我国上市公司信用风险预警研究

本文以我国上市公司作为研究样本,构建基于组合预测模型的我国商业银行信用风险管理预警系统。在结合目前国内外各商业银行信用风险预警指标体系的基础上,筛选构建较为合理并适合我国国情的信用风险预警指标体系;进而选择传统数理统计模型Logistic回归模型和人工智能模型RBF神经网络模型建立组合预测预警模型,以求能够组合不同单一模型的优点,解决信用风险预警模型的准确性和稳定性兼顾的问题,仿真结果表明:组合预测模型解决了单一模型应用中精确性和第二类问题处理能力不能同时兼得的问题,达到了预期的效果。     

个人信用评估的加权组合预测模型

目前单一的个人信用评估模型发展的很快,而精确性与稳定性却不能同时具备,且第二类误判率的能力弱,本文立足于用线性和非线性方法在个人信用评估中体现的优势,选择具有代表性的logistic回归和径向基函数神经网络方法,通过加权组合对个人信用进行预测评估,提高了评估模型的精确性与稳健性。     

组合预测模型在区域物流需求预测中的应用

针对单一预测方法用于区域物流需求量预测存在的不足,文章提出了基于预测有效度的组合预测模型,即通过组合多个单一模型的预测结果,发挥各自的优点,提高预测的精确度。以广东省江门市为例,分别采用线性回归模型、灰色GM(1,1)模型和组合预测模型对其物流需求量进行了预测,实证结果表明区域物流需求组合预测模型能够取得更高的预测精度。     

商户小额贷款信用评价模型

针对商户小额贷款信用评价问题,分别利用G1法、CRITIC法和均值方差法进行赋权。然后遵循评价结果差异最小的思路,对基于3种单一赋权方法所得的权重进行组合赋权,建立了商户小额贷款信用风险评价模型。实证结果表明:与基于单一赋权方法所得的评价结果相比,基于组合赋权所得的评价结果具有最高的一致性,其误判率和漏判率也均是最低的。     

采油厂油气产量预测模型研究

油气产量的有效预测有利于采油厂的科学决策。分别构建了采油厂油气产量预测的线性回归模型、灰色预测模型和ARIMA模型,基于三种单一模型,构建了最优加权组合预测模型。DX采油厂油气产量预测实例表明,该模型能够显著提高预测精度,为采油厂产量预测提供了新的方法。     

基于自组织方法的税收收入组合预测模型

自组织理论是基于神经网络和计算机科学的迅速发展而产生和发展起来的。它将黑箱思想、生物神经元方法、归纳法、概率论、数理逻辑等方法有机地组合起来。其主要思想是通过简单的初始输入（局部变量）的交叉组合产生第一代中间候选模型,再从第一代中间候选模型中选出最优的若干项组合而产生第二代中间候选模型,重复这样一个产生、选择和遗传进化过程,使模型复杂度不断增加,直到选出最优复杂度模型为止。本文利用自组织方法进行数据筛选和建立税收预测模型,并在数据筛选基础上建立线性回归预测模型和BP神经网络预测模型,然后结合时间序列的预测模型,利用自组织方法建立组合预测模型。通过预测结果比较得出了组合预测模型比其它单个模型具有更高的预测精度。     

家用汽车需求预测

本文首先用定性预测方法分析了家用汽车需求的主要影响因素，论证了其平稳增长的特性。然后用定量预测方法，在灰色系统模型等单一预测模型的基础上，引入了组合预测模型，通过使组合预测误差平方和最小，得到了各个单一预测方法的权重系数，建立了最优组合预测模型。对预测结果进行对比分析，验证了最优组合预测方法的准确性。最后运用所建立的最优组合预测模型对家用汽车在最近几年的需求量进行了预测。     

企业短期贷款违约预测Bayes模型构建

目前,企业违约预测模型距离实际应用还具有一定差异,表现在:(1)模型所使用的样本基本都是配对模式,与现实情况不符;(2)模型没有考虑到误判成本的非对称性.针对以上问题,本文运用SAS统计软件对某国有商业银行的2003年全部短期贷款企业的财务数据进行分析,摒弃以往配对模式,采用全样本进行分析,筛选出11个财务比率指标作为企业信用风险评价函数的计量参数.应用Bayes判别原理,引入误判成本和先验概率,构建了一个简明的违约判别模型,经检验模型是统计有效的,判别结果也是较好的.     

我国上市公司信用风险评估研究——基于Logit模型的分析

国内对Logit模型在信用风险评估应用方面已有不少实证研究,这些研究从总体预测准确率较高角度认为,该模型基本可以借鉴使用,但大多研究没有进一步区分模型误判的第一类错误与第二类错误.本文结合Logit模型的原理、优缺点,分析相关文献,选取我国上市公司财务数据进行实证分析,证实Logit模型在实际运用时犯第一类错误即高信用风险企业误判为低信用风险企业的错误率达到30%左右,而银行最担心的就是犯第一类错误,故我国商业银行在运用Logit模型对上市公司信用风险进行判断时要十分谨慎.     

中国粮食供需的预测指标体系及模型设计

通过建立粮食需求预测指标体系,从口粮、饲料粮、种子粮、工业用粮及粮食损耗角度实现了粮食需求预测。并采用基于三次指数平滑模型、灰色预测模型、支持向量机预测模型的组合预测模型,成功实现了粮食供给预测。最后,在粮食供需综合分析中,确认了粮食供需缺口的存在性。     

Quantile forecast combination using stochastic dominance

This paper derives optimal forecast combinations based on stochastic dominance efficiency (SDE) analysis with differential forecast weights for different quantiles of forecast error distribution. For the optimal forecast combination, SDE will minimize the cumulative density functions of the levels of loss at different quantiles of the forecast error distribution by combining different time-series model-based forecasts. Using two exchange rate series on weekly data for the Japanese yen/US dollar and US dollar/Great Britain pound, we find that the optimal forecast combinations with SDE weights perform better than different forecast selection and combination methods for the majority of the cases at different quantiles of the error distribution. However, there are also some very few cases where some other forecast selection and combination model performs equally well at some quantiles of the forecast error distribution. Different forecasting period and quadratic loss function are used to obtain optimal forecast combinations, and results are robust to these choices. The out-of-sample performance of the SDE forecast combinations is also better than that of the other forecast selection and combination models we considered.     

Forecast combination for discrete choice models: predicting FOMC monetary policy decisions

This paper provides a methodology for combining forecasts based on several discrete choice models. This is achieved primarily by combining one-step-ahead probability forecasts associated with each model. The paper applies well-established scoring rules for qualitative response models in the context of forecast combination. Log scores, quadratic scores and Epstein scores are used to evaluate the forecasting accuracy of each model and to combine the probability forecasts. In addition to producing point forecasts, the effect of sampling variation is also assessed. This methodology is applied to forecast US Federal Open Market Committee (FOMC) decisions regarding changes in the federal funds target rate. Several of the economic fundamentals influencing the FOMC’s decisions are integrated, or I(1), and are modeled in a similar fashion to Hu and Phillips (J Appl Econom 19(7):851– 867, 2004). The empirical results show that combining forecasted probabilities using scores generally outperforms both equal weight combination and forecasts based on multivariate models.     

An Evaluation of Alternative Methods of Forecasting Australian Inflation

The purpose of this paper is to evaluate the forecast of Australian inflation based on four alternative procedures: a univariate time series model, an interest rate model, an error correction model and a public survey of inflation forecasts. We derive estimates of expected and unexpected inflation from each of the methods and compare the out-of-sample forecasting results. Based on a range of evaluation criteria, the time series model dominates the other models, with the interest rate model, the error correction model and the survey forecasts following in that order.     

A DSGE-VAR model for forecasting key South African macroeconomic variables

The paper develops a Small Open Economy New Keynesian DSGE-VAR (SOENKDSGE-VAR) model of the South African economy, characterised by incomplete pass-through of exchange rate changes, external habit formation, partial indexation of domestic prices and wages to past inflation, and staggered price and wage setting. The model is estimated using Bayesian techniques on data from the period 1980Q1 to 2003Q2, and then used to forecast output, inflation and nominal short-term interest rate for one-to eight-quarters-ahead over an out-of sample horizon of 2003Q3 to 2010Q4. When the forecast performance of the SOENKDSGE-VAR model is compared with an independently estimated DSGE model, the classical VAR and six alternative BVAR models, we find that, barring the BVAR model based on the SSVS prior on both VAR coefficients and the error covariance, the SOENKDSGE-VAR model is found to perform competitively, if not, better than all the other VAR models.     

IMPROVING FORECAST ACCURACY BY COMBINING RECURSIVE AND ROLLING FORECASTS*

This article presents analytical, Monte Carlo, and empirical evidence on combining recursive and rolling forecasts when linear predictive models are subject to structural change. Using a characterization of the bias–variance trade‐off faced when choosing between either the recursive and rolling schemes or a scalar convex combination of the two, we derive optimal observation windows and combining weights designed to minimize mean square forecast error. Monte Carlo experiments and several empirical examples indicate that combination can often provide improvements in forecast accuracy relative to forecasts made using the recursive scheme or the rolling scheme with a fixed window width.     

Comparing the Forecasting Performance of Futures Oil Prices with Genetically Evolved Neural Networks

In this paper, a hybrid system combining neural networks and genetic training is designed to forecast future oil prices. The architectural design is that of the multilayer back propagation network that is fed monthly prices for West Texas Intermediate covering the period 1986–2014. The model’s predictions are compared to those of the one, two, three, and four-month futures prices and are evaluated both on their level of accuracy as well as correctness. While accuracy measures the degree of error, correctness tests the model’s ability to predict the direction of the movement. By processing information more efficiently, and identifying patterns that may be ill-defined as a result of pronounced price volatility, this paper aims to improve the accuracy of oil price forecasts.     

Alternate estimation criteria and measures of forecast accuracy

The purpose of this paper is to investigate and illustrate the effect that alternate estimation criteria have on measured forecast accuracy. In most instances forecast evaluation criteria (error measures) differ from the model estimation criterion, the latter most often being the traditional least squares. The results suggest that forecast accuracy may be improved when criteria other than least squares are used for model estimation purposes.     

Exchange Rate Predictability in Finite Samples

The goal of the present study is to re‐examine the exchange rate predictability with an approach that accounts for the negative effect of the finite‐sample estimation error on forecast accuracy in the in‐sample test. We consider various exchange rate models and find that despite the presence of significant population‐level predictive content in the exchange rate model, the coefficients of the predictive variables could be small enough that, with the available sample, they are estimated so imprecisely that a random walk model can be expected to forecast at least as well as the exchange rate model.     

A Bayesian method of combining judgmental and model-based density forecasts

This paper introduces a formal method of combining expert and model density forecasts when the sample of past forecasts is unavailable. It works directly with the expert forecast density and endogenously delivers weights for forecast combination, relying on probability rules only. The empirical part of the paper illustrates how the framework can be applied in forecasting US inflation by mixing density forecasts from an autoregressive model and the Survey of Professional Forecasters.     

Comments on ‘Forecasting with a real-time data set for macroeconomists’

The effects of data revision as removing measurement error on forecast model building is examined. I show various effects on model building using revised or instead real-time data. These effects include lag length selection and measured persistence. I then argue that in practice that one should include the entire dataset available (real-time and past revised data) in forecast construction.