Has the Information Channel of Monetary Policy Disappeared? Revisiting the Empirical Evidence

with Barbara Rossi and Tatevik Sekhposyan

Forthcoming, American Economic Journal: Macroeconomics

Abstract: Does the Federal Reserve have an "information advantage" in forecasting macroeconomic variables beyond what is known to private sector forecasters? And are market participants reacting only to monetary policy shocks or also to information on the future state of the economy that the Federal Reserve communicates in its announcements via an "information channel"? This paper investigates the evolution of both the information advantage and information channel over time. Although they appear to be important historically, we find substantially weaker empirical evidence of their presence in recent years once instabilities are accounted for.

Summaries: VoxEU CEPR Policy Portal Column, Brookings Hutchins Roundup.

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Working Papers

Robust Inference in Structural VAR Models identified by Non-Gaussianity

with Adam Lee and Geert Mesters

Abstract: All parameters in structural vector autoregressive (SVAR) models are locally identified when the structural shocks are independent and follow non-Gaussian distributions. Unfortunately, standard inference methods that exploit such features of the data for identification fail to yield correct coverage for structural functions of the model parameters when deviations from Gaussianity are small. To this extent, we propose a robust semi-parametric approach to conduct hypothesis tests and construct confidence sets for structural functions in SVAR models. The methodology fully exploits non-Gaussianity when it is present, but yields correct size / coverage regardless of the distance to the Gaussian distribution. Empirically, we revisit two macroeconomic SVAR studies - the oil price model of Kilian and Murphy (2012) and the labour supply-demand model of Baumeister and Hamilton (2015). These exercises highlight the importance of using weak identification robust methods to assess estimation uncertainty when using non-Gaussianity for identification.

Draft upon request

Specification Tests Robust to Multiple Instabilities

Abstract: I develop a hypothesis test for model evaluation which is robust to time-variation in parameters. The proposed method can be applied in-sample and out-of-sample to any economic model based on moment conditions. In-sample, the test selects between two nested model specifications in the presence of parameter instabilities. Out-of-sample, the test can be used to evaluate the performance of model or judgmental forecasts robust to time-variation. The key feature of the proposed test is that it is particularly powerful in the presence of multiple shifts in parameters without imposing a specific form of time-variation. Further, the test statistic provides narrative evidence on which parts of the sample drive the rejection of the null hypothesis. Simulations show that the test is accurately sized in finite samples and is more powerful than tests assuming constant coefficients or a single break if the data-generating process exhibits multiple shifts in parameters. Using the proposed test, I document the presence of short-horizon predictability in the U.S. equity premium during the postwar period. I find evidence of predictability for a large set of variables once time-variation is taken into account. The test further provides evidence of heterogeneity in the location of predictability episodes across variables. The findings explain why traditional tests often fail to uncover predictability in the full sample and why studies that split the sample at different dates often arrive at conflicting results regarding the predictive ability of a wide class of variables.

The paper has been awarded the 7th Economics Job Market Best Paper Award by UniCredit Foundation.

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