Filtern nach: Invariant Markov process Entfernen Zwang Invariant Markov process Fach C11 - Econometric and statistical methods : General - Bayesian analysis Entfernen Zwang Fach: C11 - Econometric and statistical methods : General - Bayesian analysis
Creator: Litterman, Robert B. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Dept.) Number: 274 Stichwort: Bayesian analysis, BVAR, and Vector autoregression Fach: C11 - Econometric and statistical methods : General - Bayesian analysis and C53 - Econometric modeling - Forecasting and other model applications
Creator: Doan, Thomas., Litterman, Robert B., and Sims, Christopher A. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Dept.) Number: 243 Abstract: This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. The procedure is applied to ten macroeconomic variables and is shown to improve out-of-sample forecasts relative to univariate equations. Although cross-variables responses are damped by the prior, considerable interaction among the variables is shown to be captured by the estimates. We provide unconditional forecasts as of 1982:12 and 1963:3* We also describe how a model such as this can be used to make conditional projections and to analyse policy alternatives. As an example, we analyze a Congressional Budget Office forecast made in 1982:12. While no automatic causal interpretations arise from models like ours, they provide a detailed characterization of the dynamic statistical interdependence of a set of economic variables, which may help in evaluating causal hypotheses, without containing any such hypotheses themselves. Stichwort: Bayesian methods, Forecasting, and Macroeconomics Fach: C11 - Econometric and statistical methods : General - Bayesian analysis and E27 - Macroeconomics : Consumption, saving, production, employment, and investment - Forecasting and simulation
Creator: Geweke, John. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Dept.) 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. Beschreibung:
This paper was prepared for the inaugural Colin Clark Lecture, Australasian Meetings of the Econometric Society, July 1994.
Stichwort: Model comparison, Econometric modeling, Bayesian inference, and Computation Fach: C11 - Econometric and statistical methods : General - Bayesian analysis and C53 - Econometric modeling - Forecasting and other model applications
Creator: Roberds, William. Series: Business analysis committee meeting Abstract: One of the more significant developments in econometric modeling over the past decade has been the invention of the forecasting technique known as Bayesian vector autoregression (BVAR). This paper provides a detailed description of the process of specifying a BVAR model of quarterly time series on the U.S. macroeconomy. The postsample forecasting performance of the model is evaluated at an informal level by comparing the model's performance to certain naive forecasting methods, and is evaluated at a formal level by means of efficiency tests. Although the null hypothesis of efficiency is rejected for the model's forecasts, the accuracy of the model exceeds that of naive forecasting methods, and seems comparable to that of commercial forecasting firms for early quarter forecasts. Stichwort: BVAR, Vector autoregression, and Bayesian analysis Fach: C11 - Econometric and statistical methods : General - Bayesian analysis and C53 - Econometric modeling - Forecasting and other model applications
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. Stichwort: Dynamic equilibrium economies, Nonlinear filtering, Sequential Monte Carlo methods, and Likelihood-based inference Fach: C13 - Econometric and statistical methods : General - Estimation, C11 - Econometric and statistical methods : General - Bayesian analysis, C15 - Econometric and statistical methods : General - Simulation methods, and C10 - Econometric and statistical methods : General - General