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Working Paper
Heterogeneity in the Pass-Through from Oil to Gasoline Prices: A New Instrument for Estimating the Price Elasticity of Gasoline Demand
We propose a new instrument for estimating the price elasticity of gasoline demand that exploits systematic differences across U.S. states in the pass-through of oil price shocks to retail gasoline prices. We show that these differences are primarily driven by the cost of producing and distributing gasoline, which varies with states’ access to oil and gasoline transportation infrastructure, refinery technology and environmental regulations, creating cross-sectional gasoline price shocks in response to an aggregate oil price shock. Time-varying estimates do not support the view that the ...
Working Paper
Assessing the macroeconomic impact of bank intermediation shocks: a structural approach
We take a structural approach to assessing the empirical importance of shocks to the supply of bank-intermediated credit in affecting macroeconomic fluctuations. First, we develop a theoretical model to show how credit supply shocks can be transmitted into disruptions in the production economy. Second, we use the unique micro-banking data to identify and support the model's key mechanism. Third, we find that the output effect of credit supply shocks is not only economically and statistically significant but also consistent with the vector autogression evidence. Our mode estimation indicates ...
Working Paper
Inference Based On Time-Varying SVARs Identified with Time Restrictions
We propose an approach for Bayesian inference in time-varying structural vector autoregressions (SVARs) identified with sign restrictions. The linchpin of our approach is a class of rotation-invariant time-varying SVARs in which the prior and posterior densities of any sequence of structural parameters belonging to the class are invariant to orthogonal transformations of the sequence. Our methodology is new to the literature. In contrast to existing algorithms for inference based on sign restrictions, our algorithm is the first to draw from a uniform distribution over the sequences of ...
Working Paper
Endogenous Uncertainty
We show that macroeconomic uncertainty can be considered as exogenous when assessing its effects on the U.S. economy. Instead, financial uncertainty can at least in part arise as an endogenous response to some macroeconomic developments, and overlooking this channel leads to distortions in the estimated effects of financial uncertainty shocks on the economy. We obtain these empirical findings with an econometric model that simultaneously allows for contemporaneous effects of both uncertainty shocks on economic variables and of economic shocks on uncertainty. While the traditional econometric ...
Working Paper
The Impact of Monetary Policy on Asset Prices
Estimating the response of asset prices to changes in monetary policy is complicated by the endogeneity of policy decisions and the fact that both interest rates and asset prices react to numerous other variables. This paper develops a new estimator that is based on the heteroskedasticity that exists in high frequency data. We show that the response of asset prices to changes in monetary policy can be identified based on the increase in the variance of policy shocks that occurs on days of FOMC meetings and of the Chairman's semi-annual monetary policy testimony to Congress. The identification ...
Working Paper
Decomposing the Fiscal Multiplier
Unusual circumstances often coincide with unusual fiscal policy actions. Much attention has been paid to estimates of how fiscal policy affects the macroeconomy, but these are typically average treatment effects. In practice, the fiscal “multiplier” at any point in time depends on the monetary policy response. Using the IMF fiscal consolidations dataset for identification and a new decomposition-based approach, we show how to evaluate these monetary-fiscal effects. In the data, the fiscal multiplier varies considerably with monetary policy: it can be zero, or as large as 2 depending on ...
Working Paper
A Hitchhiker’s Guide to Empirical Macro Models
This paper describes a package which uses MATLAB functions and routines to estimate VARs, local projections and other models with classical or Bayesian methods. The toolbox allows a researcher to conduct inference under various prior assumptions on the parameters, to produce point and density forecasts, to measure spillovers and to trace out the causal effect of shocks using a number of identification schemes. The toolbox is equipped to handle missing observations, mixed frequencies and time series with large cross-section information (e.g. panels of VAR and FAVAR). It also contains a number ...
Working Paper
Inference Based on Time-Varying SVARs Identified with Sign Restrictions
We propose an approach for Bayesian inference in time-varying SVARs identified with sign restrictions. The linchpin of our approach is a class of rotation-invariant time-varying SVARs in which the prior and posterior densities of any sequence of structural parameters belonging to the class are invariant to orthogonal transformations of the sequence. Our methodology is new to the literature. In contrast to existing algorithms for inference based on sign restrictions, our algorithm is the first to draw from a uniform distribution over the sequences of orthogonal matrices given the reduced-form ...
Working Paper
Identification Through Sparsity in Factor Models
Factor models are generally subject to a rotational indeterminacy, meaning that individual factors are only identified up to a rotation. In the presence of local factors, which only affect a subset of the outcomes, we show that the implied sparsity of the loading matrix can be used to solve this rotational indeterminacy. We further prove that a rotation criterion based on the 1-norm of the loading matrix can be used to achieve identification even under approximate sparsity in the loading matrix. This enables us to consistently estimate individual factors, and to interpret them as structural ...
Working Paper
A Composite Likelihood Approach for Dynamic Structural Models
We describe how to use the composite likelihood to ameliorate estimation, computational, and inferential problems in dynamic stochastic general equilibrium models. We present a number of situations where the methodology has the potential to resolve well-known problems. In each case we consider, we provide an example to illustrate how the approach works and its properties in practice.