Forecasting large datasets with Bayesian reduced rank multivariate models

The paper addresses the issue of forecasting a large set of variables using multivariate models. In particular, we propose three alternative reduced rank forecasting models and compare their predictive performance for US time series with the most promising existing alternatives, namely, factor models, large‐scale Bayesian VARs, and multivariate boosting. Specifically, we focus on classical reduced rank regression, a two‐step procedure that applies, in turn, shrinkage and reduced rank restrictions, and the reduced rank Bayesian VAR of Geweke ( 1996 ). We find that using shrinkage and rank reduction in combination rather than separately improves substantially the accuracy of forecasts, both when the whole set of variables is to be forecast and for key variables such as industrial production growth, inflation, and the federal funds rate. The robustness of this finding is confirmed by a Monte Carlo experiment based on bootstrapped data. We also provide a consistency result for the reduced rank regression valid when the dimension of the system tends to infinity, which opens the way to using large‐scale reduced rank models for empirical analysis. Copyright © 2010 John Wiley & Sons, Ltd.     

Forecasting with Bayesian Vector Autoregressions Estimated Using Professional Forecasts

We propose a Bayesian shrinkage approach for vector autoregressions (VARs) that uses short‐term survey forecasts as an additional source of information about model parameters. In particular, we augment the vector of dependent variables by their survey nowcasts, and claim that each variable modelled in the VAR and its nowcast are likely to depend in a similar way on the lagged dependent variables. In an application to macroeconomic data, we find that the forecasts obtained from a VAR fitted by our new shrinkage approach typically yield smaller mean squared forecast errors than the forecasts obtained from a range of benchmark methods. Copyright © 2015 John Wiley & Sons, Ltd.     

Forecasting the UK economy with a medium-scale Bayesian VAR

We estimate a Bayesian VAR (BVAR) for the UK economy and assess its performance in forecasting GDP growth and CPI inflation in real time relative to forecasts from COMPASS, the Bank of England’s DSGE model, and other benchmarks. We find that the BVAR outperformed COMPASS when forecasting both GDP and its expenditure components. In contrast, their performances when forecasting CPI were similar. We also find that the BVAR density forecasts outperformed those of COMPASS, despite under-predicting inflation at most forecast horizons. Both models over-predicted GDP growth at all forecast horizons, but the issue was less pronounced in the BVAR. The BVAR’s point and density forecast performances are also comparable to those of a Bank of England in-house statistical suite for both GDP and CPI inflation, as well as to the official Inflation Report projections. Our results are broadly consistent with the findings of similar studies for other advanced economies.     

Forecasting with Global Vector Autoregressive Models: a Bayesian Approach

This paper develops a Bayesian variant of global vector autoregressive (B‐GVAR) models to forecast an international set of macroeconomic and financial variables. We propose a set of hierarchical priors and compare the predictive performance of B‐GVAR models in terms of point and density forecasts for one‐quarter‐ahead and four‐quarter‐ahead forecast horizons. We find that forecasts can be improved by employing a global framework and hierarchical priors which induce country‐specific degrees of shrinkage on the coefficients of the GVAR model. Forecasts from various B‐GVAR specifications tend to outperform forecasts from a naive univariate model, a global model without shrinkage on the parameters and country‐specific vector autoregressions. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage

Many recent papers in macroeconomics have used large vector autoregressions (VARs) involving 100 or more dependent variables. With so many parameters to estimate, Bayesian prior shrinkage is vital to achieve reasonable results. Computational concerns currently limit the range of priors used and render difficult the addition of empirically important features such as stochastic volatility to the large VAR. In this paper, we develop variational Bayesian methods for large VARs that overcome the computational hurdle and allow for Bayesian inference in large VARs with a range of hierarchical shrinkage priors and with time-varying volatilities. We demonstrate the computational feasibility and good forecast performance of our methods in an empirical application involving a large quarterly US macroeconomic data set.     

Forecasting and turning point predictions in a Bayesian panel VAR model

We provide methods for forecasting variables and predicting turning points in panel Bayesian VARs. We specify a flexible model, which accounts for both interdependencies in the cross section and time variations in the parameters. Posterior distributions for the parameters are obtained for hierarchical and for Minnesota-type priors. Formulas for multistep, multiunit point and average forecasts are provided. An application to the problem of forecasting the growth rate of output and of predicting turning points in the G-7 illustrates the approach. A comparison with alternative forecasting methods is also provided.     

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.     

Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice

We consider modeling and forecasting large realized covariance matrices by penalized vector autoregressive models. We consider Lasso‐type estimators to reduce the dimensionality and provide strong theoretical guarantees on the forecast capability of our procedure. We show that we can forecast realized covariance matrices almost as precisely as if we had known the true driving dynamics of these in advance. We next investigate the sources of these driving dynamics as well as the performance of the proposed models for forecasting the realized covariance matrices of the 30 Dow Jones stocks. We find that the dynamics are not stable as the data are aggregated from the daily to lower frequencies. Furthermore, we are able beat our benchmark by a wide margin. Finally, we investigate the economic value of our forecasts in a portfolio selection exercise and find that in certain cases an investor is willing to pay a considerable amount in order get access to our forecasts. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting inflation using time‐varying Bayesian model averaging

This paper presents a Bayesian model averaging regression framework for forecasting US inflation, in which the set of predictors included in the model is automatically selected from a large pool of potential predictors and the set of regressors is allowed to change over time. Using real‐time data on the 1960–2011 period, this model is applied to forecast personal consumption expenditures and gross domestic product deflator inflation. The results of this forecasting exercise show that, although it is not able to beat a simple random‐walk model in terms of point forecasts, it does produce superior density forecasts compared with a range of alternative forecasting models. Moreover, a sensitivity analysis shows that the forecasting results are relatively insensitive to prior choices and the forecasting performance is not affected by the inclusion of a very large set of potential predictors.     

基于组合模型的中长期电力负荷预测研究

本文研究了组合预测的模型,提高了预测的准确度。并对甘肃省2011-2020年全社会用电量做组合预测。     

Forecasting realized volatility: a Bayesian model‐averaging approach

How to measure and model volatility is an important issue in finance. Recent research uses high‐frequency intraday data to construct ex post measures of daily volatility. This paper uses a Bayesian model‐averaging approach to forecast realized volatility. Candidate models include autoregressive and heterogeneous autoregressive specifications based on the logarithm of realized volatility, realized power variation, realized bipower variation, a jump and an asymmetric term. Applied to equity and exchange rate volatility over several forecast horizons, Bayesian model averaging provides very competitive density forecasts and modest improvements in point forecasts compared to benchmark models. We discuss the reasons for this, including the importance of using realized power variation as a predictor. Bayesian model averaging provides further improvements to density forecasts when we move away from linear models and average over specifications that allow for GARCH effects in the innovations to log‐volatility. Copyright © 2009 John Wiley & Sons, Ltd.     

浅谈中大型企业信息化监理的必要性

从中大型企业信息化建设的角度,结合信息化建设项目的特点,研究了信息化监理在中大型中大型信息化建设过程的重要作用及意义。     

基于支持向量机的公路大中修养护费用预测

本文采用小样本最佳通用学习方法,即最小二乘支持向量机进行费用预测。分析了算法的基本原理,构建了普通公路大中修养护费用的特征指标体系与费用预测模型,借助MATLAB编制了求解程序,得到了大中修养护费用预测值,并与一般预测方法得到的结果相比较,说明了算法的优越性。利用该算法能够有效提高预测的精度,因此能够为实际制定养护费用计划提供参考。     

Forecasting short-term defaults of firms in a commercial network via Bayesian spatial and spatio-temporal methods

To protect financial institutions from unexpected credit losses, during the monitoring phase of granted loans it is of primary importance to foresee any evidence of a contagion of liquidity distress across a network of firms. This term indicates a situation of lack of solvency of a firm (e.g., a customer) that propagates to other firms (e.g, its suppliers), which could consequently face challenges in repaying their own granted loans. In this paper, we look for the evidence of contagion of liquidity distress on an Intesa Sanpaolo proprietary dataset by means of Bayesian spatial and spatio-temporal models. Our results indicate that such models can detect cases of distress not yet apparent from covariate information collected on the firms by instead borrowing information from the network, leading to improved forecasting performance on the prediction of short-term default with respect to state-of-the-art methods.     

基于影响因素多维融合与贝叶斯概率更新的电力物资需求预测

通过梳理电力物资需求预测中存在的真实需求和计划需求、物资出库量和真实需求的不一致,以及电力物资需求的内外部影响问题,提出基于影响因素多维融合与贝叶斯概率更新的电力物资需求预测方法。首先分析了电力物资需求的内外部影响因素及其筛选,并按其对需求预测的影响程度进行权重赋值;其次设计了影响因素多维融合与贝叶斯概率更新的电力物资需求预测框架,介绍了贝叶斯概率更新的需求预测流程步骤;最后以温州市10kv配网项目的电力电缆需求预测为例进行算例说明。应用算例表明该方法能有效反映需求因素对电力物资需求变动的影响,符合电力物资需求特性,且具有很强的拓展性。     

关于大中型工业企业库存物资分类的探讨

由于受传统计划经济的影响,在很长一段时期里,我国大中型工业企业的物资分类模式被深深地烙上了"计划"的印记。不能社会主义市场经济发展需要,创建科学的物资分类并使其顺应现代企业的管理,已经显得愈发重要了。     

Forecasting with trees

The prevalence of approaches based on gradient boosted trees among the top contestants in the M5 competition is potentially the most eye-catching result. Tree-based methods out-shone other solutions, in particular deep learning-based solutions. The winners in both tracks of the M5 competition heavily relied on them. This prevalence is even more remarkable given the dominance of other methods in the literature and the M4 competition. This article tries to explain why tree-based methods were so widely used in the M5 competition. We see possibilities for future improvements of tree-based models and then distill some learnings for other approaches, including but not limited to neural networks.     

我国大中城市交通现状分析

在我国,随着汽车保有量的迅速增加。道路的修建速度已经远远不能满足车辆行驶的要求,日益严重的交通问题已成为制约我国大中城市快速发展的瓶颈。以济南为例,分析了我国大中城市交通存在的问题,并重点给出了相应的解决措施．对于我国大中城市交通问题的明确和解决有着积极的意义。     

对加强国有大中型企业员工考核工作的一些思考

员工考核工作对企业发展具有重要意义。文章提出企业应对员工实行全员考核，并针对不同层面和不同岗位的员工，运用不同的考核方法、内容、周期。     

大、中、小企业货币紧缩效应及其差异研究

中、小企业资金链断裂及破产潮是否是货币紧缩期的常态？为此,本文基于国家统计局工业企业数据,首次探讨了中国不同规模企业货币紧缩效应及其相互间的差异。研究发现,货币政策总体上基本有效,但是有偏的,对大、中、小企业的作用效果截然不同。大企业货币紧缩效应微乎其微,而中小企业受到的冲击非常突出,中型企业尤为严重,不仅外部融资水平降低,而且主营业务收入、存货、利润全面出现显著下降。这种效应一方面严重背离货币紧缩的“全局”性要求及其真正目标;另一方面固化国内大、中、小企业“大者恒大、弱者更弱”的失衡生存状态,应引起高度重视。否则中小企业困境将变成货币紧缩期的常态。