M-competition — A closer look at NAIVE2 and median APE : A note

Two observations regarding the M-Competition are presented. First, the seasonal indices that were used in the NAIVE2 method were not calculated using the exact procedures that were defined in the M-Competition paper. Second the median absolute percentage error comparative measure was not computed as one might expect it to have been and was not documented as such. The resolution of these matters might enhance the usefulness of the M-Competition study.     

Emergent clustering methods for empirical OM research

To date, the vast majority of cluster analysis applications in OM research have relied on traditional hierarchical (e.g., Ward's algorithm) and nonhierarchical (e.g., K-means algorithms) methods. Although these venerable methods should continue to be employed effectively in the OM literature, we also believe there is a significant opportunity to expand the scope of clustering methods to emergent techniques. We provide an overview of some alternative clustering procedures (including advantages and disadvantages), identify software programs for implementing them, and discuss the circumstances where they might be employed gainfully in OM research. The implementation of emergent clustering methods in the OM literature should enable researchers to offer implications for practice that might not have been uncovered with traditional methods.     

Forecasting sales response for multiple time horizons and temporally aggregated data: A comparison of constant and stochastic coefficient models

Past research on time-varying sales-response models emphasized the application of different estimation techniques in examining variation in advertising effectiveness over time. This study focuses on comparing sales forecasts using constant and stochastic coefficients sales-response models. Selected constant and stochastic coefficient models are applied to six sets of bimonthly and one set of annual advertising and sales data to assess forecasting accuracy for time horizons of various lengths. Results show improved forecasting accuracy for a first-order autoregressive stochastic coefficient model, particularly in short-run forecasting applications.     

分位数误差校正模型及应用

误差校正模型具有较好的预测能力,在时间序列分析中占据重要地位。将误差校正模型从均值框架推广到分位数框架,提出了分位数误差校正模型的概念,并给出一整套建模技术：模型表示、参数估计、模型定阶、诊断检验、密度预测等。通过数值模拟,将其与经典的均值误差校正模型、分位数自回归模型进行比较,发现分位数误差校正模型极大地提高了预测的准度与精度。此外,选取中国货币供应与物价水平之间关系作为研究对象,实证检验了分位数误差校正模型的条件密度预测能力。     

Macroeconomic forecasting using penalized regression methods

We study the suitability of applying lasso-type penalized regression techniques to macroe-conomic forecasting with high-dimensional datasets. We consider the performances of lasso-type methods when the true DGP is a factor model, contradicting the sparsity assumptionthat underlies penalized regression methods. We also investigate how the methods handle unit roots and cointegration in the data. In an extensive simulation study we find that penalized regression methods are more robust to mis-specification than factor models, even if the underlying DGP possesses a factor structure. Furthermore, the penalized regression methods can be demonstrated to deliver forecast improvements over traditional approaches when applied to non-stationary data that contain cointegrated variables, despite a deterioration in their selective capabilities. Finally, we also consider an empirical applicationto a large macroeconomic U.S. dataset and demonstrate the competitive performance of penalized regression methods.     

Evaluating alternative models of trend inflation

With the concept of trend inflation now being widely understood to be important to the accuracy of longer-term inflation forecasts, this paper assesses alternative models of trend inflation. Reflecting the models which are common in reduced-form inflation modeling and forecasting, we specify a range of models of inflation that incorporate different trend specifications. We compare the models on the basis of their accuracies in out-of-sample forecasting, both point and density. Our results show that it is difficult to say that any one model of trend inflation is the best. Several different trend specifications seem to be about equally accurate, and the relative accuracy is somewhat prone to instabilities over time.     

我国上市公司股利政策实证研究

影响股利政策的因素有法律方面的,也有公司自身方面的。其中公司自身的资本结构是最主要也是最关键的因素。资产负债率、资产的流动性、负债的内部结构以及公司规模、股权集中度会直接影响到股利分配。