Network correlation between investor’s herding behavior and overconfidence behavior

It is generally accepted that herding behavior and overconfidence behavior are unrelated or even mutually exclusive.However,these behaviors can both lead to some similar market anomalies,such as excessive trading volume and volatility in the stock market.Due to the limitation of traditional time series analysis,we try to study whether there exists network relevance between the investor’s herding behavior and overconfidence behavior based on the complex network method.Since the investor’s herding behavior is based on market trends and overconfidence behavior is based on past performance,we convert the time series data of market trends into a market network and the time series data of the investor’s past judgments into an investor network.Then,we update these networks as new information arrives at the market and show the weighted in-degrees of the nodes in the market network and the investor network can represent the herding degree and the confidence degree of the investor,respectively.Using stock transaction data of Microsoft,US S&P 500 stock index,and China Hushen 300 stock index,we update the two networks and find that there exists a high similarity of network topological properties and a significant correlation of node parameter sequences between the market network and the investor network.Finally,we theoretically derive and conclude that the investor’s herding degree and confidence degree are highly related to each other when there is a clear market trend.

Semiparametric estimation of a Box-Cox transformation model with varying coefficients model

This paper considers the estimation of a Box-Cox transformation model with varying coefficient. A two-step approach is proposed in which the first step estimates the varying coefficients nonparametrically for any given parameter α in the transformation function. Then a one-dimensional search of α has been employed based on some least absolute deviation criterion function. The validity of our estimator does not require independence assumption thus is robust to the conditional heteroscedasticity. A simulation study shows a reasonably well finite sample performance. Additionally, a comprehensive empirical study has been carefully examined.

Semiparametric estimation of average treatment effect through a random coefficient dummy endogenous variable model

This paper provides an estimation procedure for average treatment effect through a random coefficient dummy endogenous variable model. A leading example of the model is estimating the effect of a training program on earnings. The model is composed of two equations:an outcome equation and a decision equation.Given the linear restriction in outcome and decision equations,Chen(1999) provided a distribution-free estimation procedure under conditional symmetric error distributions. In this paper we extend Chen's estimator by relaxing the linear index into a nonparametric function,which greatly reduces the risk of model misspecification. A two-step approach is proposed:the first step uses a nonparametric regression estimator for the decision variable,and the second step uses an instrumental variables approach to estimate average treatment effect in the outcome equation. The proposed estimator is shown to be consistent and asymptotically normally distributed. Furthermore,we investigate the finite performance of our estimator by a Monte Carlo study and also use our estimator to study the return of college education in different periods of China. The estimates seem more reasonable than those of other commonly used estimators.

Estimation of Partially Specified Spatial Panel Data Models with Random-Effects

In this article, we study estimation of a partially specified spatial panel data linear regression with random-effects. Under the conditions of exogenous spatial weighting matrix and exogenous regressors, we give an instrumental variable estimation. Under certain sufficient assumptions, we show that the proposed estimator for the finite dimensional parameter is root-N consistent and asymptotically normally distributed and the proposed estimator for the unknown function is consistent and asymptotically distributed. Consistent estimators for the asymptotic variance-covariance matrices of both the parametric and unknown components are provided. The Monte Carlo simulation results verify our theory and suggest that the approach has some practical value.

Statistical Inference of Partially Specified Spatial Autoregressive Model

This paper studies estimation of a partially specified spatial autoregressive model with heteroskedas- ticity error term. Under the assumption of exogenous regressors and exogenous spatial weighting matrix, the unknown parameter is estimated by applying the instrumental variable estimation. Under certain sufficient conditions, the proposed estimator for the finite dimensional parameters is shown to be root-n consistent and asymptotically normally distributed; The proposed estimator for the unknown function is shown to be consis- tent and asymptotically distributed as well, though at a rate slower than root-n. Consistent estimators for the asymptotic variance-covariance matrices of both estimators are provided. Monte Carlo simulations suggest that the proposed procedure has some practical value.

Research on the Education of Migrant Children in China:A Review of the Literature

With the rapid urbanization and mass internal migration in China during the past several decades, the population of children who migrate with their parents to the cities has now reached over 35 million. The education of migrant children poses significant challenges to China's hukou based education system. In this paper, we first review the policy developments and descriptive studies related to migrant children's education to offer a comprehensive view of the issue. We then provide in-depth examination of several important quantitative literatures, including the effect of parental migration on children's education, schooling choices of migrant children and their impacts on school performance, peer effects of migrant children in urban public schools. Overall, although considerable progress has been made regarding migrant children's education in China, more fundamental policy reforms are necessary to improve the quality of migrant children^ education at the compulsory education level and beyond.

Competitive Equilibria and Benefit Distributions of Population Production Economies with External Increasing Returns

Inspired by the work of population games,we establish the model of population production economies with external increasing returns and introduce the notion of competitive equilibria.We first prove the existence of competitive equilibria under some regular assumptions.Furthermore,we assume that there exists the cooperative behavior of different populations.By proving the existence of transferable utility(TU)core,we analyze the benefit distributions of population production economies with external increasing returns.

Labour Markets in China： A Study of the Structure and Evolution of Wages

This paper examines the development of labour markets and the evolution of a structure of wages in China, using household surveys for 1988, 1995, 2002 and 2007. It finds evidence of both provincial and sectoral segmentation in labour markets, with eastern regions and the state-controlled sector enjoying high wage premiums in the early reform period. During the reform, China has progressed slowly towards an integrated labour market with convergence in incomes between the rural non-agriculture sector and the urban market-based sector by 1995, when industry flourished in the rural areas. The wage gap between the rural non-agriculture sector and other sectors increased and the urban state-controlled sector remained segmented with respect to all other sectors up to 2002. However, the data from 2007 show there has been increasing sectoral and spatial integration.

Efcient Estimation of Varying Coefcient Seemly Unrelated Regression Model

In this paper, we propose a class of varying coefficient seemingly unrelated regression models, in which the errors are correlated across the equations. By applying the series approximation and taking the contemporaneous correlations into account, we propose an efficient generalized least squares series estimation for the unknown coefficient functions. The consistency and asymptotic normality of the resulting estimators are established. In comparison with the ordinary/east squares ones, the proposed estimators are more efficient with smaller asymptotical variances. Some simulgtlon＇studies and a real application are presented to demonstrate the finite sample performance of the proposed methods. In addition, based on a B-spline approximation, we deduce the asymptotic bias and variance of the proposed estimators.

Existence of Solutions of Generalized Quasi-Variational Relation Problems by the Inductive Relations and Some Applications

By the inductive relations,this paper first obtains some existence theorems of solutions for generalized quasi-variational relation problems,which are different from other papers.As applications,some existence theorems of solutions for generalized quasi-equilibrium problems and NS-equilibria for noncooperative games under uncertainty are obtained.

Quantitative Analysis of the COVID-19 Pandemic Shock to Household Consumption in China

We study the impact of the COVID-19 pandemic shock on household consumption in China.Using household survey data,we find that the proportion of liquidity-constrained households increases quickly,but the constraint levels vary across distinct groups.We build a heterogeneous agent life cycle incomplete market model to analyze the long-run and short-run effects of the pandemic shock.The quantitative results reveal a slow recovery of consumption due to three reasons:hiking unemployment rate,declining labor productivity,and worsening income stability.The hiking unemployment rate plays the key role in households,consumption reduction since it simultaneously leads to a negative income effect and upsurging precautionary saving motives.Our paper highlights the importance of maintaining a stable labor market for faster recovery.

A Bayesian Nonparametric Investigation of the Predictive Effect of Exchange Rates on Commodity Prices

This study proposes a full Bayesian nonparametric procedure to investigate the predictive power of exchange rates in relation to commodity prices for three commodity-exporting countries:Canada,Australia,and New Zealand.We propose a new time-dependent infinite mixture of a normal linear regression model of the conditional distribution of the commodity price index.The mixing weights follow a set of Probit stick-breaking priors that are time-varying.We find that exchange rates have a positive predictive effect in general,but accounting for time variation does not improve forecasting performance.By contrast,the intercept in the regression and the lagged dependent variable show signs of parameter change over time in most cases,which is important in forecasting both the mean and the density of commodity prices one period ahead.The results also suggest that the variance is a large source of the time variation in the conditional distribution of commodity prices.

Existence of Weakly Cooperative Equilibria for Infinite-Leader-Infinite-Follower Games

In this paper,we first generalize Yang and Ju’s(J Glob Optim 65:563–573,2016)result in Hausdorff topological vector spaces.Second,we introduce the model of leader-follower games with infinitely many leaders and followers,that is,infiniteleader-infinite-follower game.We next introduce the notion of weakly cooperative equilibria for infinite-leader-infinite-follower games and prove the existence result.

A Rapid Grid Search Method for Solving Dynamic Programming Problems in Economics

We introduce a rapid grid search method in solving dynamic program- ming problems in economics. Compared to mainstream grid search methods, by us- ing local information of the Bellman equation, this method can significantly increase the efficiency in solving dynamic programming problems by reducing the grid points searched in the control space.

An Empirical Test of the ＂Financial Accelerator＂ in China： Evidence from the Chinese Industrial Enterprises Database

The purpose of this paper is to test the applicability of the ＂financial ac- celerator＂ mechanism to China. Using the Chinese Industrial Enterprises Database, we find strong evidence suggesting that the employment and investment of leveraged firms are less responsive to aggregate fluctuations. This finding goes against the im- plications of the ＂financial accelerator＂. To make sure our empirical result is reliable, we have done several robustness checks using different estimation methods and sub- samples.

The Nature and Avoidance of the ＂Middle Income Trap＂

The ＂middle income trap＂ is a significant theoretical and practical issue closely related to the economic and social transition and sustainable development of a country. This paper explores the essence of the ＂middle income trap＂ and ways to avoid it. It reveals that the inner nature of the ＂middle income trap＂ lies in the institutional transition dilemma, which results essentially from a lack of reasonable and clear definitions of governance boundaries between government and market as well as government and society. This lack of boundaries causes coexistent and interrelated government inefficiency, market distortion/failure and social anomie, leading to a stagnant transition from a factor-driven to an efficiency-driven and further innovation-driven economy. Moreover, this paper proposes that the proper way to avoid the ＂trap＂ can be found in the reconstruction of the state governance mode, that is, to transition from a development-oriented and omnipotent government to a public service-oriented and limited government, from factor-driven to efficiency-driven and further innovation-driven development, and from a traditional society to a modem civil society through defining reasonable and clear boundaries between government and market as well as government and society. Thus, reconstruction can establish a state public governance mode featuring the interactive role of government, market and society, and achieve the modernization of state governance systems and capacity.

Robust estimation for partially linear models with large-dimensional covariates

We are concerned with robust estimation procedures to estimate the parameters in partially linear models with large-dimensional covariates. To enhance the interpretability, we suggest implementing a nonconcave regularization method in the robust estimation procedure to select important covariates from the linear component. We establish the consistency for both the linear and the nonlinear components when the covariate dimension diverges at the rate of o(n1/2), where n is the sample size. We show that the robust estimate of linear component performs asymptotically as well as its oracle counterpart which assumes the baseline function and the unimportant covariates were known a priori. With a consistent estimator of the linear component, we estimate the nonparametric component by a robust local linear regression. It is proved that the robust estimate of nonlinear component performs asymptotically as well as if the linear component were known in advance.Comprehensive simulation studies are carried out and an application is presented to examine the fnite-sample performance of the proposed procedures.

Robust low-rank data matrix approximations

We review some recent approaches to robust approximations of low-rank data matrices.We consider the problem of estimating a low-rank mean matrix when the data matrix is subject to measurement errors as well as gross outliers in some of its entries.The purpose of the paper is to make various algorithms accessible with an understanding of their abilities and limitations to perform robust low-rank matrix approximations in both low and high dimensional problems.

A group adaptive elastic-net approach for variable selection in high-dimensional linear regression

In practice, predictors possess grouping structures spontaneously. Incorporation of such useful information can improve statistical modeling and inference. In addition, the high-dimensionality often leads to the collinearity problem. The elastic net is an ideal method which is inclined to reflect a grouping effect. In this paper, we consider the problem of group selection and estimation in the sparse linear regression model in which predictors can be grouped. We investigate a group adaptive elastic-net and derive oracle inequalities and model consistency for the cases where group number is larger than the sample size. Oracle property is addressed for the case of the fixed group number. We revise the locally approximated coordinate descent algorithm to make our computation. Simulation and real data studies indicate that the group adaptive elastic-net is an alternative and competitive method for model selection of high-dimensional problems for the cases of group number being larger than the sample size.

Inspections and Information Disclosure： Quality Regulations with Incomplete Enforcement

The weak enforcement and monitoring systems employed in China （e.g., insufficient inspection resources and negligible fines for noncompliance） are widely blamed for the growing unrest over food safety in the country. Given this development, we consider a model where quality inspection performed by agencies is a means of disclosing information on product quality. We analyze the price-quality equilibrium scheme and show that a higher probability of inspection leads to lower price premiums attached to qualified products. We further investigate the welfare effect of minimum quality standards and inspection efforts and show that they should be complementary. We finally suggest that a state dependent inspection strategy, such as not inspecting those firms that have previously been found to be noncompliant, will enhance social welfare.