经济计量研究中纵向数据模型的EM算法

在经济学当中，经常见到结合时间序列和横截面两者的数据(即纵向数据)。纵向数据模型常涉及到随机效应，所以对它的统计分析比一般的固定效应模型要复杂得多，常用的方法或者计算烦琐，需分两少估计，或者在多数情况下不收敛，这极大地限制了对纵向数据的开发利用。本文介绍一类纵向数据模型参数估计的EM算法，这种算法能一次性的估计模型中的所有参数，而且在多数情况下是收敛的。     

银行信贷支持县域经济发展的效率研究 ——以石家庄市为例

为了研究银行信贷对县域经济发展过程中的支持效应,本文根据《石家庄市统计年鉴》选择石家庄市17个县(含市)2010—2019年10年的面板数据,利用OLS模型进行实证分析,同时针对实证结果,通过固定效应模型再进行回归检验的方法,分析银行信贷拉动县域经济具体效应的研究。实证结果显示,石家庄市部分县市Beta系数位于0.5～1,一部分县市beta系数大于1,并且通过固定效应模型检验结果与实证结果基本一致,说明银行信贷确实对县域经济的发展有一定的支持效应,与此同时,Beta系数越大,该地区的人均生产总值越大,也就是经济发展速度越快。通过本文的研究,可以弥补在该方面的论述中实证研究不多的一些不足,并且有助于提高对县域经济的深刻理解与认识,在探索银行信贷投放量与县域经济增长之间的促进效应中具有很大意义。     

BP神经网络在防城港货物吞吐量预测中的应用

由于港口吞吐量的影响因素相当多,这些影响因素中大部分又是不可量化指标．造成数据收集和确定的困难。在介绍BP算法的基础下,使用基于时间序列的BP神经网络模型对防城港货物吞吐量进行预测,该模型不仅解决影响因素多、数据难收集的问题,而且是货物吞吐量预测方法中精度很高的一种有效方法。     

时变秩和时变系数VECM模型与“费雪效应”机制检验

研究目标:检验我国经济是否存在“费雪效应”。研究方法:将马尔科夫区制转换结构应用于对协整秩的建模,同时考虑协整参数的时变,构建时变秩和时变系数VECM模型,使用贝叶斯框架估计模型参数。研究发现：1992年1月～2016年3月间我国名义利率与通货膨胀率之间存在时变系数VECM模型与I(1)数据时变系数VAR模型两种区制的转换,我国名义利率和通货膨胀率之间主要表现为时变协整关系。估计的归一化协整向量表明,整个样本期间我国存在“费雪效应”占优;经济“新常态”时期,不存在“费雪效应”处于主导地位;2015年7月以来我国存在时变弱的“费雪效应”。研究创新：将马尔科夫区制转换结构应用于对协整秩的建模,构建时变秩和时变系数VECM模型。研究价值：有助于重新认识“费雪效应之谜”。     

联合分析的随机系数模型估计

联合分析可以帮助我们研究为什么消费者购买某一产品而不是其他产品这样一个核心的营销问题。完整轮廓法是一种比较常用的联合分析方法，在估计这个模型的系数时人们传统上采用最小二乘法回归模型。这种传统方法的主要问题在于要么模型系数不稳定，要么忽略了个人层面的差异性。随机系数模型不仅克服了传统方法的不足，而且有其独特的优点。该方法可以同时估计总体和个人层面的模型系数，可以检验个人层面系数的差异性，而且个人层面的系数更可靠。本文应用该方法对笔记本电脑案例进行了实证分析。     

大数据背景下面板数据政策评估的估计和推断

研究目标:介绍大数据背景下基于面板数据模型的政策评估方法的最新进展与相关应用。研究方法:回顾双重差分法、合成控制法、面板数据方法、因子估计方法和机器学习方法这几类方法在估计面板数据因果效应方面的最新进展后，介绍现有研究中基于上述估计量的推断方法，最后报告已有文献对于不同方法的对比，并提供实证应用建议。研究发现:当实证应用问题中随时间变化的因子个数超过一个时，特别要关注基于双向固定效应的双重差分法的适用性。运用双向固定效应设定模型不恰当时，可考虑使用基于交互固定效应模型的因子模型类估计和推断方法。研究创新:从大数据时代的政策评估需求出发，梳理基于面板数据的因果效应估计和推断方法并给出应用建议。研究价值:为实证研究者提供了选择政策评估方法的参考指南。     

部分线性单指数动态面板模型的惩罚二次推断函数估计

本文将个体效应与模型的线性部分和非线性部分同时进行估计,构建了惩罚二次推断函数估计法,估计了个体内具有自相关结构的随机效应部分线性单指数动态面板模型。该方法的优点是能够克服由个体效应引起的内生性问题,因此无论滞后因变量处于线性部分还是处于非参数部分均可得到一致有效的估计。进一步,本文证明了该估计方法的一致性和渐近正态性,同时还用Monte Carlo模拟实验评估了该估计方法在有限样本下的表现。     

随机效应Logistic模型的参数估计

在经济计量学中，对面板(panel)数据的研究是一个热门的话题。目前，讨论得较多的是如何运用线性随机效应模型来对它建模。可是，当因变量是二元的数据时，用线性随机效应模型进行建模显然是错误的。这时，比较常用的是随机效应Logistic模型。本文讨论了如何运用EM算法对随机效应Logistic模型进行参数估计。     

面板数据的贝叶斯Lasso分位回归方法

文章讨论了含有随机效应的面板数据模型,通过引入条件Laplace先验,文章构造了一种新的贝叶斯Lasso分位回归法。与一般贝叶斯分位回归法不同的是,该方法能够更大程度的将模型中非重要解释变量系数压缩至0,从而在估计系数的同时也起到了变量选择的作用。利用积分恒等式,文章构造了一种易于实施的参数估计的切片Gibbs抽样算法。模拟结果显示,在模型含有较多变量时,新方法排除“噪声”变量的能力明显高于现有文献中其他方法。文章最后对我国各地区多个宏观经济指标的面板数据进行了建模分析,演示了新方法估计参数与挑选变量的能力。     

SVM分类器在继发性干燥综合征诊断中的应用

本文为解决SLE患者并发继发性干燥综合征不易诊断及确诊主观性较强等问题,提出了一种可供计算机学习的支持向量机智能算法预测诊断模型。首先对材料中141名患者的26种相关诊断指标进行数据预处理,使之成为能够适合支持向量机计算的量化数据;其次运用交叉验证法、网格搜索法、改进的粒子群优化算法分别对支持向量机模型中的惩罚系数C与核参数g进行优化选择,并利用MATLAB软件分别画出以上3种优化方式得出的支持向量机参数模型;最终对比选出对SLE患者并发继发性干燥综合征疾病诊断预测度最高的预测模型。结果表明,基于改进的粒子群算法优化的支持向量机分类模型参数的自优化,对该疾病预测诊断精度最高。     

带多个变点的逐段连续线性分位数回归模型及应用

研究目标：建立具有多个变点的逐段连续线性分位数回归模型（Continuous Piecewise Linear Quantile Regression with Multiple Change Points,CPLQR）。研究方法：先通过LASSO和广义贝叶斯信息准则确定变点个数,再通过线性化技巧来估计变点的位置与回归系数。研究发现：新方法能够同时确定变点个数、估计变点位置和回归系数,而且具有较强的稳健性;应用该方法于年龄和身体质量指数之间关系,进一步证实了模型的实用性。研究创新：新方法能够处理多个变点的问题,通过LASSO和广义贝叶斯信息准则确定变点数目,避免了主观判断的弊端;借助线性化技巧,解决了目标函数在变点处不可导问题。研究价值：本文结果将为分析经济、金融、医药和生物等学科中存在结构变化的数据提供强有力的研究工具。     

Quantile regression under random censoring

Censored regression models have received a great deal of attention in both the theoretical and applied econometric literature. Most of the existing estimation procedures for either cross-sectional or panel data models are designed only for models with fixed censoring. In this paper, a new procedure for adapting these estimators designed for fixed censoring to models with random censoring is proposed. This procedure is then applied to the CLAD and quantile estimators of Powell (J. Econom. 25 (1984) 303, 32 (1986a) 143) to obtain an estimator of the coefficients under a mild conditional quantile restriction on the error term that is applicable to samples exhibiting fixed or random censoring. The resulting estimator is shown to have desirable asymptotic properties, and performs well in a small-scale simulation study.     

Quantile cointegrating regression

Quantile regression has important applications in risk management, portfolio optimization, and asset pricing. The current paper studies estimation, inference and financial applications of quantile regression with cointegrated time series. In addition, a new cointegration model with quantile-varying coefficients is proposed. In the proposed model, the value of cointegrating coefficients may be affected by the shocks and thus may vary over the innovation quantile. The proposed model may be viewed as a stochastic cointegration model which includes the conventional cointegration model as a special case. It also provides a useful complement to cointegration models with (G)ARCH effects. Asymptotic properties of the proposed model and limiting distribution of the cointegrating regression quantiles are derived. In the presence of endogenous regressors, fully-modified quantile regression estimators and augmented quantile cointegrating regression are proposed to remove the second order bias and nuisance parameters. Regression Wald tests are constructed based on the fully modified quantile regression estimators. An empirical application to stock index data highlights the potential of the proposed method.     

Bayesian modeling of joint and conditional distributions

In this paper, we study a Bayesian approach to flexible modeling of conditional distributions. The approach uses a flexible model for the joint distribution of the dependent and independent variables and then extracts the conditional distributions of interest from the estimated joint distribution. We use a finite mixture of multivariate normals (FMMN) to estimate the joint distribution. The conditional distributions can then be assessed analytically or through simulations. The discrete variables are handled through the use of latent variables. The estimation procedure employs an MCMC algorithm. We provide a characterization of the Kullback–Leibler closure of FMMN and show that the joint and conditional predictive densities implied by the FMMN model are consistent estimators for a large class of data generating processes with continuous and discrete observables. The method can be used as a robust regression model with discrete and continuous dependent and independent variables and as a Bayesian alternative to semi- and non-parametric models such as quantile and kernel regression. In experiments, the method compares favorably with classical nonparametric and alternative Bayesian methods.     

A kind of new time-weighted nonnegative lasso index-tracking model and its application

This study describes improved index-tracking methods to replicate the target index’s market performance in a high-dimensional sparse linear regression with nonnegative constraints on the coefficients. The main objective of this study is to construct a sparse portfolio with a better prediction effect and robustness. Considering the influence of time factors on index tracking, we propose a time-weighted nonnegative lasso index tracking model under different market constraints and define two new time-weighted construction methods. This index tracking model is an extension of Lasso and has variable selection consistency and estimation consistency under time-weighted nonnegative irrepresentable conditions similar to the irrepresentable condition in Lasso. We use the multiplicative updates algorithm to obtain the model’s solution since it is faster and simpler. The constrained index tracking problem in the stock market without short sales is studied in the latter part. The empirical results indicate that the optimized time-weighted nonnegative lasso index tracking model can obtain a smaller out-of-sample tracking error. The constructed portfolio has a better prediction effect and robustness, and we find that the exponential time-weighted method is better than the linear time-weighted method in capturing time information.     

Common correlated effects estimation of heterogeneous dynamic panel quantile regression models

This paper proposes a quantile regression estimator for a heterogeneous panel model with lagged dependent variables and interactive effects. The paper adopts the Common Correlated Effects (CCE) approach proposed in the literature and demonstrates that the extension to the estimation of dynamic quantile regression models is feasible under similar conditions to the ones used in the literature. The new quantile regression estimator is shown to be consistent and its asymptotic distribution is derived. Monte Carlo studies are carried out to study the small sample behavior of the proposed approach. The evidence shows that the estimator can significantly improve on the performance of existing estimators as long as the time series dimension of the panel is large. We present an application to the evaluation of Time-of-Use pricing using a large randomized control trial.     

Sequential estimate for linear regression models with uncertain number of effective variables

As a result of novel data collection technologies, it is now common to encounter data in which the number of explanatory variables collected is large, while the number of variables that actually contribute to the model remains small. Thus, a method that can identify those variables with impact on the model without inferring other noneffective ones will make analysis much more efficient. Many methods are proposed to resolve the model selection problems under such circumstances, however, it is still unknown how large a sample size is sufficient to identify those “effective” variables. In this paper, we apply sequential sampling method so that the effective variables can be identified efficiently, and the sampling is stopped as soon as the “effective” variables are identified and their corresponding regression coefficients are estimated with satisfactory accuracy, which is new to sequential estimation. Both fixed and adaptive designs are considered. The asymptotic properties of estimates of the number of effective variables and their coefficients are established, and the proposed sequential estimation procedure is shown to be asymptotically optimal. Simulation studies are conducted to illustrate the performance of the proposed estimation method, and a diabetes data set is used as an example.     

分位数局部调整模型及应用

本文基于分位数的回归理论与方法,提出了一个新的经济计量模型:分位数局部调整模型,并给出了其数学表示、参数估计与预测方法等一整套建模技术。分位数局部调整模型能够细致地给出响应变量在各个分位点上的条件分位数,便于揭示响应变量位置、散布与形状等动态调整过程的全景信息,从而得到比均值局部调整模型更为深刻的结果。最后,将分位数局部调整模型应用于中国货币需求分析,结果显示,在货币需求的不同阶段,不仅调整速度不同,调整方式也呈现出非对称性;M1存在货币失踪之谜现象,而M2却在条件密度第一个最优区域实现了供求均衡;最优货币需求条件密度曲线较为分散,这为央行制定货币政策预留了足够的空间。     

Markov switching quantile autoregression

This paper considers the location‐scale quantile autoregression in which the location and scale parameters are subject to regime shifts. The regime changes in lower and upper tails are determined by the outcome of a latent, discrete‐state Markov process. The new method provides direct inference and estimate for different parts of a non‐stationary time series distribution. Bayesian inference for switching regimes within a quantile, via a three‐parameter asymmetric Laplace distribution, is adapted and designed for parameter estimation. Using the Bayesian output, the marginal likelihood is readily available for testing the presence and the number of regimes. The simulation study shows that the predictability of regimes and conditional quantiles by using asymmetric Laplace distribution as the likelihood is fairly comparable with the true model distributions. However, ignoring that autoregressive coefficients might be quantile dependent leads to substantial bias in both regime inference and quantile prediction. The potential of this new approach is illustrated in the empirical applications to the US inflation and real exchange rates for asymmetric dynamics and the S&P 500 index returns of different frequencies for financial market risk assessment.     

Decomposition of differences in distribution using quantile regression

This paper proposes a semiparametric estimator of distribution functions in the presence of covariates. The method is based on the estimation of the conditional distribution by quantile regression. The conditional distribution is then integrated over the range of covariates. Counterfactual distributions can be estimated, allowing the decomposition of changes in distribution into three factors: coefficients, covariates and residuals. Sources of changes in wage inequality in the USA between 1973 and 1989 are examined. Unlike most of the literature, we find that residuals account for only 20% of the explosion of inequality in the 80s.