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 combination of models and the development of predictive distributions. Moving beyond conditioning on a fixed number of completely specified models, the paper introduces subjective Bayesian tools for formal comparison of these models with as yet incompletely specified models. The paper then shows how posterior simulators can facilitate communication between investigators (for example, econometricians) on the one hand and remote clients (for example, decision makers) on the other, enabling clients to vary the prior distributions and functions of interest employed by investigators. A theme of the paper is the practicality of subjective Bayesian methods. To this end, the paper describes publicly available software for Bayesian inference, model development, and communication and provides illustrations using two simple econometric models.     

MODEL BUILDING AND DATA MINING

This paper defines the phenomenon of data mining in econometrics and discusses various outcomes of and solutions to data mining. Both classical and Bayesian approaches are considered, each with notable advantages and disadvantages, and with the choice of loss function affecting critical values. Illustrative examples include variable addition and exclusion in a standard linear regression model, the choice of lag structure in a dynamic single equation, and specification in a simultaneous equations model.     

Estimation and Properties of a Time-Varying EGARCH(1,1) in Mean Model

Time-varying GARCH-M models are commonly employed in econometrics and financial economics. Yet the recursive nature of the conditional variance makes likelihood analysis of these models computationally infeasible. This article outlines the issues and suggests to employ a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a simulated Bayesian solution in only O(T) computational operations, where T is the sample size. Furthermore, the theoretical dynamic properties of a time-varying-parameter EGARCH(1,1)-M are derived. We discuss them and apply the suggested Bayesian estimation to three major stock markets.     

Copula Density Estimation by Total Variation Penalized Likelihood

Copulas are full measures of dependence among random variables. They are increasingly popular among academics and practitioners in financial econometrics for modeling comovements between markets, risk factors, and other relevant variables. A copula's hidden dependence structure that couples a joint distribution with its marginals makes a parametric copula non-trivial. An approach to bivariate copula density estimation is introduced that is based on a penalized likelihood with a total variation penalty term. Adaptive choice of the amount of regularization is based on approximate Bayesian Information Criterion (BIC) type scores. Performance are evaluated through the Monte Carlo simulation.     

Posterior analysis of restricted seemingly unrelated regression equation models: a recursive analytical approach

The scope of exact analytical results in Bayesian econometrics is known to be quite limited. It is, however, shown here to be broader than the simple natural-conjugare framework. Restricting the coefficients of a SURE model in a recursive linear way can not be accommodated in a natural-conjugate analysis,but still allows for analytical ingerence, exploiting the recursive characteristics over equations. These finding are used to obtain analytical posterior results in a two-equation model for money and interest rate in the UK. Subsequent research shows that such methods can substantially increase both reliability and efficiency in the analysis of more complicated models than the ine under scrutiny here.     

Posterior analysis of restricted seemingly unrelated regression equation models: a recursive analytical approach

The scope of exact analytical results in Bayesian econometrics is known to be quite limited. It is, however, shown here to be broader than the simple natural-conjugare framework. Restricting the coefficients of a SURE model in a recursive linear way can not be accommodated in a natural-conjugate analysis,but still allows for analytical ingerence, exploiting the recursive characteristics over equations. These finding are used to obtain analytical posterior results in a two-equation model for money and interest rate in the UK. Subsequent research shows that such methods can substantially increase both reliability and efficiency in the analysis of more complicated models than the ine under scrutiny here.     

Focused estimation for noisy and small data sets: a Bayesian minimum expected loss estimator approach

Central to many inferential situations is the estimation of rational functions of parameters. The mainstream in statistics and econometrics estimates these quantities based on the plug‐in approach without consideration of the main objective of the inferential situation. We propose the Bayesian Minimum Expected Loss (MELO) approach focusing explicitly on the function of interest, and calculating its frequentist variability. Asymptotic properties of the MELO estimator are similar to the plug‐in approach. Nevertheless, simulation exercises show that our proposal is better in situations characterised by small sample sizes and/or noisy data sets. In addition, we observe in the applications that our approach gives lower standard errors than frequently used alternatives when data sets are not very informative.     

A Class of Non-Gaussian State Space Models With Exact Likelihood Inference

The likelihood function of a general nonlinear, non-Gaussian state space model is a high-dimensional integral with no closed-form solution. In this article, I show how to calculate the likelihood function exactly for a large class of non-Gaussian state space models that include stochastic intensity, stochastic volatility, and stochastic duration models among others. The state variables in this class follow a nonnegative stochastic process that is popular in econometrics for modeling volatility and intensities. In addition to calculating the likelihood, I also show how to perform filtering and smoothing to estimate the latent variables in the model. The procedures in this article can be used for either Bayesian or frequentist estimation of the model’s unknown parameters as well as the latent state variables. Supplementary materials for this article are available online.     

空间计量经济学理论体系的解析及其展望

随着空间经济理论研究出现重大突破,空间计量经济学也从边缘走向现代计量经济学的主流。重点对空间计量经济模型的设立包括空间横截面数据模型、空间面板数据模型以及空间离散数据模型进行讨论,对模型参数估计方法包括最大似然估计法、两阶段最小二乘法和矩估计法等进行分析,对模型检验方法包括Moran’s I方法和LM/RS方法等内容进行总结,最后展望了该理论研究未来的发展趋势。     

Past,present and future of econometrics

In this article salient aspects of the past, present and future of econometrics are considered. These include a resumé of past key developments in econometric modeling, inference and uses of econometrics. Further, some comments are made relating to various statistical inference procedures, techniques of model formulation and the relations of theory and application. It is concluded that a stronger interaction between theory and application will do much to promote further progress in econometrics in the future.     

Great Expectatrics: Great Papers,Great Journals,Great Econometrics

The article discusses alternative Research Assessment Measures (RAM), with an emphasis on the Thomson Reuters ISI Web of Science database (hereafter ISI). Some analysis and comparisons are also made with data from the SciVerse Scopus database. The various RAM that are calculated annually or updated daily are defined and analyzed, including the classic 2-year impact factor (2YIF), 2YIF without journal self-citations (2YIF*), 5-year impact factor (5YIF), Immediacy (or zero-year impact factor (0YIF)), Impact Factor Inflation (IFI), Self-citation Threshold Approval Rating (STAR), Eigenfactor score, Article Influence, C3PO (Citation Performance Per Paper Online), h-index, Zinfluence, and PI-BETA (Papers Ignored – By Even The Authors). The RAM are analyzed for 10 leading econometrics journals and 4 leading statistics journals. The application to econometrics can be used as a template for other areas in economics, for other scientific disciplines, and as a benchmark for newer journals in a range of disciplines. In addition to evaluating high quality research in leading econometrics journals, the paper also compares econometrics and statistics, alternative RAM, highlights the similarities and differences of the alternative RAM, finds that several RAM capture similar performance characteristics for the leading econometrics and statistics journals, while the new PI-BETA criterion is not highly correlated with any of the other RAM, and hence conveys additional information regarding RAM, highlights major research areas in leading journals in econometrics, and discusses some likely future uses of RAM, and shows that the harmonic mean of 13 RAM provides more robust journal rankings than relying solely on 2YIF.     

空间计量理论与应用研究综述

文章首先从模型识别、估计、检验、预测等方面介绍了空间计量经济学的最新研究现状。选取空间计量经济学应用最多的知识溢出、区域金融领域从实证角度进行文献综述。最后指出空间计量理论与应用领域的研究方向。     

经济统计学与计量经济学等相关学科的关系及发展前景

本文从统计学和经济学统一的视角,分析与阐述经济统计学与计量经济学等相关学科——概率论、数理统计学、计量经济学以及经济理论（包括数理经济学）之间的相互关系及发展前景。作为从样本信息推断母体特征的一般方法论,数理统计学由于符合人类科学研究的过程与需要,因而在自然科学和社会科学的很多领域得到了广泛应用。计量经济学是经济实证研究的推断方法论。经济统计学与计量经济学一起,构成经济实证研究完整的方法论,其中,作为经济测度方法论,经济统计学不仅提供定量描述经济实际运行的理论、方法与工具,它也是经济实证研究的先决条件与基础,是计量经济学理论发展的一个重要推动力量。经济统计学面临不少挑战,但有深厚的学科根基与巨大的发展空间,其作用是任何相关学科均不能替代的。统计学各个分支的交叉融合,将推动经济统计学和计量经济学的共同发展,从而进一步提升中国经济学实证研究的水平与科学性。     

空间计量模型选择及其模拟分析

空间计量模型的选择是空间计量建模的一个重要组成部分,也是空间计量模型实证分析的关键步骤。本文对空间计量模型选择中的Moran指数检验、LM检验、似然函数、三大信息准则、贝叶斯后验概率、马尔可夫链蒙特卡罗方法做了详细的理论分析。并在此基础之上,通过Matlab编程进行模拟分析,结果表明：在扩充的空间计量模型族中进行模型选择时,基于OLS残差的Moran指数与LM检验均存在较大的局限性,对数似然值最大原则缺少区分度,LM检验只针对SEM和SAR模型的区分有效,信息准则对大多数模型有效,但是也会出现误选。而当给出恰当的M-H算法时,充分利用了似然函数和先验信息的MCMC方法,具有更高的检验效度,特别是在较大的样本条件下得到了完全准确的判断,且对不同阶空间邻接矩阵的空间计量模型的选择也非常有效。     

Bayesian Dynamic Factor Models and Portfolio Allocation

We discuss the development of dynamic factor models for multivariate financial time series, and the incorporation of stochastic volatility components for latent factor processes. Bayesian inference and computation is developed and explored in a study of the dynamic factor structure of daily spot exchange rates for a selection of international currencies. The models are direct generalizations of univariate stochastic volatility models and represent specific varieties of models recently discussed in the growing multivariate stochastic volatility literature. We discuss model fitting based on retrospective data and sequential analysis for forward filtering and short-term forecasting. Analyses are compared with results from the much simpler method of dynamic variance-matrix discounting that, for over a decade, has been a standard approach in applied financial econometrics. We study these models in analysis, forecasting, and sequential portfolio allocation for a selected set of international exchange-rate-return time series. Our goals are to understand a range of modeling questions arising in using these factor models and to explore empirical performance in portfolio construction relative to discount approaches. We report on our experiences and conclude with comments about the practical utility of structured factor models and on future potential model extensions.     

空间面板数据模型设定问题分析

空间面板数据模型将空间计量经济学和面板数据方法相结合,不仅同时考虑时空特征,而且将空间效应纳入研究体系,成为当前计量经济学的热点研究领域,但其模型设定、参数估计及模型检验也更为复杂,实证研究中往往出现模型设定偏误等问题。因此,基于空间面板数据模型的前沿理论,重点探讨模型设定中的常见问题,包括空间滞后模型与空间误差模型的选择、随机效应与固定效应的选择以及模型拟合优度的选择与比较,为模型的应用和新模型的扩展提供理论依据和参考。     

University teaching of econometrics a personal view

University teachers of economics have generally been keen to propound, discuss and criticise alternative points of view on ways of structuring courses in their discipline. Econometricians, by contrast, have in this respect been very neglectful - though their subject has been a university discipline for over a quarter of a century.

This paper appraises broadly the issues relevant to the design of an academic program in econometrics, at both undergraduate and graduate levels, and proposes ways in which a blended education in economics and econometrics may best be offered.     

University teaching of econometrics a personal view

University teachers of economics have generally been keen to propound, discuss and criticise alternative points of view on ways of structuring courses in their discipline. Econometricians, by contrast, have in this respect been very neglectful - though their subject has been a university discipline for over a quarter of a century.

This paper appraises broadly the issues relevant to the design of an academic program in econometrics, at both undergraduate and graduate levels, and proposes ways in which a blended education in economics and econometrics may best be offered.     

计量经济学和统计学视角下的线性回归模型——再议线性回归模型的设定

从属性、构建方法及意义等方面,分析研究线性回归模型在计量经济学和统计学两学科视角下的差异,并根据这种差异进一步提出回归模型的基本设定思路。研究表明:识别这种差异是完成模型设定工作的基础性和必要性举措,有助于实现线性回归模型的正确设定。以经典例证对计量经济学和统计学回归模型在应用中的区别以及模型设定问题进行进一步展示和分析。     

Peter Schmidt: Econometrician and consummate professional

Peter Schmidt has been one of its best-known and most respected econometricians in the profession for four decades. He has brought his talents to many scholarly outlets and societies, and has played a foundational and constructive role in the development of the field of econometrics. Peter Schmidt has also served and led the development of Econometric Reviews since its inception in 1982. His judgment has always been fair, informed, clear, decisive, and constructive. Respect for ideas and scholarship of others, young and old, is second nature to him. This is the best of traits, and Peter serves as an uncommon example to us all. The seventeen articles that make up this Econometric Reviews Special Issue in Honor of Peter Schmidt represent the work of fifty of the very best econometricians in our profession. They honor Professor Schmidt's lifelong accomplishments by providing fundamental research work that reflects many of the broad research themes that have distinguished his long and productive career. These include time series econometrics, panel data econometrics, and stochastic frontier production analysis.