项目来源

香港研究资助局基金(RGC)

项目主持人

Dr Preve, Daniel

项目受资助机构

City University of Hong Kong

立项年度

2013

立项时间

未公开

项目编号

198813

研究期限

未知 / 未知

项目级别

省级

受资助金额

136500.00港币

学科

Business Studies

学科代码

未公开

基金类别

Early Career Scheme

关键词

未公开

参与者

未公开

参与机构

未公开

项目标书摘要：给非专业人士理解的研究摘要在不同的实体(如公司，国家或个人)在单一时间周期内收集的数据被称为横截面数据。通过在一段时间当中实体间的不同及差异，横截面数据可以用来研究经济变数之间的关系。透过假设横截面数据是独立及分布平均的随机变数而建立的模型是相当有用的。如果实体进行采样，采用简单随机抽样，这种假设是能满足的。线性回归提供一个有用的模型描述横截面数据。跟横截面数据不同，时间序列数据是收集在一段时间内一个单一实体(如失业措施或汇率)的数据。时间序列数据可以用来研究经济变数随着时间的演变和推移，及预测这些变数未来的改变。透过假设时间序列为一个随机过程而建立的模型是相当有用的。更具体地说，如果要允许未来观测值的不可预测性，我们需要假设各观测值是一个随机变数。在众多时间序列模型中，自回归模型对於了解时间序列数据的动态是十分有用的。在实践中，一个经济模型的参数是未知的而且需要使用实际的数据来估计。通常，这里所描述的线性回归模型和自回归模型估计是利用最小二乘法(OLS)来估计。在回归分析中，如果线性回归模型的误差和回归变数之间是相关的，这会使由 OLS 估计的参数变得不一致(也就是说即便在大样本的情况下，估计出来的参数跟实际的参数仍会有不同)。同样地，在时间序列分析中，如果自回归模型的误差之间是相关的，自回归模型所得的参数也会跟实际参数不一致。在一定的限制中，线性规划方法是比 OLS 的更好选择。最近的研究表明，在上述情况下，这些线性规划方法的估计量具有超级一致性，因此比 OLS 更好。这个项目有两部分，其中涉及线性规划方法及其在经济学和金融学中的应用。第一部份涉及在有限制的线性回归模型中的估计和推断。第二部分包括在最近推出的有限制下的非线性时间序列模型(特别在经济学上)的估计和推断。;

Application Abstract: Data on different entities(e.g.companies,countries or individuals)collected at a single time period are called cross-sectional data.Cross-sectional data can be used to study relationships among economic variables by studying differences across entities at a single period in time.It is useful to model cross-sectional observations as realizations of independent,identically distributed random variables.This assumption is satisfied if the entities are sampled by simple random sampling.A useful class of models for describing cross-sectional data is provided by linear regressions.
        In contrast to cross-sectional data,time series data are data for a single entity collected over a period of time(e.g.unemployment measures or exchange rates).Time series data can be used to study the evolution of economic variables over time and to forecast future values of those variables.It is useful to model an observed time series as a realization of a random process.More specifically,to allow for the unpredictable nature of future observations it is assumed that each observation is a realized value of a random variable.A useful class of models for describing the dynamics of a time series is provided by autoregressive(AR)processes.
        In practice,the parameters of an economic model are unknown and need to be estimated using actual data.The linear regression and AR models described herein are usually estimated by the method of ordinary least squares(OLS).In regression analysis,it is well known that the OLS estimator is inconsistent(may not be close to the true parameter values with high probability even when the data set is very large)for the regression parameters when the error term is correlated(linearly associated)with the explanatory variables in the regression.Similarly,in time series analysis,it is known that the OLS estimator is inconsistent for the AR parameters in an AR process with serially correlated errors.Under certain restrictions,a promising alternative to OLS is linear programming(LP)based estimators.Recent research show that these estimators can be superconsistent in the above mentioned situations and,hence,preferable to OLS.This project has two parts which relate to LP-based estimators and their applications in economics and finance.The first part concerns estimation and inference in a class of restricted linear regression models.The second part involves estimation and inference in a,for economics,recently introduced class of restricted nonlinear time series models.