Solving the incomplete market model with aggregate uncertainty using a perturbation method

We use a perturbation method to solve the incomplete markets model with aggregate uncertainty described in den Haan et al. [Computational suite of models with heterogeneous agents: incomplete markets and model uncertainty. Journal of Economic Dynamics & Control, this issue]. To apply that method, we use a “barrier method” to replace the original problem with occasionally binding inequality constraints by one with only equality constraints. We replace the structure with a continuum of agents by a setting in which a single infinitesimal agent faces prices generated by a representative-agent economy. We also solve a model variant with a large (but finite) number of agents. Our perturbation-based method is much simpler and faster than other methods.     

GMADM-based attributes selection method in developing prediction model

Attribute Selection is an important issue for developing a prediction model, however, how to determine an effective attribute selection algorithm is an important but difficult issue. Attribute selection can effectively delete the irrelevant and redundant attributes to increase the prediction accuracy, and evaluating attribute selection methods usually need to consider several criteria such as accuracy, type I error, and type II error. In this paper, the selected attribute process is modeled as a group multiple attributes decision making (GMADM) problem. In evaluating different GMACD methods, the most results usually are consistently, But there are some situations where the evaluated outcomes have different results. The GMADM method is useful tool for evaluating attribute selection algorithms, and the TOPSIS is capable of identifying a compromised solution when different GMADM method result in conflicting rankings. Therefore, this paper proposes an objective (persuasive) GMADM-based attributes selection method to solve this disagreement and help decision makers pick the most suitable method. After verification, the proposed model is more persuasive to evaluate the attributes selection methods for developing prediction model.     

Forecasting With the Standardized Self‐Perturbed Kalman Filter

We propose and study the finite‐sample properties of a modified version of the self‐perturbed Kalman filter of Park and Jun (Electronics Letters 1992; 28 : 558–559) for the online estimation of models subject to parameter instability. The perturbation term in the updating equation of the state covariance matrix is weighted by the estimate of the measurement error variance. This avoids the calibration of a design parameter as the perturbation term is scaled by the amount of uncertainty in the data. It is shown by Monte Carlo simulations that this perturbation method is associated with a good tracking of the dynamics of the parameters compared to other online algorithms and to classical and Bayesian methods. The standardized self‐perturbed Kalman filter is adopted to forecast the equity premium on the S&P 500 index under several model specifications, and determines the extent to which realized variance can be used to predict excess returns. Copyright © 2016 John Wiley & Sons, Ltd.     

Allocation model for theme park advertising budget

This paper treats advertising budgeting, which is a challenging issue for academicians and practitioners. A problem-solving tool, namely Project Advancement (PA), is employed to identify the characteristics of the budget allocation issue the advertising manager faces with. In line with the characteristics (i.e., fundamental objective (FO), the length of value-based time limit of advertising campaigns, and advancement strategy of advertising campaigns), an efficient allocation model for achieving the FO is then constructed. A case of theme park advertising budgeting throughout this paper provides insights into this approach and yields illustrations to the allocation model. Analytic Hierarchy Process (AHP) method is employed for estimate of objective coefficients of the allocation model. Managerial implications are identified with regard to using the new approach.     

Comparison of solutions to the multi-country Real Business Cycle model

We compare the performance of perturbation, projection, and stochastic simulation algorithms for solving the multi-country RBC model described in Den Haan et al. (this issue). The main challenge of solving this model comes from its large number of continuous-valued state variables, ranging between four and 20 in the specifications we consider. The algorithms differ substantially in terms of speed and accuracy, and a clear trade-off exists between the two. Perturbation methods are very fast but invoke large approximation errors except at points close to the steady state; the projection methods considered are accurate on a large area of the state space but are very slow for specifications with many state variables; stochastic simulation methods have lower accuracy than projection methods, but their computational cost increases only moderately with the state-space dimension. Simulated series generated by different methods can differ noticeably, but only small differences are found in unconditional moments of simulated variables. On the basis of our comparison, we identify the factors that account for differences in accuracy and speed across methods, and we suggest directions for further improvement of some approaches.     

一种多品种最小费用流求解方法的研究

尽管有许多实际问题都可以建立单品种网络流规划模型．但是多品种问题则更为普遍，其中最典型的是运输中的多物资问题。本文通过引入修正费用的办法来解决多品种竞争问题：首先建立起多品种流最小费用问题的数学模型，然后根据弧在增广圈中出现的次数来确定流量调整量，得到了一种求解多品种最小费用流问题的方法。     

Diagnostic analysis for a vector autoregressive model under Student′s t‐distributions

In this paper, we use the local influence method to study a vector autoregressive model under Student^′s t‐distributions. We present the maximum likelihood estimators and the information matrix. We establish the normal curvature diagnostics for the vector autoregressive model under three usual perturbation schemes for identifying possible influential observations. The effectiveness of the proposed diagnostics is examined by a simulation study, followed by our data analysis using the model to fit the weekly log returns of Chevron stock and the Standard & Poor's 500 Index as an application.     

Local influence analysis for Poisson autoregression with an application to stock transaction data

In statistical diagnostics and sensitivity analysis, the local influence method plays an important role and has certain advantages over other methods in several situations. In this paper, we use this method to study time series of count data when employing a Poisson autoregressive model. We consider case‐weights, scale, data, and additive perturbation schemes to obtain their corresponding vectors and matrices of derivatives for the measures of slope and normal curvatures. Based on the curvature diagnostics, we take a stepwise local influence approach to deal with data with possible masking effects. Finally, our established results are illustrated to be effective by analyzing a stock transactions data set.     

基于改进蚁群算法的应急物流中心选址问题研究

针对灾情等级不确定情况下应急物流的特点,本文利用不确定理论的相关方法,就应急物流中转站选址问题进行了相关分析和研究,构建了适应于应急物流特性的中心选址模型。最后进行仿真实例,利用改进蚁群算法求解模型,并得出相关结论。     

An analytical approximation formula for European option pricing under a new stochastic volatility model with regime-switching

In this paper, an analytical approximation formula for pricing European options is obtained under a newly proposed hybrid model with the volatility of volatility in the Heston model following a Markov chain, the adoption of which is motivated by the empirical evidence of the existence of regime-switching in real markets. We first derive the coupled PDE (partial differential equation) system that governs the European option price, which is solved with the perturbation method. It should be noted that the newly derived formula is fast and easy to implement with only normal distribution function involved, and numerical experiments confirm that our formula could provide quite accurate option prices, especially for relatively short-tenor ones. Finally, empirical studies are carried out to show the superiority of our model based on S&P 500 returns and options with the time to expiry less than one month.     

基于重心法的应急物流中心选址初探

文章针对应急物流系统对时间的特殊要求,利用重心法建立了应急物流中心的选址模型。结果表明:重心法是一种简便、工作量较小的方法,能较好地简化应急物流中心的选址问题,具有较强的实用性。     

fETSmcs: Feature-based ETS model component selection

The well-developed ETS (ExponenTial Smoothing, or Error, Trend, Seasonality) method incorporates a family of exponential smoothing models in state space representation and is widely used for automatic forecasting. The existing ETS method uses information criteria for model selection by choosing an optimal model with the smallest information criterion among all models fitted to a given time series. The ETS method under such a model selection scheme suffers from computational complexity when applied to large-scale time series data. To tackle this issue, we propose an efficient approach to ETS model selection by training classifiers on simulated data to predict appropriate model component forms for a given time series. We provide a simulation study to show the model selection ability of the proposed approach on simulated data. We evaluate our approach on the widely used M4 forecasting competition dataset in terms of both point forecasts and prediction intervals. To demonstrate the practical value of our method, we showcase the performance improvements from our approach on a monthly hospital dataset.     

Diagnostic tools in beta regression with varying dispersion

We consider the issue of performing residual and local influence analyses in beta regression models with varying dispersion, which are useful for modelling random variables that assume values in the standard unit interval. In such models, both the mean and the dispersion depend upon independent variables. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. An application using real data is presented and discussed.     

复杂被、主动隔振系统动力学模型的创建

本文的主要内容是对复杂系统的动力学建模,用一个统一的模型概括了隔振理论中的多种隔振问题,以及主动与被动控制方式问题,具有理论研究上的普遍意义;并引入频率平均功率流作为效果评价指标。     

A comparison of two model averaging techniques with an application to growth empirics

Parameter estimation under model uncertainty is a difficult and fundamental issue in econometrics. This paper compares the performance of various model averaging techniques. In particular, it contrasts Bayesian model averaging (BMA) — currently one of the standard methods used in growth empirics — with a new method called weighted-average least squares (WALS). The new method has two major advantages over BMA: its computational burden is trivial and it is based on a transparent definition of prior ignorance. The theory is applied to and sheds new light on growth empirics where a high degree of model uncertainty is typically present.     

Introduction to research on Hirschman's Exit,Voice, and Loyalty model

This article introduces this special issue of theEmployee Responsibilities and Rights Journal on recent work exploring Albert O. Hirschman's Exit, Voice, and Loyalty model of dissatisfaction. This special issue provides a forum for researchers and theorists with various perspectives on the model to present their ideas in one place. There are six original articles in this issue, and one discussion piece. While these articles do not resolve the controversies surrounding the Exit, Voice, and Loyalty model, they do provide a clear picture of the current status of research and theory on dissatisfaction in organizations from this perspective.     

Raising the bar (11)

This editorial summarizes the papers published in issue 14(2) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper deals with past and current challenges for regional science research. The second paper investigates whether people living in deprived neighbourhoods have less chance of succeeding in a job application. The third paper finds evidence that real estate firms can avoid price competition when market shares of their allies increase in the vicinity. The fourth paper is methodological: it considers a spatial autoregressive (SAR) model with heterogeneous coefficients and extensively analyzes the impact of this extension on the direct and indirect effects estimates. The fifth paper proposes an innovative method to estimate the elements of the spatial weight matrix in a spatial econometric model. The final paper is econometric–theoretical: it proposes a new generalized method of moments (GMM) estimator of the coefficients of a SAR model if the error terms are heteroskedastic of an unknown form.     

自相关与阻尼比扰动混沌系统结合的检测方法

文章阐述了自相关检测方法和阻尼比扰动Duffing振子检测系统的检测原理,在此基础上提出了自相关与阻尼比扰动Duffing振子检测系统相结合的一种新检测方法。文中介绍了新方法的检测原理,建立了检测模型,并在白、色噪声背景下对方波信号的进行检测,仿真结果表明新检测方法的输入信噪比检测门限明显优于自相关检测方法以及单独采用阻尼比扰动的混沌检测方法,白噪声、两种色噪声背景下信噪比分别为-35.97dB、-34.75dB和-32.1dB。     

公路项目运营可持续性物元/层次分析评价法

文章针对公路项目社会经济可持续发展进行分析,将其影响因素归纳为:社会发展、经济发展、政治问题和公路部门素质等4个方面,在此基础上构建了公路项目社会经济可持续发展评价指标体系,再运用物元分析法和层次分析法,建立了评价模型。通过实证项目中的模糊运用,对公路项目社会经济可持续发展进行综合评价。     

Forecasting a long memory process subject to structural breaks

We develop an easy-to-implement method for forecasting a stationary autoregressive fractionally integrated moving average (ARFIMA) process subject to structural breaks with unknown break dates. We show that an ARFIMA process subject to a mean shift and a change in the long memory parameter can be well approximated by an autoregressive (AR) model and suggest using an information criterion (AIC or Mallows’ _Cp$C_p$) to choose the order of the approximate AR model. Our method avoids the issue of estimation inaccuracy of the long memory parameter and the issue of spurious breaks in finite sample. Insights from our theoretical analysis are confirmed by Monte Carlo experiments, through which we also find that our method provides a substantial improvement over existing prediction methods. An empirical application to the realized volatility of three exchange rates illustrates the usefulness of our forecasting procedure. The empirical success of the HAR-RV model can be explained, from an econometric perspective, by our theoretical and simulation results.