Bayesian Model Averaging and exchange rate forecasts

Exchange rate forecasting is hard and the seminal result of Meese and Rogoff [Meese, R., Rogoff, K., 1983. Empirical exchange rate models of the seventies: Do they fit out of sample? Journal of International Economics 14, 3–24] that the exchange rate is well approximated by a driftless random walk, at least for prediction purposes, still stands despite much effort at constructing other forecasting models. However, in several other macro and financial forecasting applications, researchers in recent years have considered methods for forecasting that effectively combine the information in a large number of time series. In this paper, I apply one such method for pooling forecasts from several different models, Bayesian Model Averaging, to the problem of pseudo out-of-sample exchange rate predictions. For most currency–horizon pairs, the Bayesian Model Averaging forecasts using a sufficiently high degree of shrinkage, give slightly smaller out-of-sample mean square prediction error than the random walk benchmark. The forecasts generated by this model averaging methodology are however very close to, but not identical to, those from the random walk forecast.     

Growth Empirics in Panel Data Under Model Uncertainty and Weak Exogeneity

This paper considers panel growth regressions in the presence of model uncertainty and reverse causality concerns. For this purpose, my econometric framework combines Bayesian model averaging with a suitable likelihood function for dynamic panel models with weakly exogenous regressors and fixed effects. An application of this econometric methodology to a panel of countries over the 1960–2000 period highlights the difficulties in identifying the sources of economic growth by means of cross‐country regressions. In particular, none of the nine candidate regressors considered can be labeled as a robust determinant of economic growth. Moreover, the estimated rate of conditional convergence is indistinguishable from zero. Copyright © 2014 John Wiley & Sons, Ltd.     

Bayesian Model Averaging: A Systematic Review and Conceptual Classification

Bayesian model averaging (BMA) provides a coherent and systematic mechanism for accounting for model uncertainty. It can be regarded as an direct application of Bayesian inference to the problem of model selection, combined estimation and prediction. BMA produces a straightforward model choice criterion and less risky predictions. However, the application of BMA is not always straightforward, leading to diverse assumptions and situational choices on its different aspects. Despite the widespread application of BMA in the literature, there were not many accounts of these differences and trends besides a few landmark revisions in the late 1990s and early 2000s, therefore not accounting for advancements made in the last decades. In this work, we present an account of these developments through a careful content analysis of 820 articles in BMA published between 1996 and 2016. We also develop a conceptual classification scheme to better describe this vast literature, understand its trends and future directions and provide guidance for the researcher interested in both the application and development of the methodology. The results of the classification scheme and content review are then used to discuss the present and future of the BMA literature.     

Returns to Schooling and Bayesian Model Averaging: A Union of Two Literatures

Abstract.  In this paper, we review and unite the literatures on returns to schooling and Bayesian model averaging. We observe that most studies seeking to estimate the returns to education have done so using particular (and often different across researchers) model specifications. Given this, we review Bayesian methods which formally account for uncertainty in the specification of the model itself, and apply these techniques to estimate the economic return to a college education. The approach described in this paper enables us to determine those model specifications which are most favored by the given data, and also enables us to use the predictions obtained from all of the competing regression models to estimate the returns to schooling. The reported precision of such estimates also account for the uncertainty inherent in the model specification. Using U.S. data from the National Longitudinal Survey of Youth (NLSY), we also revisit several 'stylized facts' in the returns to education literature and examine if they continue to hold after formally accounting for model uncertainty.     

Methods and Tools for Bayesian Variable Selection and Model Averaging in Normal Linear Regression

In this paper, we briefly review the main methodological aspects concerned with the application of the Bayesian approach to model choice and model averaging in the context of variable selection in regression models. This includes prior elicitation, summaries of the posterior distribution and computational strategies. We then examine and compare various publicly available R‐packages, summarizing and explaining the differences between packages and giving recommendations for applied users. We find that all packages reviewed (can) lead to very similar results, but there are potentially important differences in flexibility and efficiency of the packages.     

基于动态模型平均的中国通货膨胀实时预测

本文研究了动态模型平均方法 (DMA)及其参数估计。DMA方法允许方程所含变量、变量系数及模型所含方程同时变动,适用于对宏观经济指标进行实时预测。本文利用DMA对中国通货膨胀进行实时预测表明,DMA方法下的中国通货膨胀预测解释变量处于0～3;以CPI指数和GDP平减指数作为通货膨胀衡量指标的情况下,不同预测期的解释变量被包含概率是时变的;遗忘因子为0.95时,利用DMA方法对我国通货膨胀的预测效果最佳,优于贝叶斯模型平均和时变向量自回归模型。     

基于贝叶斯组合模型的物流需求预测及分析

为克服单一预测方法假设条件及适用范围存在局限性的不足,以贝叶斯概率模型为基础,建立了基于GM(1,1)和多元线性回归模型的贝叶斯组合预测模型,各模型权重能够根据前期预测误差进行自适应调整,以保证预测精度.以北京市2009-2013年物流需求预测为例进行实例分析,结果表明贝叶斯组合预测模型的平均预测误差为0.95%,模型具有良好的自适应性和动态调整性,预测精度较高,可应用于物流需求预测研究.     

Interconnections Between Eurozone and US Booms and Busts Using a Bayesian Panel Markov‐Switching VAR Model

The proposed panel Markov‐switching VAR model accommodates changes in low and high data frequencies and incorporates endogenous time‐varying transition matrices of country‐specific Markov chains, allowing for interconnections. An efficient multi‐move sampling algorithm draws time‐varying Markov‐switching chains. Using industrial production growth and credit spread data, several important data features are obtained. Three regimes appear, with slow growth becoming persistent in the eurozone. Turning point analysis indicates the USA leading the eurozone cycle. Amplification effects influence recession probabilities for Eurozone countries. A credit shock results in temporary negative industrial production growth in Germany, Spain and the USA. Core and peripheral countries exist in the eurozone. Copyright © 2016 John Wiley & Sons, Ltd.     

Outlier‐Robust Bayesian Multinomial Choice Modeling

A Bayesian method for outlier‐robust estimation of multinomial choice models is presented. The method can be used for both correlated as well as uncorrelated choice alternatives and guarantees robustness towards outliers in the dependent and independent variables. To account for outliers in the response direction, the fat‐tailed multivariate Laplace distribution is used. Leverage points are handled via a shrinkage procedure. A simulation study shows that estimation of the model parameters is less influenced by outliers compared to non‐robust alternatives. An analysis of margarine scanner data shows how our method can be used for better pricing decisions. Copyright © 2015 John Wiley & Sons, Ltd.     

Optimal Portfolio Choice Under Decision‐Based Model Combinations

We propose a density combination approach featuring combination weights that depend on the past forecast performance of the individual models entering the combination through a utility‐based objective function. We apply this model combination scheme to forecast stock returns, both at the aggregate level and by industry, and investigate its forecasting performance relative to a host of existing combination methods, both within the class of linear and time‐varying coefficients, stochastic volatility models. Overall, we find that our combination scheme produces markedly more accurate predictions than the existing alternatives, both in terms of statistical and economic measures of out‐of‐sample predictability. Copyright © 2016 John Wiley & Sons, Ltd.     

Finding Bayesian Optimal Designs for Nonlinear Models: A Semidefinite Programming‐Based Approach

This paper uses semidefinite programming (SDP) to construct Bayesian optimal design for nonlinear regression models. The setup here extends the formulation of the optimal designs problem as an SDP problem from linear to nonlinear models. Gaussian quadrature formulas (GQF) are used to compute the expectation in the Bayesian design criterion, such as D‐, A‐ or E‐optimality. As an illustrative example, we demonstrate the approach using the power‐logistic model and compare results in the literature. Additionally, we investigate how the optimal design is impacted by different discretising schemes for the design space, different amounts of uncertainty in the parameter values, different choices of GQF and different prior distributions for the vector of model parameters, including normal priors with and without correlated components. Further applications to find Bayesian D‐optimal designs with two regressors for a logistic model and a two‐variable generalised linear model with a gamma distributed response are discussed, and some limitations of our approach are noted.     

Plant Scale and Exchange‐Rate‐Induced Productivity Growth

This paper examines the impact of exchange rate movements on firm‐level productivity through changes in the scale of production. We employ plant‐level data to examine whether, and in what direction, exchange rate movements affect the scale of production, and how these changes in scale influence productivity. The paper finds that a real appreciation of the domestic currency reduces shipments and this negative effect is larger for exporters (both domestic and foreign owned). The paper also finds evidence that the appreciation‐induced reduction of scale negatively affects productivity at the plant level. This scale effect more than offsets any potential gains from the appreciation‐induced reduction in the price of imported inputs.     

基于贝叶斯网络的公路工程造价指数预测模型研究

本文拟通过贝叶斯网络的结构学习建立造价指数影响因素间的因果关系,通过参数学习建立影响因素之间的条件概率分布;并以此为基础构建基于贝叶斯网络的造价指数预测专家系统,为造价指数的动态变化提供一种新方法.     

一种基于MCMC稳态模拟的贝叶斯索赔校正模型

Btihlmann模型是贝叶斯方法在经验费率厘定中最为著名的应用，然而该模型在结构参数先验信息不足的情况下，并不能得出参数的无偏后验估计。本文针对传统方法的不足，运用基于MCMC模拟的贝叶斯方法对历史数据进行校正，通过Gibbs抽样构造出一种多层Poisson模型稳态分布的马尔可夫链，动态模拟出索赔频率的后验分布以及缺失参数值的后验估计，改进了传统的索赔校正模型，提高了计算的精度。利用WinBUGS软件包进行建模分析，证明了该模型的直观性与有效性。     

基于贝叶斯网络的公路工程造价指数预测模型研究

本文拟通过贝叶斯网络的结构学习建立造价指数影响因素间的因果关系,通过参数学习建立影响因素之间的条件概率分布;并以此为基础构建基于贝叶斯网络的造价指数预测专家系统,为造价指数的动态变化提供一种新方法.     

基于贝叶斯网络的公路工程造价指数预测模型研究

本文拟通过贝叶斯网络的结构学习建立造价指数影响因素间的因果关系,通过参数学习建立影响因素之间的条件概率分布;并以此为基础构建基于贝叶斯网络的造价指数预测专家系统,为造价指数的动态变化提供一种新方法.     

Bayesian Estimation of Willingness‐to‐pay Where Respondents Mis‐report Their Preferences*

We introduce a modified conditional logit model that takes account of uncertainty associated with mis‐reporting in revealed preference experiments estimating willingness‐to‐pay (WTP). Like Hausman et al. [Journal of Econometrics (1988) Vol. 87, pp. 239–269], our model captures the extent and direction of uncertainty by respondents. Using a Bayesian methodology, we apply our model to a choice modelling (CM) data set examining UK consumer preferences for non‐pesticide food. We compare the results of our model with the Hausman model. WTP estimates are produced for different groups of consumers and we find that modified estimates of WTP, that take account of mis‐reporting, are substantially revised downwards. We find a significant proportion of respondents mis‐reporting in favour of the non‐pesticide option. Finally, with this data set, Bayes factors suggest that our model is preferred to the Hausman model.