Results 1  10
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128
Markov Chain Monte Carlo Simulation Methods in Econometrics
, 1993
"... We present several Markov chain Monte Carlo simulation methods that have been widely used in recent years in econometrics and statistics. Among these is the Gibbs sampler, which has been of particular interest to econometricians. Although the paper summarizes some of the relevant theoretical literat ..."
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Cited by 153 (9 self)
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We present several Markov chain Monte Carlo simulation methods that have been widely used in recent years in econometrics and statistics. Among these is the Gibbs sampler, which has been of particular interest to econometricians. Although the paper summarizes some of the relevant theoretical literature, its emphasis is on the presentation and explanation of applications to important models that are studied in econometrics. We include a discussion of some implementation issues, the use of the methods in connection with the EM algorithm, and how the methods can be helpful in model specification questions. Many of the applications of these methods are of particular interest to Bayesians, but we also point out ways in which frequentist statisticians may find the techniques useful.
Interdependent preferential trade agreement memberships: An empirical analysis’,
 Journal of International Economics,
, 2008
"... Abstract Recent theoretical work on bilateral trade preferences stresses their dependence on but also their consequences for the multilateral trading system. In particular, a country's choice of participating in a preferential trade agreement (PTA) depends on the choice of other economies to p ..."
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Cited by 49 (3 self)
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Abstract Recent theoretical work on bilateral trade preferences stresses their dependence on but also their consequences for the multilateral trading system. In particular, a country's choice of participating in a preferential trade agreement (PTA) depends on the choice of other economies to participate therein. However, recent empirical work on the determinants of PTA formation assumes that countries are independent in that regard. This paper lays out an empirical analysis to study the role of interdependencies in PTA membership in a large dataset of 15, 753 countrypairs. Applying modern econometric techniques, a PTA membership is found to create an incentive for other countrypairs to participate in a PTA as well. Especially, countries have an incentive to participate in the same PTA if their neighbors are members already.
Modelbased clustering of multiple time series
 CEPR Discussion Paper
, 2004
"... We propose to use the attractiveness of pooling relatively short time series that display similar dynamics, but without restricting to pooling all into one group. We suggest to estimate the appropriate grouping of time series simultaneously along with the groupspecific model parameters. We cast est ..."
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Cited by 33 (2 self)
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We propose to use the attractiveness of pooling relatively short time series that display similar dynamics, but without restricting to pooling all into one group. We suggest to estimate the appropriate grouping of time series simultaneously along with the groupspecific model parameters. We cast estimation into the Bayesian framework and use Markov chain Monte Carlo simulation methods. We discuss model identification and base model selection on marginal likelihoods. A simulation study documents the efficiency gains in estimation and forecasting that are realized when appropriately grouping the time series of a panel. Two economic applications illustrate the usefulness of the method in analyzing also extensions to Markov switching within clusters and heterogeneity within clusters, respectively. JEL classification: C11,C33,E32
The Econometrics of DSGE Models
, 2009
"... In this paper, I review the literature on the formulation and estimation of dynamic stochastic general equilibrium (DSGE) models with a special emphasis on Bayesian methods. First, I discuss the evolution of DSGE models over the last couple of decades. Second, I explain why the profession has decide ..."
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Cited by 31 (1 self)
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In this paper, I review the literature on the formulation and estimation of dynamic stochastic general equilibrium (DSGE) models with a special emphasis on Bayesian methods. First, I discuss the evolution of DSGE models over the last couple of decades. Second, I explain why the profession has decided to estimate these models using Bayesian methods. Third, I brie‡y introduce some of the techniques required to compute and estimate these models. Fourth, I illustrate the techniques under consideration by estimating a benchmark DSGE model with real and nominal rigidities. I conclude by o¤ering some pointers for future research.
Bayesian Regression Analysis With Scale Mixtures of Normals
, 1999
"... This paper considers a Bayesian analysis of the linear regression model under independent sampling from general scale mixtures of Normals. Using a common reference prior, we investigate the validity of Bayesian inference and the existence of posterior moments of the regression and scale parameters. ..."
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Cited by 22 (7 self)
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This paper considers a Bayesian analysis of the linear regression model under independent sampling from general scale mixtures of Normals. Using a common reference prior, we investigate the validity of Bayesian inference and the existence of posterior moments of the regression and scale parameters. We find that whereas existence of the posterior distribution does not depend on the choice of the design matrix or the mixing distribution, both of them can crucially intervene in the existence of posterior moments. We identify some useful characteristics that allow for an easy verification of the existence of a wide range of moments. In addition, we provide full characterizations under sampling from finite mixtures of Normals, Pearson VII or certain Modulated Normal distributions. For empirical applications, a numerical implementation based on the Gibbs sampler is recommended.
Forecasting the South African economy with VARs and VECMs
 South African Journal of Economics
, 2006
"... The paper uses the Gibbs sampling technique to estimate a heteroscedastic Bayesian Vector Error Correction Model (BVECM) of the South African economy for the period 1970:12000:4, and then forecasts GDP, consumption, investment, short and long term interest rates, and the CPI over the period of 2001 ..."
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Cited by 20 (14 self)
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The paper uses the Gibbs sampling technique to estimate a heteroscedastic Bayesian Vector Error Correction Model (BVECM) of the South African economy for the period 1970:12000:4, and then forecasts GDP, consumption, investment, short and long term interest rates, and the CPI over the period of 2001:1 to 2005:4. We find that a tight prior produces relatively more accurate forecasts than a loose one. The outofsampleforecast accuracy resulting from the Gibbs sampled BVECM is compared with those generated from a Classical VECM and a homoscedastic BVECM. The homoscedastic BVECM is found to produce the most accurate out of sample forecasts.
A family of geographically weighted regression models
 In Advances in spatial econometrics, edited by L. Anselin and
, 1999
"... A Bayesian treatment of locally linear regression methods introduced in McMillen (1996) and labeled geographically weighted regressions (GWR) in Brunsdon, Fotheringham and Charlton (1996) is set forth in this paper. GWR uses distancedecayweighted subsamples of the data to produce locally linear e ..."
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Cited by 19 (2 self)
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A Bayesian treatment of locally linear regression methods introduced in McMillen (1996) and labeled geographically weighted regressions (GWR) in Brunsdon, Fotheringham and Charlton (1996) is set forth in this paper. GWR uses distancedecayweighted subsamples of the data to produce locally linear estimates for every point in space. While the use of locally linear regression represents a true contribution in the area of spatial econometrics, it also presents problems. It is argued that a Bayesian treatment can resolve these problems and has a great many advantages over ordinary leastsquares estimation used by the GWR method. 1 1
Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation
 Poltiical Analysis
"... Bayesian simulation is increasingly exploited in the social sciences for estimation and inference of model parameters. But an especially useful (if often overlooked) feature of Bayesian simulation is that it can be used to estimate any function of model parameters, including “auxiliary ” quantities ..."
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Cited by 18 (1 self)
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Bayesian simulation is increasingly exploited in the social sciences for estimation and inference of model parameters. But an especially useful (if often overlooked) feature of Bayesian simulation is that it can be used to estimate any function of model parameters, including “auxiliary ” quantities such as goodnessoffit statistics, predicted values, and residuals. Bayesian simulation treats these quantities as if they were missing data, sampling from their implied posterior densities. Exploiting this principle also lets researchers estimate models via Bayesian simulation where maximumlikelihood estimation would be intractable. Bayesian simulation thus provides a unified solution for quantitative social science. I elaborate these ideas in a variety of contexts: these include generalized linear models for binary responses using data on bill cosponsorship recently reanalyzed in Political Analysis, item–response models for the measurement of respondent’s levels of political information in public opinion surveys, the estimation and analysis of legislators’ ideal points from rollcall data, and outlierresistant regression estimates of incumbency advantage in U.S. Congressional elections. 1 Bayesian Simulation: Estimation, Inference, and Communication
A bayesian probit model with spatial dependencies
 Advances in Econometrics: Volume 18: Spatial and Spatiotemporal Econometrics
, 2004
"... A Bayesian probit model with individual effects that exhibit spatial dependencies is set forth. Since probit models are often used to explain variation in individual choices, these models may well exhibit spatial interaction effects due to the varying spatial location of the decision makers. That is ..."
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Cited by 18 (1 self)
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A Bayesian probit model with individual effects that exhibit spatial dependencies is set forth. Since probit models are often used to explain variation in individual choices, these models may well exhibit spatial interaction effects due to the varying spatial location of the decision makers. That is, individuals located at similar points in space may tend to exhibit similar choice behavior. The model proposed here allows for a parameter vector of spatial interaction effects that takes the form of a spatial autoregression. This model extends the class of Bayesian spatial logit/probit models presented in LeSage (2000) and relies on a hierachical construct that we estimate via Markov Chain Monte Carlo methods. We illustrate the model by applying it to the 1996 presidential election results for 3,110 US counties. 1 1