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Working Paper
A Gibbs simulator for restricted VAR models
Zha, Tao; Waggoner, Daniel F.
(2000)
Many economic applications call for simultaneous equations VAR modeling. We show that the existing importance sampler can be prohibitively inefficient for this type of models. We develop a Gibbs simulator that works for both simultaneous and recursive VAR models with a much broader range of linear restrictions than those in the existing literature. We show that the required computation is of an SUR type, and thus our method can be implemented cheaply even for large systems of multiple equations.
FRB Atlanta Working Paper
, Paper 2000-3
Working Paper
Quantifying the half-life of deviations from PPP: The role of economic priors
Zha, Tao; Kilian, Lutz
(1999)
The half-life of deviations from purchasing power parity (PPP) plays a central role in the ongoing debate about the ability of macroeconomic models to account for the time series behavior of the real exchange rate. The main contribution of this paper is a general framework in which alternative priors for the half-life of deviations from PPP can be examined. We show how to incorporate formally the prior views of economists about the half-life. In our empirical analysis we provide two examples of such priors. One example is a consensus prior consistent with widely held views among economists ...
FRB Atlanta Working Paper
, Paper 99-21
Working Paper
Budget constraints and time-series evidence on consumption: comment
Roberds, William
(1992)
FRB Atlanta Working Paper
, Paper 92-14
Working Paper
Has the Romers' narrative approach identified monetary policy shocks
Leeper, Eric M.
(1993)
FRB Atlanta Working Paper
, Paper 93-1
Working Paper
Conditional forecasts in dynamic multivariate models
Zha, Tao; Waggoner, Daniel F.
(1998)
In the existing literature, conditional forecasts in the vector autoregressive (VAR) framework have not been commonly presented with probability distributions or error bands. This paper develops Bayesian methods for computing such distributions or bands. It broadens the class of conditional forecasts to which the methods can be applied. The methods work for both structural and reduced-form VAR models and, in contrast to common practices, account for the parameter uncertainty in small samples. Empirical examples under the flat prior and under the reference prior of Sims and Zha (1998) are ...
FRB Atlanta Working Paper
, Paper 98-22
Working Paper
Fiscal competition and reality: A time series approach
Becsi, Zsolt
(1998)
Strategic interjurisdictional behavior and the interaction over time of the mean and dispersion of average tax rates across states are analyzed in a vector autoregression model. Variance decompositions reveal that fiscal competition explains roughly one-third of the time variation of state and local taxes. Impulse response functions identify the type of fiscal competition and the characteristics of leaders and followers. Local tax dynamics agree with Wildasin's (1988) results on expenditure competition with significant short- and medium-run effects but insignificant long-run effects. State ...
FRB Atlanta Working Paper
, Paper 98-19
Working Paper
Identification, vector autoregression, and block recursion
Zha, Tao
(1996)
In the applications of identified VAR models, finite-sample properties are not obvious to obtain when identifying restrictions are imposed on some lagged relationships. As a result, researchers have either left lagged relationships unrestricted even though some restrictions clearly make economic sense or failed to provide correct inference of the estimates. We extend the Bayesian methodology in the existing literature to these cases and develop the blockwise Monte Carlo methods. We show how to implement these methods to obtain the estimation and inference.
FRB Atlanta Working Paper
, Paper 96-8
Working Paper
Likelihood-preserving normalization in multiple equation models
Zha, Tao; Waggoner, Daniel F.
(2000)
Causal analysis in multiple equation models often revolves around the evaluation of the effects of an exogenous shift in a structural equation. When taking into account the uncertainty implied by the shape of the likelihood, we argue that how normalization is implemented matters for inferential conclusions around the maximum likelihood (ML) estimates of such effects. We develop a general method that eliminates the distortion of finite-sample inferences about these ML estimates after normalization. We show that our likelihood-preserving normalization always maintains coherent economic ...
FRB Atlanta Working Paper
, Paper 2000-8
Working Paper
GARCH-based identification and estimation of triangular systems
Prono, Todd
(2008)
Diagonal GARCH is shown to support identification of the triangular system and is argued as a higher moment analog to traditional exclusion restrictions used for determining suitable instruments. The estimator for this result is ML in the case where a distribution for the GARCH process is known and GMM otherwise. For the GMM estimator, an alternative weighting matrix is proposed.
Supervisory Research and Analysis Working Papers
, Paper QAU08-4
Working Paper
GARCH-based identification of triangular systems with an application to the CAPM: still living with the roll critique
Prono, Todd
(2006)
This paper presents a new method for identifying triangular systems of time-series data. Identification is the product of a bivariate GARCH process. Relative to the literature on GARCH-based identification, this method distinguishes itself both by allowing for a time-varying covariance and by not requiring a complete estimation of the GARCH parameters. Estimation follows OLS and standard univariate GARCH and ARMA techniques, or GMM. A Monte Carlo study of the GMM estimator is provided. The identification method is then applied in testing a conditional version of the CAPM. ; Quantitative ...
Working Papers
, Paper 07-1
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