MLP ensembles improve long term prediction accuracy over single networks

This work describes an award winning approach for solving the NN3 Forecasting Competition problem, focusing on the sound experimental validation of its main innovative feature. The NN3 forecasting task consisted of predicting 18 future values of 111 short monthly time series. The main feature of the approach was the use of the median for combining the forecasts of an ensemble of 15 MLPs to predict each time series. Experimental comparison to a single MLP shows that the ensemble increases the performance accuracy for multiple-step ahead forecasting. This system performed well on the withheld data, having finished as the second best solution of the competition with an SMAPE of 16.17%.     

政府规模、政府支出增长与经济增长关系的非线性研究

本文运用面板平滑转换回归模型(PSTR),在非线性的框架下对政府规模与经济增长关系的渐进演变展开深入研究,并对可能引发两者关系结构性转变的警戒政府规模进行有效估算。研究结果表明,政府规模与经济增长之间存在着非线性关系,即随着政府规模的逐步增大,由于税负增加等因素的影响,使得政府支出增加所产生的负效应影响逐步凸显,而政府规模进一步扩大并超过警戒水平时,过度拥挤的政府支出对经济增长将由促进作用转变为阻碍作用。研究还发现,尽管我国政府支出增长与经济增长的关系参数值有所下降,但由于基础设施落后,公共物品与公共服务供给仍然相对不足,政府支出的增加仍有助于促进经济的进一步发展。     

用模拟退火算法寻找Heston期权定价模型参数

随机波动率模型由于放松了Black-Sholes模型的假定而更符合市场情况,因此成为研究金融衍生品定价的热点。Heston随机波动率不同于其他随机波动率模型之处在于其存在闭形式解。Heston期权定价模型在应用中需要确定五个待估参数,此问题通常比较困难。本文采用模拟退火算法并利用最小化残差平方和来估算,该算法以一定概率跳出局部极小值,从而以概率1收敛到全局极小值,最终得到Heston模型的待估参数。在实证研究中,本文利用香港恒生股票指数期权在2010年10月15日交易的数据,得到待估参数,并用该参数对2010年10月18日期权进行了模拟定价。     

基于半参数LM-ARMAX模型的股价波动成因分析

对股票价格波动的建模分析,一直是经济与金融研究的核心领域,是什么导致了股票价格的波动,也一直是投资主体共同关注的话题。有鉴于此,本文选择沪市1991-2010年所有上市公司的数据,建立了LM-ARMAX模型来实证股票价格波动的决定因素,最后根据模型半参数估计的结果,进行了基于半参数估计的非线性检验和基于Wild Bootstrap的Smirnov检验,结果表明:市账率和成交量是股票价格波动的主要因素,而净资产收益率对股票价格波动的影响不显著。     

冷却剂参数对铣槽喷管低周疲劳寿命的影响

采用有限体积流固耦合计算方法、非线性有限元热结构耦合分析方法和局部应变法研究大面积比铣槽喷管三维再生冷却槽道在循环工作条件下的热结构变形与低周疲劳寿命,并对比分析了冷却剂质量流量与入口温度对铣槽喷管疲劳使用寿命的影响。计算结果表明,铣槽喷管热结构响应呈现复杂的三维效应,应变较大位置主要分布在与肋连接的内衬区域,喷管中部的残余应变量最大;冷却槽道低周疲劳寿命分布和热结构响应基本一致,最小寿命位于喷管中部与肋相连的内衬区域燃气侧;随冷却剂质量流量增加,铣槽喷管低周疲劳寿命不断提高;随冷却剂入口温度增加喷管尾部低周疲劳寿命值不断降低,而喷管中前部的低周疲劳寿命值却不断提高,当冷却剂入口温度为280K左右时,本文的铣槽喷管总体使用寿命达到最大。      

Implementation of competitive nonlinear pricing: tariffs with inclusive consumption

The paper studies how the optimal nonlinear quantity-payment allocation can be truthfully implemented by optional tariffs in a differentiated goods duopoly. Consumers choose from a menu of tariffs and are subsequently billed according to this. We find that implementation by simple two part tariffs may not be a feasible strategy in a duopoly because the optimal nonlinear tariff exhibits a convexity for lower quantities. We show that the optimal outcome can be implemented if the firms can use two part tariffs with inclusive consumption. The fixed fee includes a free consumption allowance, whereas subsequent consumption is charged according to a steep unit price. That way the firm is able to secure voluntary participation without violating the incentive constraint. The paper shows some examples from the telecommunications industry where firms offer two part tariffs with free call minutes to low demand segments.     

A Nonlinear Approach for Modeling and Forecasting US Business Cycles

The purpose of this paper is to provide a complete evaluation of four regime-switching models by checking their performance in detecting US business cycle turning points, in replicating US business cycle features and in forecasting US GDP growth rate. Both individual and combined forecasts are considered. Results indicate that while the Markov-switching model succeeded in replicating all the NBER peak and trough dates without an extra-cycle detection, it seems to be outperformed by the Bounce-back model in term of the delay time to a correct alarm. Concerning business cycle features characterization, none of the competing models dominates over all the features. The performance of the Markov-switching and bounce back models in detecting turning points was not translated into an improved business cycle feature characterization since they are outperformed by the Floor and Ceiling model. The forecast performance of the considered models varies across regimes and across forecast horizons. That is, the model performing best in an expansion period is not necessarily the same in a recession period and similarly for the forecast horizons. Finally, combining such individual forecasts generally leads to increased forecast accuracy especially for h=1.     

Identification and estimation of nonlinear dynamic panel data models with unobserved covariates

This paper considers nonparametric identification of nonlinear dynamic models for panel data with unobserved covariates. Including such unobserved covariates may control for both the individual-specific unobserved heterogeneity and the endogeneity of the explanatory variables. Without specifying the distribution of the initial condition with the unobserved variables, we show that the models are nonparametrically identified from two periods of the dependent variable _Yit$Y_{it}$ and three periods of the covariate _Xit$X_{it}$. The main identifying assumptions include high-level injectivity restrictions and require that the evolution of the observed covariates depends on the unobserved covariates but not on the lagged dependent variable. We also propose a sieve maximum likelihood estimator (MLE) and focus on two classes of nonlinear dynamic panel data models, i.e., dynamic discrete choice models and dynamic censored models. We present the asymptotic properties of the sieve MLE and investigate the finite sample properties of these sieve-based estimators through a Monte Carlo study. An intertemporal female labor force participation model is estimated as an empirical illustration using a sample from the Panel Study of Income Dynamics (PSID).     

Semiparametric quantile regression estimation in dynamic models with partially varying coefficients

We study quantile regression estimation for dynamic models with partially varying coefficients so that the values of some coefficients may be functions of informative covariates. Estimation of both parametric and nonparametric functional coefficients are proposed. In particular, we propose a three stage semiparametric procedure. Both consistency and asymptotic normality of the proposed estimators are derived. We demonstrate that the parametric estimators are root-n$n$ consistent and the estimation of the functional coefficients is oracle. In addition, efficiency of parameter estimation is discussed and a simple efficient estimator is proposed. A simple and easily implemented test for the hypothesis of a varying-coefficient is proposed. A Monte Carlo experiment is conducted to evaluate the performance of the proposed estimators.     

基于广义交叉谱密度方法的线性和非线性格兰杰因果关系检验

鉴于现行的均值格兰杰因果关系检验或者无法检验非线性的格兰杰因果关系,或者存在“维数灾难”问题,我们利用Chung 和 Hong (2007) 的广义交叉谱方法提出了一个能统一检验线性和非线性均值格兰杰因果关系的检验统计量。我们的广义交叉谱检验统计量渐近服从一个标准正态分布,它不但能考虑所有滞后阶的信息,而且避免了“维数灾难”问题。蒙特卡罗试验结果表明广义交叉谱检验具有良好的有限样本表现。