Creator: Duncan, George T. and Lin, Lizbie Gee-Sun Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 000 描述:
This paper was published with no issue number.
关键词: Time series and Entry and exit 学科: C12 - Hypothesis Testing: General and C32 - Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Creator: Geweke, John and Keane, Michael P. Series: Staff report (Federal Reserve Bank of Minneapolis. Research Department) Number: 237 Abstract:
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 by increasing the number of distributions in the mixture these models effectively remove the normality assumption and are much closer to semiparametric models. When a Bayesian approach is taken, there is an exact finite-sample distribution theory for the choice probability conditional on the covariates. The paper uses artificial data to show how posterior odds ratios can discriminate between normal and nonnormal distributions in probit models. The method is also applied to female labor force participation decisions in a sample with 1,555 observations from the PSID. In this application, Bayes factors strongly favor mixture of normals probit models over the conventional probit model, and the most favored models have mixtures of four normal distributions for the disturbance term.
关键词: Normal mixture, Discrete choice, and Markov chain Monte Carlo 学科: C25 - Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities and C11 - Bayesian Analysis: General
Creator: Christiano, Lawrence J. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 415 Abstract:
This article studies the accuracy of two versions of Kydland and Prescott's (1980, 1982) procedure for approximating optimal decision rules in problems in which the objective fails to be quadratic and the constraints fail to be linear. The analysis is carried out using a version of the Brock-Mirman (1972) model of optimal economic growth. Although the model is not linear quadratic, its solution can nevertheless be computed with arbitrary accuracy using a variant of existing value-function iteration procedures. I find the Kydland-Prescott approximate decision rules are very similar to those implied by value-function iteration.
关键词: Production function, Optimization, Growth model, Markov chain, State space, and Decision rule 学科: C40 - Econometric and Statistical Methods: Special Topics: General
Creator: Geweke, John and Petrella, Lea Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 553 Abstract:
This paper provides a general and efficient method for computing density ratio class bounds on posterior moments, given the output of a posterior simulator. It shows how density ratio class bounds for posterior odds ratios may be formed in many situations, also on the basis of posterior simulator output. The computational method is used to provide density ratio class bounds in two econometric models. It is found that the exact bounds are approximated poorly by their asymptotic approximation, when the posterior distribution of the function of interest is skewed. It is also found that posterior odds ratios display substantial variation within the density ratio class, in ways that cannot be anticipated by the asymptotic approximation.
关键词: Bayesian inference, Markov-chain Monte Carlo, Normal mixture, and Probit model 学科: C11 - Bayesian Analysis: General and C63 - Computational Techniques; Simulation Modeling
Creator: Geweke, John Series: Staff report (Federal Reserve Bank of Minneapolis. Research Department) Number: 192 Abstract:
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 methods for simulation from univariate and multivariate distributions and their application to the approximation of integrals. The exposition gives emphasis to combinations of different approaches and assessment of the accuracy of numerical approximations to integrals and expectations. The survey illustrates these procedures with applications to simulation and integration problems in economics.
Creator: Geweke, John Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 532 Abstract:
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 in economics. It combines Markov chain Monte Carlo and independence Monte Carlo with importance sampling to provide an efficient and generic method for updating posterior distributions. It exploits the multiplicative decomposition of marginalized likelihood into predictive factors, to compute posterior odds ratios efficiently and with minimal further investment in software. It argues for the use of predictive odds ratios in model comparison in economics. Finally, it suggests procedures for public reporting that will enable remote clients to conveniently modify priors, form posterior expectations of their own functions of interest, and update the posterior distribution with new observations. A series of examples explores the practicality and efficiency of these methods.
This paper was prepared for the inaugural Colin Clark Lecture, Australasian Meetings of the Econometric Society, July 1994.
关键词: Computation, Model comparison, Bayesian inference, and Econometric modeling 学科: C53 - Forecasting Models; Simulation Methods and C11 - Bayesian Analysis: General
Creator: Schulhofer-Wohl, Sam Series: Staff Reports (Federal Reserve Bank of Minneapolis) Number: 462 Abstract:
This appendix contains seven sections. Section A reports results from running regressions of labor earnings on GDP using data from the PSID, for comparison with the results using HRS data in the body of the paper. Section B examines the relationship between family income, aggregate shocks, and risk preferences in the PSID. Section C gives technical details on the Markov Chain Monte Carlo estimation employed in table 1 of the paper and reports the complete parameter estimates for the regressions summarized in that table. Section D reports results when the relationship between earnings and aggregate shocks is estimated with individual-specific coecients rather than common coecients for each risk-tolerance group. Section E reports results comparable to table 1 of the paper and table D.1 of this appendix using only Social Security covered earnings instead of the combination of Social Security and W-2 earnings. Section F reports robustness checks for tables 2 and 3 of the paper under alternative definitions of the household and the consumption and income variables. Section G reports robustness checks for tables 2 and 3 under an alternative definition of the leisure variable.
关键词: Risk preferences, Heterogeneity, Imperfect insurance, and Risk sharing 学科: E21 - Macroeconomics : Consumption, saving, production, employment, and investment - Consumption ; Saving ; Wealth and E24 - Macroeconomics : Consumption, saving, production, employment, and investment - Employment ; Unemployment ; Wages ; Intergenerational income distribution ; Aggregate human capital
Creator: Fernandez-Villaverde, Jesus and Rubio-Ramírez, Juan Francisco Series: Joint committee on business and financial analysis Abstract:
This paper presents a method to perform likelihood-based inference in nonlinear dynamic equilibrium economies. This type of models has become a standard tool in quantitative economics. However, existing literature has been forced so far to use moment procedures or linearization techniques to estimate these models. This situation is unsatisfactory: moment procedures suffer from strong small samples biases and linearization depends crucially on the shape of the true policy functions, possibly leading to erroneous answers. We propose the use of Sequential Monte Carlo methods to evaluate the likelihood function implied by the model. Then we can perform likelihood-based inference, either searching for a maximum (Quasi-Maximum Likelihood Estimation) or simulating the posterior using a Markov Chain Monte Carlo algorithm (Bayesian Estimation). We can also compare different models even if they are nonnested and misspecified. To perform classical model selection, we follow Vuong (1989) and use the Kullback-Leibler distance to build Likelihood Ratio Tests. To perform Bayesian model comparison, we build Bayes factors. As an application, we estimate the stochastic neoclassical growth model.
关键词: Sequential Monte Carlo methods, Nonlinear filtering, Dynamic equilibrium economies, and Likelihood-based inference 学科: C11 - Bayesian Analysis: General, C10 - Econometric and Statistical Methods and Methodology: General, C13 - Estimation: General, and C15 - Statistical Simulation Methods: General
Creator: Geweke, John Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 540 Abstract:
The reduced rank regression model arises repeatedly in theoretical and applied econometrics. To date the only general treatment of this model have been frequentist. This paper develops general methods for Bayesian inference with noninformative reference priors in this model, based on a Markov chain sampling algorithm, and procedures for obtaining predictive odds ratios for regression models with different ranks. These methods are used to obtain evidence on the number of factors in a capital asset pricing model.
关键词: Factor model, Capital asset pricing model, and Predictive odds 学科: C11 - Bayesian Analysis: General and C15 - Statistical Simulation Methods: General
Creator: Todd, Richard M. Series: Business analysis committee meeting 描述:
Version without Software Appendix appears on the Federal Reserve Bank of Minneapolis Web site at http://www.minneapolisfed.org/publications_papers/pub_display.cfm?id=571
关键词: BVAR, Vector autoregression, and Bayesian analysis 学科: C53 - Econometric modeling - Forecasting and other model applications