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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.
Working Paper , Paper 18-12

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 Series , Paper 2020-12

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 ...
FRB Atlanta Working Paper , Paper 2015-8

Working Paper
Inference Based on SVARs Identified with Sign and Zero Restrictions: Theory and Applications

In this paper, we develop algorithms to independently draw from a family of conjugate posterior distributions over the structural parameterization when sign and zero restrictions are used to identify SVARs. We call this family of conjugate posterior distributions normal-generalized-normal. Our algorithms draw from a conjugate uniform-normal-inverse-Wishart posterior over the orthogonal reduced-form parameterization and transform the draws into the structural parameterization; this transformation induces a normal-generalized-normal posterior distribution over the structural parameterization. ...
FRB Atlanta Working Paper , Paper 2014-1

Working Paper
Minimum distance estimation of possibly non-invertible moving average models

This paper considers estimation of moving average (MA) models with non-Gaussian errors. Information in higher-order cumulants allows identification of the parameters without imposing invertibility. By allowing for an unbounded parameter space, the generalized method of moments estimator of the MA(1) model has classical (root-T and asymptotic normal) properties when the moving average root is inside, outside, and on the unit circle. For more general models where the dependence of the cumulants on the model parameters is analytically intractable, we consider simulation-based estimators with two ...
FRB Atlanta Working Paper , Paper 2013-11

Identifying shocks via time-varying volatility

An n-variable structural vector auto-regression (SVAR) can be identified (up to shock order) from the evolution of the residual covariance across time if the structural shocks exhibit heteroskedasticity (Rigobon (2003), Sentana and Fiorentini (2001)). However, the path of residual covariances can only be recovered from the data under specific parametric assumptions on the variance process. I propose a new identification argument that identifies the SVAR up to shock orderings using the autocovariance structure of second moments of the residuals, implied by an arbitrary stochastic process for ...
Staff Reports , Paper 871

Working Paper
Refining the Workhorse Oil Market Model

The Kilian and Murphy (2014) structural vector autoregressive model has become the workhorse model for the analysis of oil markets. I explore various refinements and extensions of this model, including the effects of (1) correcting an error in the measure of global real economic activity, (2) explicitly incorporating narrative sign restrictions into the estimation, (3) relaxing the upper bound on the impact price elasticity of oil supply, (4) evaluating the implied posterior distribution of the structural models, and (5) extending the sample. I demonstrate that the substantive conclusions of ...
Working Papers , Paper 1910

Working Paper
Identification Through Sparsity in Factor Models

Factor models are generally subject to a rotational indeterminacy, meaning that individualfactors are only identified up to a rotation. In the presence of local factors, which only affecta subset of the outcomes, we show that the implied sparsity of the loading matrix can be usedto 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 approximatesparsity in the loading matrix. This enables us to consistently estimate individual factors, andto interpret them as structural ...
Working Papers , Paper 20-25


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