Model selection in univariate time series forecasting using discriminant analysis

When a large number of time series are to be forecast on a regular basis, as in large scale inventory management or production control, the appropriate choice of a forecast model is important as it has the potential for large cost savings through improved accuracy. A possible solution to this problem is to select one best forecast model for all the series in the dataset. Alternatively one may develop a rule that will select the best model for each series. Fildes (1989) calls the former an aggregate selection rule and the latter an individual selection rule. In this paper we develop an individual selection rule using discriminant analysis and compare its performance to aggregate selection for the quarterly series of the M-Competition data. A number of forecast accuracy measures are used for the evaluation and confidence intervals for them are constructed using bootstrapping. The results indicate that the individual selection rule based on discriminant scores is more accurate, and sometimes significantly so, than any aggregate selection method.     

Bayesian Premium Rating with Latent Structure

We propose a fully Bayesian approach to non-life risk premium rating, based on hierarchical models with latent variables for both claim frequency and claim size. Inference is based on the joint posterior distribution and is performed by Markov Chain Monte Carlo. Rather than plug-in point estimates of all unknown parameters, we take into account all sources of uncertainty simultaneously when the model is used to predict claims and estimate risk premiums. Several models are fitted to both a simulated dataset and a small portfolio regarding theft from cars. We show that interaction among latent variables can improve predictions significantly. We also investigate when interaction is not necessary. We compare our results with those obtained under a standard generalized linear model and show through numerical simulation that geographically located and spatially interacting latent variables can successfully compensate for missing covariates. However, when applied to the real portfolio data, the proposed models are not better than standard models due to the lack of spatial structure in the data.     

Evaluating quantile-bounded and expectile-bounded interval forecasts

In many different contexts, decision-making is improved by the availability of probabilistic predictions. The accuracy of probabilistic forecasting methods can be compared using scoring functions and insight provided by calibration tests. These tests evaluate the consistency of predictions with the observations. Our main agenda in this paper is interval forecasts and their evaluation. Such forecasts are usually bounded by two quantile forecasts. However, a limitation of quantiles is that they convey no information regarding the size of potential exceedances. By contrast, the location of an expectile is dictated by the whole distribution. This prompts us to propose expectile-bounded intervals. We provide interpretation, a consistent scoring function and a calibration test. Before doing this, we reflect on the evaluation of forecasts of quantile-bounded intervals and expectiles, and suggest extensions of previously proposed calibration tests in order to guard against strategic forecasting. We illustrate ideas using day-ahead electricity price forecasting.     

Is the optimal income tax regressive?

A class of solutions that violates the standard results on the sign and shape of optimal tax-rates is shown to be less exceptional than what might be expected, thus casting doubt on the general necessity of qualitative features of income tax-schedules accepted heretofore.     

外资企业选址与企业生产率

本文利用Grossman-Helpman-Szeidl（2006）模型研究了外资企业在发展中国家内部的选址决策,模型表明：效率高的企业偏向选址于固定成本高可变成本低的地方,效率低的企业偏向选址于固定成本低可变成本高的地方。同时各个地区的效率空间会被运输成本所压缩。由于中国东部、中西部与东南亚国家之间劳动力工资以及固定投资成本的结构差异,高效率的企业会选址于印尼、菲律宾、越南三国,效率处于中间的企业会选址于中国中西部以及中国东部,效率最低的企业会选址于马来西亚、泰国两国。随后本文用2004-2013年Bvd亚太企业母公司层面面板数据验证了企业因效率不同而导致的选址差异。最后本文分析了双边自由贸易区对企业选址的效率空间的影响,研究表明双边自由贸易区对中国中西部的影响大于对中国东部的影响。