中国宏观经济对沪市的波动溢出效应研究——基于VAR（k）-MGARCH—BEKK（1，1）模型

本文基于VAR（k）-MGARCH—BEKK（1,1）模型和Wald检验探讨了2003—2008年期间的全国银行间同业拆借利率、汇率、企业商品价格指数、工业增加值增长率、M0、宏观景气指数和消费价格指数对沪市的波动溢出效应,结果表明,除了企业商品价格指数和M0之外,其它宏观经济指标的增长率（速度）均对沪市收益率产生显著的、直接的波动溢出效应。     

具有GARCH（1，1）-正态误差项的非对称单位根检验研究

摘　　要:理论研究表明许多经济变量呈现出非对称的门限自回归（TAR）或动态门限自回归（M-TAR）数据生成机制,因而非对称单位根检验就成为该领域的主要研究方向之一。本文对非对称单位根检验Enders-Granger方法在GARCH（1,1）-正态误差项下的检验水平与检验势作了系统的仿真研究。研究表明：GARCH（1,1）-正态误差项的TAR或M-TAR模型会对该方法的检验水平和检验势产生重要影响。     

A test of the normality assumption in ordered probit model

This paper presents a Lagrange multiplier test of the normality assumption underlying the ordered probit model. The test is presented both for the standard ordered probit model and a version in which censoring is present in the dependent variable. The test is then compared to normality tests proposed here compares favorably to tests based on artificial regression techinques.     

A monte carlo comparison of tests based on the durbin-watson statistic with other autocorrelation tests in dynamic models

This paper examines the sampling properties of a number of serial correlation tests in dynamic linear models which include one or two lags of the dependent variable. Among the tests considered are the Durbin-Watson (DW) bounds test, modified versions of the DW proposed recently by King and Wu and Inder, Durbin's m test, Inder's point optimal test and a Hausman type test. Sampling designs include models with one or two lags of the dependent variable. The m, Hausman, and Inder's tests have the best performance, while Inder's modified DW test appears to be better than the other DW based tests. Results also suggest that tests are less powerful and more sensitive to design parameters in models with higher dynamics, with the DW-based tests being the most sensitive.     

Bootstrapping a consistent nonparametric goodness-of-fit test

In this paper, we employ the parametric bootstrap to approximate the finite sample distribution of a goodness-of-fit test statistic in Fan (1994). We show that the proposed bootstrap procedure works in that the bootstrap distribution conditional on the random sample tends to the asymptotic distribution of the test statistic in probability. A simulation study demonstrates that the bootstrap approximation works extremely well in small samples with only 25 observations and is very robust to the value of the smoothing parameter in the kernel density estimation.     

地方保护主义和地区间贸易壁垒的检验性分析

随着市场经济的发展,贸易作为经济运行中的一个环节,其重要性越来越凸现出来,而阻碍贸易正常进行的地方保护主义和贸易壁垒对宏观经济的良性发展起到了消极的作用。准确把握我国地方保护主义和地区间贸易壁垒的现状意义重大。本文基于国务院发展研究中心发展战略和区域经济研究部与北京大学光华管理学院所作的“地区间经济联系障碍的调查问卷”,对前期回收的有效样本,运用统计检验分析的方法,就当前地方保护和贸易壁垒的存在性以及企业的不同类型是否与其面临的地方保护和贸易壁垒有程度上的关联等问题做了相关研究,认为企业的规模和所有制性质对企业面临的地方保护和贸易壁垒基本没有程度上的影响;同时,统计分析结果还表明目前国内地区间贸易壁垒偏重于无形和隐形的形式。     

The application of the durbin-watson test to the dynamic regression model under normal and non-normal errors

Until recently, a difficulty with applying the Durbin-Watson (DW) test to the dynamic linear regression model has been the lack of appropriate critical values. Inder (1986) used a modified small-disturbance distribution (SDD) to find approximate critical values. King and Wu (1991) showed that the exact SDD of the DW statistic is equivalent to the distribution of the DW statistic from the regression with the lagged dependent variables replaced by their means. Unfortunately, these means are unknown although they could be estimated by the actual variable values. This provides a justification for using the exact critical values of the DW statistic from the regression with the lagged dependent variables treated as non-stochastic regressors. Extensive Monte Carlo experiments are reported in this paper. They show that this approach leads to reasonably accurate critical values, particularly when two lags of the dependent variable are present. Robustness to non-normality is also investigated.     

Extending the Barnard’s test to non-inferiority

In 1945, George Alfred Barnard presented an unconditional exact test to compare two independent proportions. Critical regions for this test, by construction accomplish the very useful property of being Barnard convex sets. Besides, there are empirical findings suggesting that Barnard’s test is the most generally powerful. For Barnard’s test, calculation of critical regions is complicated due that they are constructed in an iterative form until is obtained a test size, as close as possible to the nominal significance level and less than or equal to it. In this article we present an extension to non-inferiority of this very leading test. This extension was contructed for any dissimilarity measure and tables were constructed for the difference between proportions. Also we calculate the critical regions for this extended test for sample sizes less or equal than 30, nominal significance level 0.01, 0.025, 0.05, and 0.10 and for non-inferiority margins 0.05, 0.10, 0.15, and 0.20. Additionally, we computed test sizes for the mentioned configurations. To do this calculations, we have written a program in the R environment.     

Testing hypotheses about covariance matrices using bootstrap methods

The usual chi-squared approximation to test statistics based on normal theory for testing covariance structures of multivariate populations is very sensitive to the normality assumption. Two general bootstrap procedures are developed in this paper to obtain approximately valid critical values for these test statistics when the data are not normally distributed. The first is based on separate sampling from individual samples, and the second is based on sampling from pooled samples. Although the second method requires more assumptions, its small sample properties are better.     

Estimation of regression coefficient from survey data based on test of significance

The ordinary least squares (OLS)estimator of regression coeffecient is implicitly based on I.I.D.assumption, which is rarely satisfied by survey data. Many approaches are proposed in the literature which can be classified in two broad categories as model based and design consistent.Du Mouchel and Duncan (1983) proposed a test statistic λwhich helps in testing the ignorability of sampling weights.In this article a preliminary test estimator based on λ is proposed. The model based properties of this estimator has been invetigated theoritically where as to study the design based properties simulation approach is adopted. It has been observed that the proposed estimator is a better cimpromise between model based and randomization based inferential frame work.