中国制造业产业政策实施有效性评价

产业政策是市场经济国家普遍采用的一种公共调节政策,产业政策有效性评价是检验政策实施效果的基本途径。在合理构建制造业产业政策绩效评价指标体系的基础上,运用灰色关联神经网络智能算法对2005-2014年各指标数据进行预测,再利用差值对比法对2005-2014年中国制造业产业政策作用效力进行综合评价。分别从产业结构、组织、科技、布局角度实证分析了制造业在转型升级过程中产业政策实施绩效水平及其演进阶段性特征,可为今后制定合理的制造业产业政策提供科学的理论依据。     

G7与BRIC经济实力变迁的预测

G7是世界上最发达的七个国家而BRIC是世界上最有发展潜力的四个大国,这两类国家对当今世界和未来世界影响举足轻重,对它们进行对比分析具有重要的现实意义和历史意义。传统的对比及预测方法建立在绝对数基础之上,受经济周期、通货膨胀及汇率的影响,预测误差较大。本文采取独特视角以相对数为基础建立统计模型,对比分析并预测G7和BRIC相对实力及相对人民生活水平的变迁,这样不仅消除了经济周期、通货膨胀和汇率因素的影响,而且统计检验结果显示模型具有较高的预测精度。     

基于随机森林的基金评级模型选择

首先汇集基金常用评价指标,建立一个统一的基金评级指标体系;其次利用随机森林建立基金评级模型;最后通过实验验证了该方法的有效性和优越性．本研究将为投资者提供一个投资决策的优良工具．     

Prediction accuracy for reservation-based forecasting methods applied in Revenue Management

With a few notable exceptions, airlines and hospitality forecasting research has been focused so far on point predictions of customers’ bookings. However, Revenue Management decisions are subject to a much greater risk when based exclusively on point predictions. To overcome this drawback, we propose a stochastic framework that allows the construction of prediction intervals for reservation-based (pickup) forecasting methods, which are widely used in the industry. Moreover, we introduce an extension of the multiplicative pickup technique based on Generalized Linear Models. We test the proposed framework with real reservation data from a medium-sized hotel on Lake Maggiore (Italy) and we obtain more efficient prediction intervals relative to classical time series methods. Our approach can be useful to hotel revenue managers that wish to make more informed decisions, planning alternative pricing and room allocation strategies for a range of possible demand scenarios.     

Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction

State-of-the-art methods using attention mechanism in Recurrent Neural Networks have shown exceptional performance targeting sequential predictions and classifications. We explore the attention mechanism in Long–Short-Term Memory (LSTM) network based stock price movement prediction. Our proposed model significantly enhances the LSTM prediction performance in the Hong Kong stock market. The attention LSTM (AttLSTM) model is compared with the LSTM model in Hong Kong stock movement prediction. Further parameter tuning results also demonstrate the effectiveness of the attention mechanism in LSTM-based prediction method.     

Default Patterns in Seven EU Countries: A Random Forest Approach

This study uses the relatively new “random forest” (RF) approach, which is based on decision-tree analysis by combining the results of a large set of decision trees. RFs have so far been little used for default prediction but offer an interesting alternative to well-established default prediction techniques. Based on accounting data from 945,062 observed European firms from seven countries in 2010 and 1,019,312 firms in 2011, we provide evidence on the country-specific default patterns. Because of the strong imbalance of the data sets with regard to the solvency status, standard RF implementations have to be modified to allow the estimation of realistic default propensities. We find that by far most accurate out-of-sample default propensities can be obtained for Italy followed by Portugal and Spain and the least accurate for the UK and Finland. The debt ratio, rate of return on sales, dynamic gearing ratio, and the rate of return on assets are found to be the most important variables for default prediction. The variable importance rankings are rather country specific, pointing to heterogeneity in the default patterns across the countries studied.     

Mining big data using parsimonious factor,machine learning,variable selection and shrinkage methods

A number of recent studies in the economics literature have focused on the usefulness of factor models in the context of prediction using “big data” (see Bai and Ng, 2008; Dufour and Stevanovic, 2010; Forni, Hallin, Lippi, & Reichlin, 2000; Forni et al., 2005; Kim and Swanson, 2014a; Stock and Watson, 2002b, 2006, 2012, and the references cited therein). We add to this literature by analyzing whether “big data” are useful for modelling low frequency macroeconomic variables, such as unemployment, inflation and GDP. In particular, we analyze the predictive benefits associated with the use of principal component analysis (PCA), independent component analysis (ICA), and sparse principal component analysis (SPCA). We also evaluate machine learning, variable selection and shrinkage methods, including bagging, boosting, ridge regression, least angle regression, the elastic net, and the non-negative garotte. Our approach is to carry out a forecasting “horse-race” using prediction models that are constructed based on a variety of model specification approaches, factor estimation methods, and data windowing methods, in the context of predicting 11 macroeconomic variables that are relevant to monetary policy assessment. In many instances, we find that various of our benchmark models, including autoregressive (AR) models, AR models with exogenous variables, and (Bayesian) model averaging, do not dominate specifications based on factor-type dimension reduction combined with various machine learning, variable selection, and shrinkage methods (called “combination” models). We find that forecast combination methods are mean square forecast error (MSFE) “best” for only three variables out of 11 for a forecast horizon of $h=1$, and for four variables when $h=3$ or $12$. In addition, non-PCA type factor estimation methods yield MSFE-best predictions for nine variables out of 11 for $h=1$, although PCA dominates at longer horizons. Interestingly, we also find evidence of the usefulness of combination models for approximately half of our variables when $h>1$. Most importantly, we present strong new evidence of the usefulness of factor-based dimension reduction when utilizing “big data” for macroeconometric forecasting.     

A prediction model of Consumer's willingness to fly in autonomous air taxis

As companies begin to consider new alternatives to urban transportation and urban air mobility, one method under investigation is autonomous air taxis. Literature indicates that people, in general, have positive attitudes towards innovation and new technology. However, complex factors determine their willingness and speed in acceptance. The objective of this study was to examine which factors significantly forecast consumer willingness to fly in autonomous air taxis. A quantitative methodology and non-experimental design were accomplished using 510 participants to develop the regression equation and assess model fit. Six significant predictors of consumer willingness to fly in autonomous air taxis were found: familiarity, value, fun factor, wariness of new technology, fear and happiness. Three additional analyses were assessed using an independent sample of participants, revealing strong model fit. Few previous studies have provided a quantitative assessment of which factors significantly predict consumer willingness to fly in autonomous air taxis. The study contributes to the body of knowledge by identifying six significant factors which account for over 76% of the variance. These findings may help the industry, manufacturers and regulators identify the types of individuals most willing to try this new form of transportation and provide more information on the type of consumer most likely to buy in to this new form of transportation.     

Experience conditioning in commuter modal choice modelling – Does it make a difference?

This paper investigates the impact of decision maker’s experience on model elasticities and predicted market share, using data collected in Sydney on commuter mode choice. Usage frequency is used as a proxy for experience and two separate mode choice models are estimated – one with experience conditioning choice and one without. Key model outputs are compared and we find that differences in the value of travel time savings and model elasticities are very marked. This suggests that ignoring experience that one has with each alternative in their choice set may be a candidate source of error in travel demand forecasts. We develop a method to obtain the level of experience for use in application of choice models to increase their prediction power.     

宁波市海水产品产量预测与对策

论文利用1990～2009年的宁波市海水产品产量的统计数据,运用灰色系统理论建立海洋捕捞和海洋养殖及水产品总量的GM（1,1）模型,对宁波市海水产品产量做出了近期预测,预测结果较为合理。在对宁波市海洋渔业现状及预测结果分析的基础上,论文认为资源与环境的刚性约束将成为今后长时期制约宁波市渔业可持续发展的主要因素,对此提出了相应的政策建议以促进宁波市海洋渔业产量稳步增长。