Results 1  10
of
117
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 ..."
Abstract

Cited by 138 (8 self)
 Add to MetaCart
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, Volume 76, Issue 2, December Estevadeordal, Antoni & Caroline Freund & Emanuel Ornelas, 2008. "Does Regionalism Affect Trade Liberalization
 Ishito and K. Ito (2003), „Vertical Intraindustry Trade and Foreign Direct Investment in East Asia‟, Journal of the Japanese and International Economies
, 2008
"... Previous empirical work on the determinants of preferential trade agreement (PTA) membership assumes a country’s PTA participation to leave other countries ’ willingness to participate unaffected. More precisely, the presumption is that new PTAs do neither influence the formation of other new PTAs i ..."
Abstract

Cited by 35 (1 self)
 Add to MetaCart
(Show Context)
Previous empirical work on the determinants of preferential trade agreement (PTA) membership assumes a country’s PTA participation to leave other countries ’ willingness to participate unaffected. More precisely, the presumption is that new PTAs do neither influence the formation of other new PTAs in the future nor do they affect the subsequent enlargement of existing ones. This view is at odds with hypotheses put forward by both political scientists and economists. This paper lays out an empirical analysis to study the role of interdependence in PTA membership in two large datasets: panel data covering 10, 430 unique countrypairs in eleven fiveyear intervals between 1950 and 2005, and an even larger set of 15, 753 countrypairs in a crosssection for the year 2005. Applying modern econometric techniques, a PTA membership is found to create an incentive for other countries to form new PTAs or, even more so, to participate in existing ones. This interdependence is stronger among adjacent countries and, more generally, ones with a higher level of ’natural ’ bilateral trade.
Bayesian Approach for Neural Networks  Review and Case Studies
 Neural Networks
, 2001
"... We give a short review on the Bayesian approach for neural network learning and demonstrate the advantages of the approach in three real applications. We discuss the Bayesian approach with emphasis on the role of prior knowledge in Bayesian models and in classical error minimization approaches. The ..."
Abstract

Cited by 27 (10 self)
 Add to MetaCart
(Show Context)
We give a short review on the Bayesian approach for neural network learning and demonstrate the advantages of the approach in three real applications. We discuss the Bayesian approach with emphasis on the role of prior knowledge in Bayesian models and in classical error minimization approaches. The generalization capability of a statistical model, classical or Bayesian, is ultimately based on the prior assumptions. The Bayesian approach permits propagation of uncertainty in quantities which are unknown to other assumptions in the model, which may be more generally valid or easier to guess in the problem. The case problems studied in this paper include a regression, a classification, and an inverse problem. In the most thoroughly analyzed regression problem, the best models were those with less restrictive priors. This emphasizes the major advantage of the Bayesian approach, that we are not forced to guess attributes that are unknown, such as the number of degrees of freedom in the model, nonlinearity of the model with respect to each input variable, or the exact form for the distribution of the model residuals.
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 ..."
Abstract

Cited by 26 (1 self)
 Add to MetaCart
(Show Context)
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 ..."
Abstract

Cited by 21 (1 self)
 Add to MetaCart
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. ..."
Abstract

Cited by 18 (5 self)
 Add to MetaCart
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.
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 ..."
Abstract

Cited by 15 (2 self)
 Add to MetaCart
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
Gaussian process regression with Studentt likelihood
"... In the Gaussian process regression the observation model is commonly assumed to be Gaussian, which is convenient in computational perspective. However, the drawback is that the predictive accuracy of the model can be significantly compromised if the observations are contaminated by outliers. A robus ..."
Abstract

Cited by 15 (4 self)
 Add to MetaCart
(Show Context)
In the Gaussian process regression the observation model is commonly assumed to be Gaussian, which is convenient in computational perspective. However, the drawback is that the predictive accuracy of the model can be significantly compromised if the observations are contaminated by outliers. A robust observation model, such as the Studentt distribution, reduces the influence of outlying observations and improves the predictions. The problem, however, is the analytically intractable inference. In this work, we discuss the properties of a Gaussian process regression model with the Studentt likelihood and utilize the Laplace approximation for approximate inference. We compare our approach to a variational approximation and a Markov chain Monte Carlo scheme, which utilize the commonly used scale mixture representation of the Studentt distribution. 1
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 ..."
Abstract

Cited by 12 (1 self)
 Add to MetaCart
(Show Context)
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
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 ..."
Abstract

Cited by 11 (1 self)
 Add to MetaCart
(Show Context)
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