and 8% other Fed officials. There is large variation across administrations: President Nixon interacted with Fed officials 160 times, while only 6 interactions occurred under Clinton. These interactions arise endogenously in response to economic conditions, which is why narrative identification is needed to isolate the political pressure component.

Q3. What are the estimated macroeconomic effects of political pressure shocks?

Political pressure shocks are found to increase inflation strongly and persistently, to have statistically weak negative effects on activity, and a pressure shock half as large as Nixon’s sustained over six months is estimated to raise the price level by more than 8%. The weak activity effect distinguishes these shocks from standard demand expansions; the mechanism operates more through expectations channels than through aggregate demand, consistent with the public observability of political pressure on the central bank. The evidence also suggests political pressure shocks contributed to inflationary episodes in periods beyond the Nixon era.

Q4. Why do political pressure shocks transmit differently from conventional monetary policy easing shocks?

Political pressure shocks transmit differently from standard expansionary monetary policy shocks primarily because political pressure on the Fed can be publicly observed, which generates a stronger direct effect on inflation expectations than a private Fed decision to ease. The paper finds a stronger effect of political pressure shocks on inflation expectations relative to the activity effect, consistent with this channel: when the public observes that the President is pressuring the central bank, expected inflation rises even before the Fed acts on that pressure.

Key concepts

President-Fed personal interactions : face-to-face or telephone contacts between U.S. Presidents and Federal Reserve officials recorded in historical presidential daily schedules 1933–2016; used as a noisy observable proxy for political attention to the Fed, from which a political pressure shock series is extracted via narrative restrictions.

political pressure shock : an exogenous, structurally identified shock to the intensity of political influence on Fed policy, isolated using a narrative SVAR restriction that the 1971 Nixon-Burns spike in interactions was driven by political pressure rather than economic conditions.

narrative identification : an approach that imposes sign or zero restrictions on a structural VAR at specific historical episodes known from external archival evidence to be driven predominantly by a particular structural shock; here used to exploit the Nixon-Burns and Johnson-Fed pressure episodes.

Uniform Priors for Impulse Responses

Mon, 01 Jan 0001 00:00:00 +0000

Structural vector autoregressions (SVARs) identified with sign restrictions are a widely used tool for estimating dynamic causal effects in macroeconomics. Critics—notably Baumeister and Hamilton (2015) and Watson (2020)—have called for caution because the standard practice of using a uniform prior over the set of orthogonal matrices (with respect to the Haar measure) induces non-uniform marginal prior distributions over the identified sets of individual impulse responses. This paper formally challenges that caution: through an if-and-only-if theorem the authors show that the uniform prior over orthogonal matrices is not only sufficient but also necessary to induce a uniform joint prior distribution over the identified set for the vector of impulse responses—a result that holds for any prior distribution over the reduced-form parameters. The paper additionally shows how to conduct posterior inference based on a uniform joint prior for the vector of impulse responses, which requires modifying the prior for the reduced-form parameters away from the standard Minnesota prior while retaining the uniform prior over orthogonal matrices. An application to Watson’s (2020) empirical example finds that joint credible sets under this new prior are similar to, but wider than, those obtained under the standard approach, and that imposing tighter identifying restrictions sharpens inference under both priors.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. What is the core result and what does it imply for applied work?

The central result is an if-and-only-if theorem: the uniform prior over the set of orthogonal matrices is both sufficient and necessary for the conventional Bayesian approach to induce a uniform joint prior distribution over the identified set for the vector of impulse responses, for any prior over the reduced-form parameters. The critics’ concern about non-uniform individual marginal priors does not extend to the joint object: when inference targets the full vector of impulse responses, the standard Haar prior is exactly appropriate. Practitioners interested in joint inference on the shape and comovement of the impulse response function need not heed the call for caution.

Q2. Why does non-uniformity of individual marginal priors not imply non-uniformity of the joint distribution?

The marginal distribution extracted from a uniform joint distribution over a compact manifold need not be uniform; marginal uniformity and joint uniformity are different properties, and only the latter is required for observationally equivalent vectors to be distinguished solely by the identifying restrictions. Baumeister and Hamilton (2015) and Watson (2020) correctly note that individual impulse responses have non-uniform marginal priors under the Haar measure, but this is not the relevant criterion when the object of interest is the entire impulse response vector. The paper’s theorem shows the joint distribution is uniform, which is the property that ensures the identification restrictions—not the prior—drive the posterior shape.

Q3. How does one implement a uniform joint prior for the vector of impulse responses?

The authors show that a uniform joint prior for the vector of impulse responses requires a modified prior for the reduced-form parameters: one that is independent between (B, Σ) and Q, takes a model-dependent non-standard form for (B, Σ), and retains a uniform prior over orthogonal matrices. The induced reduced-form prior resembles but differs from both the standard Minnesota prior and Uhlig’s (2005) “weak prior.” Because the induced prior for (B, Σ, Q) is still a uniform-normal-inverse-Wishart (UNIW) distribution, the conventional sampling algorithm applies without modification; analysts supply the modified reduced-form prior while continuing to draw Q uniformly from the Haar measure.

Q4. What does the empirical illustration show?

In Watson’s (2020) empirical example, joint credible sets under the uniform-joint-prior approach are similar to but wider than those under the standard Minnesota-prior approach. The widening is consistent with theory: the uniform joint prior spreads probability mass more evenly over the identified set rather than concentrating it toward regions favored by the Minnesota prior. The finding that tighter identifying restrictions sharpen inference under both approaches reinforces the conclusion of Inoue and Kilian (2022b) that many sign restrictions help when the focus is on joint distributions.

Q5. How is the analysis generalized?

The paper extends the results to a broader class of objects of interest—any smooth function of impulse responses, such as combinations of structural elasticities and standard deviations—with an importance-sampling correction when the induced prior over orthogonal matrices is not uniform in the extended case. The generalization exploits the diffeomorphism between IR parameters and orthogonal reduced-form parameters, which allows the change-of-variables formula to apply to any smooth object of interest.

Key concepts

vector of impulse responses : the collection of impulse responses across all variables, shocks, and horizons, treated as a single vector object for joint inference; contrasted with individual impulse responses (the response of one variable to one shock at one horizon).

uniform prior over orthogonal matrices (Haar measure) : the unique probability measure on the set of n×n orthogonal matrices invariant under left and right multiplication; the standard prior used in Bayesian sign-restricted SVARs.

identified set : the set of vectors of impulse responses that are observationally equivalent given the data and the sign restrictions; the conventional approach draws uniformly from this set under the Haar prior.

uniform-normal-inverse-Wishart (UNIW) prior : the joint prior over orthogonal reduced-form parameters consisting of the Haar prior over Q and a normal-inverse-Wishart prior over (B, Σ); conjugate and computationally tractable.