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用于推断与优化的序贯自适应贝叶斯学习算法AI 解读

The sequentially adaptive Bayesian learning algorithm (SABL) builds on and ties together ideas from sequential Monte Carlo and simulated annealing. The algorithm can be used to simulate from Bayesian posterior distributions, using either data tempering or power tempering, or for optimization. A key feature of SABL is that the introduction of information is adaptive and controlled, ensuring that the algorithm performs reliably and efficiently in a wide variety of applications…

利用多个宏观经济模型进行预测AI 解读

We establish methods that improve the predictions of macroeconometric models—dynamic factor models, dynamic stochastic general equilibrium models, and vector autoregressions—using a quarterly U.S. data set. We measure prediction quality with one-step-ahead probability densities assigned in real time. Two steps lead to substantial improvements: (a) the use of full Bayesian predictive distributions rather than conditioning on the posterior mode for parameters and (b) the use…

利用分数多项式逼近正规经济函数的似然推断AI 解读

This paper shows that regular fractional polynomials can approximate regular cost, production and utility functions and their first two derivatives on closed compact subsets of the strictly positive orthant of Euclidean space arbitrarily well. These functions therefore can provide reliable approximations to demand functions and other economically relevant characteristics of tastes and technology. Using canonical cost function data, it shows that full Bayesian inference for…

离散选择实验中单调偏好的非参数贝叶斯建模AI 解读

Discrete choice experiments are widely used to learn about the distribution of individual preferences for product attributes. Such experiments are often designed and conducted deliberately for the purpose of designing new products. There is a long-standing literature on nonparametric and Bayesian modelling of preferences for the study of consumer choice when there is a market for each product, but this work does not apply when such markets fail to exist as is the case with…

最优预测组合AI 解读

We consider the properties of weighted linear combinations of prediction models, or linear pools, evaluated using the log predictive scoring rule. Although exactly one model has limiting posterior probability, an optimal linear combination typically includes several models with positive weights. We derive several interesting results: for example, a model with positive weight in a pool may have zero weight if some other models are deleted from that pool. The results are…

多重结构断点模型中的推断与预测AI 解读

This paper develops a new Bayesian approach to structural break modeling. The focuses of the approach are the modeling of in-sample structural breaks and forecasting time series allowing out-of-sample breaks. The model has several desirable features. First, the number of regimes is not fixed but is treated as a random variable. Second, the model adopts a hierarchical prior for regime coefficients, which allows for the coefficients of one regime to contain information about…

分层马尔可夫正态混合模型及金融资产收益应用AI 解读

Motivated by the common problem of constructing predictive distributions for daily asset returns over horizons of one to several trading days, this article introduces a new model for time series. This model is a generalization of the Markov normal mixture model in which the mixture components are themselves normal mixtures, and it is a specific case of an artificial neural network model with two hidden layers. The article uses the model to construct predictive distributions…

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后验模拟器的正确性检验AI 解读

Analytical or coding errors in posterior simulators can produce reasonable but incorrect approximations of posterior moments. This article develops simple tests of posterior simulators that detect both kinds of errors, and uses them to detect and correct errors in two previously published articles. The tests exploit the fact that a Bayesian model specifies the joint distribution of observables (data) and unobservables (parameters). There are two joint distribution…

选择模型中医院质量的贝叶斯推断AI 解读

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 used to infer hospital quality. However, hospital admission is not random and some hospitals may attract patients with greater unobserved severity of illness than others. In this situation the assumption of random admission leads to spurious inference about hospital quality. This…

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导言：推断与决策AI 解读

A major focus of applied econometric work is decision making in the context of uncertainty.Sometimes this work is several steps removed from the actual decision.For example, it may entail providing information in a form more suited to decision making, or reducing the uncertainty in the decision-making environment.Increasingly, applied econometricians are becoming more directly involved in decision making.The quality and quantity of data available are increasing, while the…

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计量经济学中先验密度比类的稳健性AI 解读

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 the method in an econometric model of typical complexity. In this model, the exact bounds for expectations of some functions of interest are well approximated by the established asymptotic approximation but others are not. Software for the computations is publicly available in a…

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宏观经济时间序列的先验及其应用AI 解读

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 parameters. An informative but nonconjugate family of prior distributions for the parameters is introduced, indexed by a single parameter that can be readily elicited. The main technical contribution is the construction of posterior moments, densities, and odd ratios by using a…

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