Risultati della ricerca
Creator: Boyd, John H. and Graham, Stanley L. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 378 Parola chiave: Securities , Risk, Real estate, Nonbank activities, Bank holding companies, and Insurance Soggetto: C15 - Statistical Simulation Methods: General and G21 - Banks; Depository Institutions; Micro Finance Institutions; Mortgages
Creator: Geweke, John Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 526 Parola chiave: Econometrics, Monte Carlo, and Simulation Soggetto: C15 - Statistical Simulation Methods: General and C63 - Computational Techniques; Simulation Modeling
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.
Parola chiave: Factor model, Capital asset pricing model, and Predictive odds Soggetto: C11 - Bayesian Analysis: General and C15 - Statistical Simulation Methods: General
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.
Parola chiave: Sequential Monte Carlo methods, Nonlinear filtering, Dynamic equilibrium economies, and Likelihood-based inference Soggetto: C11 - Bayesian Analysis: General, C10 - Econometric and Statistical Methods and Methodology: General, C13 - Estimation: General, and C15 - Statistical Simulation Methods: General
Creator: Kilian, Lutz and Ohanian, Lee E. Series: Staff report (Federal Reserve Bank of Minneapolis. Research Department) Number: 244 Abstract:
Unit root tests against trend break alternatives are based on the premise that the dating of the trend breaks coincides with major economic events with permanent effects on economic activity, such as wars and depressions. Standard economic theory, however, suggests that these events have large transitory, rather than permanent, effects on economic activity. Conventional unit root tests against trend break alternatives based on linear ARIMA models do not capture these transitory effects and can result in severely distorted inference. We quantify the size distortions for a simple model in which the effects of wars and depressions can reasonably be interpreted as transitory. Monte Carlo simulations show that in moderate samples, the widely used Zivot-Andrews (1992) test mistakes transitory dynamics for trend breaks with high probability. We conclude that these tests should be used only if there are no plausible economic explanations for apparent trend breaks in the data.
Parola chiave: Transitory Shocks, Unit Roots, and Trend-Breaks Soggetto: C15 - Statistical Simulation Methods: General, E32 - Business Fluctuations; Cycles, and C22 - Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Creator: Geweke, John, Keane, Michael P., and Runkle, David Edward Series: Staff report (Federal Reserve Bank of Minneapolis. Research Department) Number: 177 Abstract:
Statistical inference in multinomial multiperiod probit models has been hindered in the past by the high dimensional numerical integrations necessary to form the likelihood functions, posterior distributions, or moment conditions in these models. We describe three alternative approaches to inference that circumvent the integration problem: Bayesian inference using Gibbs sampling and data augmentation to compute posterior moments, simulated maximum likelihood (SML) estimation using the GHK recursive probability simulator, and method of simulated moment (MSM) estimation using the GHK simulator. We perform a set of Monte-Carlo experiments to compare the performance of these approaches. Although all the methods perform reasonably well, some important differences emerge. The root mean square errors (RMSEs) of the SML parameter estimates around the data generating values exceed those of the MSM estimates by 21 percent on average, while the RMSEs of the MSM estimates exceed those of the posterior parameter means obtained via agreement via Gibbs sampling by 18 percent on average. While MSM produces a good agreement between empirical RMSEs and asymptotic standard errors, the RMSEs of the SML estimates exceed the asymptotic standard errors by 28 percent on average. Also, the SML estimates of serial correlation parameters exhibit significant downward bias.
Parola chiave: Simulated maximum likelihood, Discrete choice, Panel data, Bayesian inference, Method of simulated moments, and Gibbs sampling Soggetto: C15 - Statistical Simulation Methods: General and C35 - Multiple or Simultaneous Equation Models: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions