2005) ‘Demand System Estimation and its Application to Horizontal Merger Analysis (2008)
Daniel Hosken, David Scheffman, Michael Vita, Luke Froeb, John Geweke, Jerry Hausman, ...
The past decade has witnessed remarkable developments in the quantitative analysis of horizontal mergers. Increases in computing power and the quantity and quality of data available have...
Embedding Bayesian Tools in Mathematical Software (2008)
John Geweke, William Mccausland
The BACC software provides its users with tools for Bayesian Analysis, Computation and Communications. These tools are embedded in mathematical software applications such as Matlab and Gauss. From...
This paper provides a generic, very fast method for computing exact density ratio class bounds on posterior expectations, given the output of a posterior simulator. It illustrates application of the...
This paper provides a generic, very fast method for computing exact density ratio class bounds on posterior expectations, given the output of a posterior simulator. It illustrates application of the...
This paper provides a generic, very fast method for computing exact density ratio class bounds on posterior expectations, given the output of a posterior simulator. It illustrates application of the...
J. M. Bernardo, J. O. Berger, A. P. Dawid, John Geweke
This paper exposits and develops Bayesian methods of model criticism and robustness analysis. The objectives are to clarify the Bayesian interpretation of non-Bayesian diagnostic tests, and provide...
Luke M. Froeb, John Geweke, Christopher T. Taylor, Ramsey Shehadeh, We Jonathan Chatzkel, David Yans, ...
In this paper, we examine price movements over time around the collapse of a bid-rigging conspiracy. While the mean decreased by sixteen percent, the standard deviation increased by over two hundred...
James Mitchell, Kenneth F. Wallis, John Geweke, Discussion James Mitchell
Summary. In a recent article Gneiting, Balabdaoui and Raftery (JRSSB, 2007) propose the criterion of sharpness for the evaluation of predictive distributions or density forecasts. They motivate their...
John Geweke, Daniel Houser, Michael Keane
Over the last decade econometric inference based on simulation techniques has become increasingly common, particularly for latent variable models. The reason is that such models often generate...
The construction and implementation of a Gibbs sampler for efficient simulation from the truncated multivariate normal and Student-t distributions is described. It is shown how the accuracy and...
This paper provides a generic, very fast method for computing exact density ratio class bounds on posterior expectations, given the output of a posterior simulator. It illustrates application of the...
1 Variable Selection and Model Comparison (2007)
In the specification of linear regression models it is common to indicate a list of candidate variables from which a subset enters the model with nonzero coefficients. This paper interprets this...
Bayesian Inference for Linear Models Subject to Linear Inequality Constraints (2007)
The normal linear model, with sign or other linear inequality constraints on its coefficients, arises very commonly in many scientific applications. Given inequality constraints Bayesian inference is...
Bayesian Inference for Linear Models Subject to Linear Inequality Constraints (2007)
The normal linear model, with sign or other linear inequality constraints on its coefficients, arises very commonly in many scientific applications. Given inequality constraints Bayesian inference is...
Data augmentation and Gibbs sampling are two closely related, sampling-based approaches to the calculation of posterior moments. The fact that each produces a sample whose constituents are neither...
Predicting Turning Points Dan Chin * Kenwood Capital Management LLC (2007)
This paper presents a new method predicting turning points. paper formally defines turning point; develops a probit model estimating probability turning point; and then examines both in-sample...
J. M. Bernardo, J. O. Berger, A. P. Dawid, John Geweke
This paper exposits and develops Bayesian methods of model criticism and robustness analysis. The objectives are to clarify the Bayesian interpretation of non-Bayesian diagnostic tests, and provide...
John Geweke, George C. Davis, George C. Davis
this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. Table of Contents Background of...
Communications In Statistics, John Geweke, Hisashi Tanizaki
Th Metropolis-Hastingsalgorith hg been important in th recent development of Bayesmeth9H( Thh algorith generates random draws from a target distribution utilizing a sampling (or proposal)...
Bayesian Model Comparison and Validation 1 (2007)
Models are the venue for much of the work of the economics profession. We use them to express, compare and evaluate alternative ways of addressing important questions. Applied econometricians are...
Models, Computational Experiments and Reality (2007)
DSGE models are designed to mimic only certain aspects of reality, usually speci…ed moments of observable data. They typically have other implications that are clearly false and lead to their...
Interpretation and inference in mixture models: Simple MCMC works (2007)
The mixture model likelihood function is invariant with respect to permutation of the components of the mixture. If functions of interest are permutation sensitive, as in classification applications,...
Econometrics: A Bird's Eye View (2006)
Geweke, John, Horowitz, Joel, Pesaran, M. Hashem
As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly over the past few decades. Major advances have taken place in the analysis of...
Econometrics: A Bird’s Eye View ∗ (2006)
John Geweke, Joel Horowitz, Hashem Pesaran
As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly over the past few decades. Major advances have taken place in the analysis of...
Smoothly mixing regressions (2006)
This paper extends the conventional Bayesian mixture of normals model by permitting state probabilities to depend on observed covariates. The dependence is captured by a simple multinomial probit...
1 I thank two anonymous referees for their thoughtful comments and suggestions. (2006)
John Geweke, John Kareken, Kenneth Klee, Antonio Merlo, ...
Tjomme Rusticus for his excellent assistance on data collection. Financial support
Econometrics: A Bird’s Eye View ∗ (2006)
John Geweke, Joel Horowitz, Hashem Pesaran
As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly over the past few decades. Major advances have taken place in the analysis of...
Bayesian Cross-Sectional Analysis of the Conditional Distribution of Earnings of Men in (2005)
The United States, John Geweke, Michael Keane
This study develops practical methods for Bayesian nonparametric inference in regression models. The emphasis is on extending a nonparametric treatment of the regression function to the full...
W. J. Granger, Allan Timmerman, Amsterdam North-holl, John Geweke, Charles Whiteman
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general requires explicit formulation of a model, and conditioning on known quantities, in...
Harvard University, Federal Reserve Bank of San Francisco, (2002)
John Geweke, Gautam Gowrisankaran, Robert J. Town
This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are widely...
Abstract: In this paper, an attempt is made to show a general solution to nonlinear and/or non-Gaussian state space modeling in a Bayesian framework, which corresponds to an extension of Carlin,...
notice, is given to the source. Bayesian Inference for Hospital Quality in a Selection Model (2001)
John Geweke, Gautam Gowrisankaran, Robert J. Town, John Geweke, Gautam Gowrisankaran, Robert J. Town
National Bureau of Economic Research or the Federal
John Geweke, Gautam Gowrisankaran, Robert J. Town
This paper develops new econometric methods to estimate hospital quality and other models with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are...
Computational experiments and reality (1999)
This study explores three alternative econometric interpretations of dynamic, stochastic general equilibrium (DSGE) models. Under a strong econometric interpretation, these models provide likelihood...
On Markov Chain Monte Carlo Methods for Nonlinear and Non-Gaussian State-Space Models (1999)
In this paper, a nonlinear and/or non-Gaussian smoother utilizing Markov chain Monte Carlo Methods is proposed, where the measurement and transition equations are specified in any general formulation...
John Geweke, Gautam Gowrisankaran, Robert J. Town
This paper develops new econometric methods to estimate hospital quality and other models with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are...
Computational Experiments and Reality (1999)
This study explores three alternative econometric interpretations of dynamic, stochastic general equilibrium (DSGE) models. Under a strong econometric interpretation, these models provide likelihood...
Computational Experiments and Reality (1999)
This study explores three alternative econometric interpretations of dynamic, stochastic general equilibrium (DSGE) models. Under a strong econometric interpretation, these models provide likelihood...
Computational Experiments and Reality (1999)
This study explores three alternative econometric interpretations of dynamic, stochastic general equilibrium (DSGE) models. Under a strong econometric interpretation, these models provide likelihood...
John Geweke, Gautam Gowrisankaran, Robert J. Town
This paper develops new econometric methods to estimate hospital quality and other models with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are...
and Federal Reserve Bank of Minneapolis (1997)
This paper generalizes the normal probit model of dichotomous choice by introducing mixtures of normals distributions for the disturbance term. By mixing on both the mean and variance parameters and...
Variable selection and model comparison in regression (1996)
In the specification of linear regression models it is common to indicate a list of candidate variables from which a subset enters the model with nonzero coefficients. In some cases any combination...
Variable selection and model comparison in regression (1996)
In the specification of linear regression models it is common to indicate a list of candidate variables from which a subset enters the model with nonzero coefficients. This paper interprets this...
Variable selection and model comparison in regression (1996)
In the specification of linear regression models it is common to indicate a list of candidate variables from which a subset enters the model with nonzero coefficients. In some cases any combination...
Fractional Integration with Drift: Estimation in Small Samples (1996)
Anthony A. Smith, Fallaw Sowell, Stanley E. Zin, John Geweke, James Mackinnon
We examine the finite-sample behavior of estimators of the order of integration in a fractionally integrated time-series model. In particular, we compare exact time-domain likelihood estimation to...
Bayesian comparison of econometric models (1994)
This paper integrates and extends some recent computational advances in Bayesian inference with the objective of more fully realizing the Bayesian promise of coherent inference and model comparison...
This paper integrates and extends some recent computational advances in Bayesian inference with the objective of more fully realizing the Bayesian promise of coherent inference and model comparison...
Bayesian treatment of the independent Student-t linear model (1993)
This article takes up methods for Bayesian inference in a linear model in which the disturbances are independent and have identical Student-t distributions. It exploits the equivalence of the...
Bayesian treatment of the independent Student-t linear model (1993)
This article takes up methods for Bayesian inference in a linear model in which the disturbances are independent and have identical Student-t distributions. It exploits the equivalence of the...
Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments (1992)
John Geweke, J. O. Berger, A. P. Dawid
Data augmentation and Gibbs sampling are two closely related, sampling-based approaches to the calculation of posterior moments. The fact that each produces a sample whose constituents are neither...
Priors for macroeconomic time series and their application (1992)
This paper takes up Bayesian inference in a general trend stationary model for macroeconomic time series with independent Student-t disturbances. The model is linear in the data, but nonlinear in...
This paper takes up Bayesian inference in a general trend stationary model for macroeconomic time series with independent Student-t disturbances. The model is linear in the data, but nonlinear in...
This paper takes up Bayesian inference in a general trend stationary model for macroeconomic time series with independent Student-t disturbances. The model is linear in the data, but nonlinear in...
This paper takes up Bayesian inference in a general trend stationary model for macroeconomic time series with independent Student-t disturbances. The model is linear in the data, but nonlinear in...
The construction and implementation of a Gibbs sampler for efficient simulation from the truncated multivariate normal and Student-t distributions is described. It is shown how the accuracy and...
Employment turnover and wage dynamics in U.S. manufacturing, 1932-1972 / (1975)
Prepared under grant no. 91-27-75-03 for the Manpower Administration, U.S. Dept. of Labor.
Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments
Data augmentation and Gibbs sampling are two closely related, sampling-based approaches to the calculation of posterior moments. The fact that each produces a sample whose constituents are neither...
Alternative computational approaches to inference in the multinomial probit model
John Geweke, Michael Keane, David Runkle
This research compares several approaches to inference in the multinomial probit model, based on Monte-Carlo results for a seven choice model. The experiment compares the simulated maximum likelihood...
Measuring the pricing error of the arbitrage pricing theory
This paper provides an exact Bayesian framework for analyzing the arbitrage pricing theory (APT). Based on the Gibbs sampler, we show how to obtain the exact posterior distributions for functions of...
Monte Carlo simulation and numerical integration
This is a survey of simulation methods in economics, with a specific focus on integration problems. It describes acceptance methods, importance sampling procedures, and Markov chain Monte Carlo...
Computational techniques for applied econometric analysis of macroeconomic and financial processes
Geweke, John, Groenen, Patrick J.F., Paap, Richard, Van Dijk, Herman K.
Geweke, John, Whiteman, Charles, G. Elliott, C. Granger, A. Timmermann
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general requires explicit formulation of a model, and conditioning on known quantities, in...
Posterior Simulators in Econometrics
Economics is the discipline of using data to revise beliefs about economic issues. In Bayesian econometrics, the revision is conducted in accordance with the laws of probability, conditional on what...
Computational Experiments and Reality
A common practice in macroeconomics is to assess the validity of general equilibrium models by first deriving their implications for population moments and then comparing population moments with...
Using Simulation Methods for Bayesian Econometric Models
This paper surveys the fundamental principles of subjective Bayesian inference in econometrics and their implementation using posterior simulation methods. The emphasis is on the combination of...
Bayesian inference for hospital quality in a selection model
John Geweke, Gautam Gowrisankaran, Robert J. Town
This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are widely...
Hierarchical Markov normal mixture models with applications to financial asset returns
With the aim of constructing predictive distributions for daily returns, we introduce a new Markov normal mixture model in which the components are themselves normal mixtures. We derive the...
Using simulation methods for bayesian econometric models: inference, development,and communication
This paper surveys the fundamental principles of subjective Bayesian inference in econometrics and the implementation of those principles using posterior simulation methods. The emphasis is on the...
Bayesian Inference for Hospital Quality in a Selection Model
John Geweke, Gautam Gowrisankaran, Robert J. Town
This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and nonrandom selection. Mortality rates in patient discharge records are widely...
Bayesian Inference in Econometric Models Using Monte Carlo Integration.
Methods for the systematic application of Monte Carlo integration with importance sampling to Bayesian inference are developed. Conditions under which the numerical approximation converges almost...
Computationally intensive methods for integration in econometrics
Geweke, John, Keane, Michael, J.J. Heckman, E.E. Leamer
Until recently, inference in many interesting models was precluded by the requirement of high dimensional integration. But dramatic increases in computer speed, and the recent development of new...
Prior Density-Ratio Class Robustness in Econometrics.
This article provides a generic, very fast method for computing exact density-ratio class bounds on posterior expectations, given the output of a posterior simulator. It illustrates application of...
A variance screen for collusion
Abrantes-Metz, Rosa M., Froeb, Luke M., Geweke, John, Taylor, Christopher T.
Geweke, John, Martin, Donald L
Measuring the impact of potentially controllable factors on the willingness of youth to undertake health risks is important to informed public health policy decisions. Typically the only data linking...
Using simulation methods for Bayesian econometric models: inference, development, and communication
This paper surveys the fundamental principles of subjective Bayesian inference in econometrics and the implementation of those principles using posterior simulation methods. The emphasis is on the...
Priors for macroeconomic time series and their application
This paper takes up Bayesian inference in a general trend stationary model for macroeconomic time series with independent Student-t disturbances. The model is linear in the data, but nonlinear in...
Alternative Computational Approaches to Inference in the Multinomial Probit Model.
Geweke, John, Keane, Michael P, Runkle, David
This research compares several approaches to inference in the multinominal profit model, based on two Monte Carlo experiments for a seven choice model. The methods compared are the simulated maximum...
Measuring the Pricing Error of the Arbitrage Pricing Theory.
This article provides an exact Bayesian framework for analyzing the arbitrage pricing theory (APT). Based on the Gibbs sampler, we show how to obtain the exact posterior distributions for functions...
A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or...
Bayesian Inference for Hospital Quality in a Selection Model
John Geweke, Gautam Gowrisankaran, Robert J. Town
This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are widely...
Comparing and evaluating Bayesian predictive distributions of asset returns.
Bayesian inference in a time series model provides exact, out-of-sample predictive distributions that fully and coherently incorporate parameter uncertainty. This study compares and evaluates...
Priors for Macroeconomic Time Series and Their Application
This paper takes up Bayesian inference in a general trend stationary model for macroeconomic time series with independent Student-t disturbances. The model is linear in the data, but nonlinear in...
A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or...
A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or...
Economic Complexity: Chaos, Sunspots, Bubbles, and Nonlinearity
Barnett,William A., Geweke,John, Shell,Karl
The contents of this volume comprise the proceedings of the International Symposia in Economic Theory and Econometrics conference held in 1987 at the IC^T2 (Innovation, Creativity, and Capital)...
Economic Complexity: Chaos, Sunspots, Bubbles, and Nonlinearity
Barnett,William A., Geweke,John, Shell,Karl
The contents of this volume comprise the proceedings of the International Symposia in Economic Theory and Econometrics conference held in 1987 at the IC^T2 (Innovation, Creativity, and Capital)...