Results 1  10
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23
Bayesian Multivariate Time Series Methods for Empirical Macroeconomics
, 2009
"... Macroeconomic practitioners frequently work with multivariate time series models such as VARs, factor augmented VARs as well as timevarying parameter versions of these models (including variants with multivariate stochastic volatility). These models have a large number of parameters and, thus, over ..."
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Cited by 54 (12 self)
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Macroeconomic practitioners frequently work with multivariate time series models such as VARs, factor augmented VARs as well as timevarying parameter versions of these models (including variants with multivariate stochastic volatility). These models have a large number of parameters and, thus, overparameterization problems may arise. Bayesian methods have become increasingly popular as a way of overcoming these problems. In this monograph, we discuss VARs, factor augmented VARs and timevarying parameter extensions and show how Bayesian inference proceeds. Apart from the simplest of VARs, Bayesian inference requires the use of Markov chain Monte Carlo methods developed for state space models and we describe these algorithms. The focus is on the empirical macroeconomist and we offer advice on how to use these models and methods in practice and include empirical illustrations. A website provides Matlab code for carrying out Bayesian inference in these models.
Large TimeVarying Parameter VARs
, 2013
"... In this paper, we develop methods for estimation and forecasting in large timevarying parameter vector autoregressive models (TVPVARs). To overcome computational constraints, we draw on ideas from the dynamic model averaging literature which achieve reductions in the computational burden through t ..."
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Cited by 14 (2 self)
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In this paper, we develop methods for estimation and forecasting in large timevarying parameter vector autoregressive models (TVPVARs). To overcome computational constraints, we draw on ideas from the dynamic model averaging literature which achieve reductions in the computational burden through the use forgetting factors. We then extend the TVPVAR so that its dimension can change over time. For instance, we can have a large TVPVAR as the forecasting model at some points in time, but a smaller TVPVAR at others. A final extension lies in the development of a new method for estimating, in a timevarying manner, the parameter(s) of the shrinkage priors commonlyused with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output and interest rates demonstrates the feasibility and usefulness of our approach.
Moving Average Stochastic Volatility Models with Application to Inflation Forecast
, 2013
"... We introduce a new class of models that has both stochastic volatility and moving average errors, where the conditional mean has a state space representation. Having a moving average component, however, means that the errors in the measurement equation are no longer serially independent, and estimat ..."
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Cited by 11 (11 self)
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We introduce a new class of models that has both stochastic volatility and moving average errors, where the conditional mean has a state space representation. Having a moving average component, however, means that the errors in the measurement equation are no longer serially independent, and estimation becomes more difficult. We develop a posterior simulator that builds upon recent advances in precisionbased algorithms for estimating these new models. In an empirical application involving U.S. inflation we find that these moving average stochastic volatility models provide better insample fitness and outofsample forecast performance than the standard variants with only stochastic volatility.
Bayesian Analysis of Latent Threshold Dynamic Models
, 2011
"... We describe a general approach to dynamic sparsity modelling in time series and statespace models. Timevarying parameters are linked to latent processes that are thresholded to induce zero values adaptively, providing dynamic variable inclusion/selection. We discuss Bayesian model estimation and p ..."
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Cited by 11 (3 self)
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We describe a general approach to dynamic sparsity modelling in time series and statespace models. Timevarying parameters are linked to latent processes that are thresholded to induce zero values adaptively, providing dynamic variable inclusion/selection. We discuss Bayesian model estimation and prediction in dynamic regressions, timevarying vector autoregressions and multivariate volatility models using latent thresholding. Substantive examples in macroeconomics and financial time series show the utility of this approach to dynamic parameter reduction and timevarying sparsity modelling in terms of statistical and economic interpretations as well as improved predictions.
Time Varying Dimension Models
, 2010
"... Abstract: Time varying parameter (TVP) models have enjoyed an increasing popularity in empirical macroeconomics. However, TVP models are parameterrich and risk over…tting unless the dimension of the model is small. Motivated by this worry, this paper proposes several Time Varying dimension (TVD) m ..."
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Cited by 9 (7 self)
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Abstract: Time varying parameter (TVP) models have enjoyed an increasing popularity in empirical macroeconomics. However, TVP models are parameterrich and risk over…tting unless the dimension of the model is small. Motivated by this worry, this paper proposes several Time Varying dimension (TVD) models where the dimension of the model can change over time, allowing for the model to automatically choose a more parsimonious TVP representation, or to switch between di¤erent parsimonious representations. Our TVD models all fall in the category of dynamic mixture models. We discuss the properties of these models and present methods for Bayesian inference. An application involving US in‡ation forecasting illustrates and compares the di¤erent TVD models. We …nd our TVD approaches exhibit better forecasting performance than several standard benchmarks and shrink towards parsimonious speci…cations.
Sustainable migration policies
"... This paper considers whether countries might mutually agree a policy of allowing free movement of workers. For the countries to agree, the short run costs must outweighed by the long term benefits that result from better labor market flexibility and income smoothing. We show that such policies are l ..."
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Cited by 6 (0 self)
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This paper considers whether countries might mutually agree a policy of allowing free movement of workers. For the countries to agree, the short run costs must outweighed by the long term benefits that result from better labor market flexibility and income smoothing. We show that such policies are less likely to be adopted for less risk averse workers and for countries that trade more. More surprisingly we find that some congestion costs can help. This reverses the conventional wisdom that congestion costs tend to inhibit free migration policies.
Stable and efficient coalitional networks
, 2011
"... We develop a theoretical framework that allows us to study which bilateral links and coalition structures are going to emerge at equilibrium. We define the notion of coalitional network to represent a network and a coalition structure, where the network specifies the nature of the relationship each ..."
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Cited by 6 (0 self)
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We develop a theoretical framework that allows us to study which bilateral links and coalition structures are going to emerge at equilibrium. We define the notion of coalitional network to represent a network and a coalition structure, where the network specifies the nature of the relationship each individual has with her coalition members and with individuals outside her coalition. To predict the coalitional networks that are going to emerge at equilibrium we propose the concepts of strong stability and of contractual stability. Contractual stability imposes that any change made to the coalitional network needs the consent of both the deviating players and their original coalition partners. Requiring the consent of coalition members under the simple majority or unanimity decision rule may help to reconcile stability and efficiency. Moreover, this new framework can provide in sights that one cannot obtain if
Oligopolistic competition with general complementarities
, 2011
"... In this paper we extend the basic model of Cournot competition to the case where both the demand function and the cost functions of each firm depend on the amounts produced by competitors. In this modified setting, proving existence of equilibria becomes harder. We develop a generalization of the th ..."
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Cited by 3 (0 self)
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In this paper we extend the basic model of Cournot competition to the case where both the demand function and the cost functions of each firm depend on the amounts produced by competitors. In this modified setting, proving existence of equilibria becomes harder. We develop a generalization of the theory of supermodular games in the context where individual decision variables take values in a totally ordered set to prove existence of equilibria in this generalized Cournot setting.
Efficient estimation of Bayesian VARMAs with timevarying coefficients,” manuscript
, 2015
"... Empirical work in macroeconometrics has been mostly restricted to using VARs, even though there are strong theoretical reasons to consider general VARMAs. A number of articles in the last two decades have conjectured that this is because estimation of VARMAs is perceived to be challenging and propo ..."
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Cited by 2 (2 self)
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Empirical work in macroeconometrics has been mostly restricted to using VARs, even though there are strong theoretical reasons to consider general VARMAs. A number of articles in the last two decades have conjectured that this is because estimation of VARMAs is perceived to be challenging and proposed various ways to simplify it. Nevertheless, VARMAs continue to be largely dominated by VARs, particularly in terms of developing useful extensions. We address these computational challenges with a Bayesian approach. Specifically, we develop a Gibbs sampler for the basic VARMA, and demonstrate how it can be extended to models with timevarying VMA coefficients and stochastic volatility. We illustrate the methodology through a macroeconomic forecasting exercise. We show that in a class of models with stochastic volatility, VARMAs produce better density forecasts than VARs, particularly for short forecast horizons.
Forecasting with Dimension Switching VARs
, 2012
"... Abstract: This paper develops methods for Bayesian VAR forecasting when the researcher is uncertain about which variables enter the VAR and the dimension of the VAR may be changing over time. It considers the case where there are N variables which might potentially enter a VAR and the researcher is ..."
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Cited by 2 (1 self)
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Abstract: This paper develops methods for Bayesian VAR forecasting when the researcher is uncertain about which variables enter the VAR and the dimension of the VAR may be changing over time. It considers the case where there are N variables which might potentially enter a VAR and the researcher is interested in forecasting N ∗ N−N ∗ of them. Thus, the researcher is faced with 2 potential VARs. If N is large, conventional Bayesian methods can be infeasible due to the computational burden of dealing with a huge model space. Allowing for the dimension of the VAR to change over time only increases this burden. In light of these considerations, this paper uses computationally practical approximations adapted from the dynamic model averaging literature so as to develop methods for dynamic dimension selection (DDS) in VARs. In an inflation forecasting application, we show the benefits of DDS. In particular, DDS switches between different parsimonious VARs and forecasts appreciably better than various small and large dimensional VARs.