非线性动态面板模型的条件GMM估计

基于时间序列的实证分析已经证实,很多经济变量的动态调整过程都存在非线性的平滑转换机制.本文将传统的线性动态面板模型扩展为平滑转换的非线性动态面板模型,并基于对非线性参数的格点搜索,提出了一种简便易行的非线性动态面板模型估计程序--条件GMM估计,其估计量具有一致性.仿真实验结果显示,条件GMM估计量在有限样本下具有良好表现.同时,非线性动态面板模型的条件GMM估计还为在非线性框架下检验面板单位根创造了条件.     

电梯检验方法探索

目前,国家对电梯的标准、法规不能同步于电梯产品的发展速度,造成现场检测项目同报告书中检验内容与要求或存在一定距离,而检验方法也就是无法实施操作。     

行业间生产率联动对中国工业生产率增长的影响——引入经济距离矩阵的空间GMM估计

本文把反映行业间生产率联动的购买距离矩阵和销售距离矩阵引入空间自回归模型,研究行业间生产率联动对我国工业生产率增长的影响。为了克服引入社会经济距离矩阵带来的异方差和矩阵的行标准化问题,本文采用空间GMM法进行模型的估计。结果表明,行业生产率联动对我国工业生产率增长具有显著的正影响,并且在资源密集型、劳动密集型和资本密集型工业行业中,行业间生产率联动对工业生产率增长的影响相对于其他因素的影响更为稳健。此外,由销售距离矩阵所体现的联动作用效果整体上大于购买距离矩阵体现的相关效果。     

简述压力容器外观检验

压力容器的检验中,外观检验为最基本的检验方法。本文主要对压力容器的外观检验方法和要点进行论述,并提出一些建议。     

检验机构如何把好检验报告质量关

产品质量检验报告能全面、客观地反映产品的质量信息,一般是由独立于供需双方的第三方专业检验机构完成的。第三方专业检验机构具有相对的独立性和公正性,有资格向社会出具公正数据（检验报告）。质检机构所从事的工作就是检验各种产品的质量,依据一定的技术标准给出所检产品是否合格的评价,并出具检验报告。那么,检验报告作为一种特殊的产品,检验机构又该如何把好质量关呢？     

棉花水杂检验与公量检验

在棉花水杂检验与公量检验中，除说明含水检验必须结合过秤外，又因棉包内外层吸湿的不同，造成假性亏重。成包皮棉在交接中，允许因大气原因造成的准重差率为５‰。但含杂也影响准重，对此问题结合公量检验进行了探讨，并提出了相应的建议。     

超检测范围检验及使用非标方法时检验报告的编制

质检所在检验工作中经常会遇到一些超能力范围检验、使用非标方法的现象.作为一个依法设置、为社会提供公正检验数据的检验机构来说,应对此现象做详细分析,并相应制定不同的检验报告编制模式,以求进一步规范检验工作、回避风险.     

如何提高检验报告的质量

质检机构所从事的工作就是检验各种产品的质量,依据一定的技术标准给出所检产品是否合格的评价,并出具检验报告。那么,检验报告作为一种特殊的产品,检验机构如何提高检验报告质量呢?注重原始记录的格式化原始记录是整个检验过程和检验结果信息的真实记录,是编制检验报告的基础。一般来说,检验机构都有     

我国宏观审慎监管有效性及其GMM方法检验

采用系统GMM方法对我国宏观审慎监管有效性检验发现，当前我国实施的部分宏观审慎工具是有效的，一定程度上降低了系统性风险的发生概率，但宏观审慎政策与货币政策的协调性较复杂，政策组合除了相互补充协调外，“政策抵消”和“政策超调”的现象也较为普遍，宏观审慎政策弥补货币政策缺陷的作用并没有得到充分发挥。因此，我们一方面要构建宏观审慎工具箱，根据各地情况选择工具，提高工具有效性；另一方面要充分发挥金融稳定发展委员会的监管协调作用，探索宏观审慎政策与货币政策相互补充的逻辑机制，构建货币政策和宏观审慎政策双支柱调控框架，提高我国宏观审慎监管的有效性。     

自相关检验方法的检验功效研究

研究目标:探究DW检验和LM检验的检验功效及其渐近性。研究方法:运用蒙特卡罗模拟实验方法结合相关影响因素对两种检验方法进行分析与比较。影响因素包括样本容量、解释变量的随机性及自相关性、随机误差项的自相关程度以及分布形态。研究发现:DW和LM检验功效与样本容量和随机误差项的自相关程度正相关,与解释变量的自相关程度负相关;解释变量的随机性对DW和LM检验功效无显著影响;误差项的几种常见分布形态的变化对DW和LM检验功效的影响可以忽略;在误差项存在一阶自相关的情况下,DW检验效果优于LM检验效果。研究创新:以DW检验和LM检验的假设条件为出发点,探究比较不同条件下自相关检验方法的检验功效。研究价值:在实证研究背景下为有效地选择自相关检验方法提供借鉴参考。     

GMM estimation of a maximum entropy distribution with interval data

We develop a generalized method of moments (GMM) estimator for the distribution of a variable where summary statistics are available only for intervals of the random variable. Without individual data, one cannot calculate the weighting matrix for the GMM estimator. Instead, we propose a simulated weighting matrix based on a first-step consistent estimate. When the functional form of the underlying distribution is unknown, we estimate it using a simple yet flexible maximum entropy density. Our Monte Carlo simulations show that the proposed maximum entropy density is able to approximate various distributions extremely well. The two-step GMM estimator with a simulated weighting matrix improves the efficiency of the one-step GMM considerably. We use this method to estimate the U.S. income distribution and compare these results with those based on the underlying raw income data.     

Kernel-weighted GMM estimators for linear time series models

This paper analyzes the higher-order asymptotic properties of generalized method of moments (GMM) estimators for linear time series models using many lags as instruments. A data-dependent moment selection method based on minimizing the approximate mean squared error is developed. In addition, a new version of the GMM estimator based on kernel-weighted moment conditions is proposed. It is shown that kernel-weighted GMM estimators can reduce the asymptotic bias compared to standard GMM estimators. Kernel weighting also helps to simplify the problem of selecting the optimal number of instruments. A feasible procedure similar to optimal bandwidth selection is proposed for the kernel-weighted GMM estimator.     

A Generalized Method of Moments Estimator for a Spatial Panel Model with an Endogenous Spatial Lag and Spatial Moving Average Errors

Abstract

This paper proposes a new generalized method of moments (GMM) estimator for spatial panel models with spatial moving average errors combined with a spatially autoregressive dependent variable. Monte Carlo results are given suggesting that the GMM estimator is consistent. The estimator is applied to English real estate price data.     

新凯恩斯菲利普斯曲线的弱识别检验和稳健估计

对基于GMM大样本统计量的检验和估计,弱识别会带来点估计不一致和置信区间规模不正确的问题。本文讨论了弱识别问题产生的原因、影响、检验和相关弱识别稳健统计量。用Cragg-Donald F统计量对NKPC重新进行了检验,发现的确存在弱识别问题,本文进而对NKPC的参数进行了弱识别稳健的估计,并且用CLR统计量构造了相应规模正确的置信区间。实证表明中国的通货膨胀具有明显的向前和向后行为,而向前行为从数量上来说要大于向后行为的影响,但是向后行为在数量上来说也是不可忽视的;子样本的检验表明通涨的向后行为有增强的趋势。     

Residual‐based IV estimation of dynamic panel data models with fixed effects

In dynamic panel regression, when the variance ratio of individual effects to disturbance is large, the system‐GMM estimator will have large asymptotic variance and poor finite sample performance. To deal with this variance ratio problem, we propose a residual‐based instrumental variables (RIV) estimator, which uses the residual from regressing Δy_i,t?1 on as the instrument for the level equation. The RIV estimator proposed is consistent and asymptotically normal under general assumptions. More importantly, its asymptotic variance is almost unaffected by the variance ratio of individual effects to disturbance. Monte Carlo simulations show that the RIV estimator has better finite sample performance compared to alternative estimators. The RIV estimator generates less finite sample bias than difference‐GMM, system‐GMM, collapsing‐GMM and Level‐IV estimators in most cases. Under RIV estimation, the variance ratio problem is well controlled, and the empirical distribution of its t‐statistic is similar to the standard normal distribution for moderate sample sizes.     

The finance-growth nexus: The role of banks

We contribute to the finance-growth nexus literature by showing that credit origin, bank ownership, type of credit, and bank type matter in economic growth. We use a unique dataset covering 5555 cities in Brazil, with granular information on credit characteristics. We find that non-earmarked credit to the corporate sector is associated with municipal economic growth more strongly than earmarked credit, despite the increase in the relevance of the latter after the global financial crisis. We also find that the type of credit—whether the loans are general purpose or for a specific purpose—is associated with economic growth in different ways. Overall, credit provided to the corporate sector by domestic private banks is correlated with higher economic growth rates. In contrast, the relationship between credit from state-owned banks and economic growth becomes statistically significant only after the crisis. Although we follow the finance-growth literature in our empirical exercises using internal instruments in generalized method of moments (GMM) estimations, we also conduct robustness tests using two additional external instruments: the number of complaints filed against each bank and local credit accessibility. Our findings with external instruments are the same with respect to the use of traditional internal instruments in GMM estimations.     

The determinants of commercial banking profitability in low-, middle-, and high-income countries

Using a broad bank-level dataset and the GMM estimator technique described by Arellano and Bover (1995), this paper analyses how bank-specific characteristics, macroeconomic variables, and industry-specific factors affect the profitability of 10,165 commercial banks across 118 countries over the period from 1998 to 2012. Grouping the countries according to three income levels, we show that the determinants of bank profitability included in our model can explain existing profitability differences among commercial banks in low-, middle-, and high-income countries. The profitability determinants vary quite widely across the different levels of income in terms of significance, sign and size of the effect. The level of income has thus an important impact on the determinants of bank profitability.     

A Comparative Analysis of Different IV and GMM Estimators of Dynamic Panel Data Models

It is well known that the usual procedures for estimating panel data models are inconsistent in the dynamic setting. A large number of consistent estimators however, have been proposed in the literature. This paper provides a survey of the majority of mainstream estimators, which tend to consist of IV and GMM ones. It also considers a newly proposed extension to the promising Wansbeek–Bekker estimator (Harris & Mátyás, 2000). To provide guidance to the applied researcher working on micro-datasets, the small sample performance of these estimators is evaluated using a set of Monte Carlo experiments.     

Neglected heterogeneity in moment condition models

The central concern of this paper is parameter heterogeneity in models specified by a number of unconditional or conditional moment conditions and thereby the provision of a framework for the development of apposite optimal $m$-tests against its potential presence. We initially consider the unconditional moment restrictions framework. Optimal $m$-tests against moment condition parameter heterogeneity are derived with the relevant Jacobian matrix obtained in terms of the second order own derivatives of the moment indicator in a leading case. GMM and GEL tests of specification based on generalized information matrix equalities appropriate for moment-based models are described and their relation to optimal $m$-tests against moment condition parameter heterogeneity examined. A fundamental and important difference is noted between GMM and GEL constructions. The paper is concluded by a generalization of these tests to the conditional moment context and the provision of a limited set of simulation experiments to illustrate the efficacy of the proposed tests.