Moment-based estimation of smooth transition regression models with endogenous variables

Nonlinear regression models have been widely used in practice for a variety of time series and cross-section datasets. For purposes of analyzing univariate and multivariate time series data, in particular, smooth transition regression (STR) models have been shown to be very useful for representing and capturing asymmetric behavior. Most STR models have been applied to univariate processes, and have made a variety of assumptions, including stationary or cointegrated processes, uncorrelated, homoskedastic or conditionally heteroskedastic errors, and weakly exogenous regressors. Under the assumption of exogeneity, the standard method of estimation is nonlinear least squares. The primary purpose of this paper is to relax the assumption of weakly exogenous regressors and to discuss moment-based methods for estimating STR models. The paper analyzes the properties of the STR model with endogenous variables by providing a diagnostic test of linearity of the underlying process under endogeneity, developing an estimation procedure and a misspecification test for the STR model, presenting the results of Monte Carlo simulations to show the usefulness of the model and estimation method, and providing an empirical application for inflation rate targeting in Brazil. We show that STR models with endogenous variables can be specified and estimated by a straightforward application of existing results in the literature.     

Causality: a Statistical View

Statistical aspects of causality are reviewed in simple form and the impact of recent work discussed. Three distinct notions of causality are set out and implications for densities and for linear dependencies explained. The importance of appreciating the possibility of effect modifiers is stressed, be they intermediate variables, background variables or unobserved confounders. In many contexts the issue of unobserved confounders is salient. The difficulties of interpretation when there are joint effects are discussed and possible modifications of analysis explained. The dangers of uncritical conditioning and marginalization over intermediate response variables are set out and some of the problems of generalizing conclusions to populations and individuals explained. In general terms the importance of search for possibly causal variables is stressed but the need for caution is emphasized.     

Predictive financial models of the euro area: A new evaluation test

This paper investigates the predictive ability of financial variables for euro area growth. Our forecasts are built from univariate autoregressive and single equation models. Euro area aggregate forecasts are constructed both by employing aggregate variables and by aggregating country-specific forecasts. The forecast evaluation is based on a recently developed test for equal predictive ability between nested models. Employing a monthly dataset from the period between January 1988 and May 2005 and setting the out-of-sample period to be from 2001 onwards, we find that the single most powerful predictor on a country basis is the stock market returns, followed by money supply growth. However, for the euro area aggregate, the set of most powerful predictors includes interest rate variables as well. The forecasts from pooling individual country models outperform those from the aggregate itself for short run forecasts, while for longer horizons this pattern is reversed. Additional benefits are obtained when combining information from a range of variables or combining model forecasts.     

试论阐述平衡常数存在的问题

本文指出了普通化学中阐述平衡常数存在的两个问题,论证其把经验平衡常数与标准平衡常数混为一谈而导致的概念混乱;以及因处理不妥而产生的错误。并且还提出了相应的修正方法。     

Relative performance of the grid, nearest neighbor, and OLS estimators

This article provides various paradigms for the grid estimator, the most useful being a representation of the grid estimator as a combination of the nonparametric nearest neighbor estimator and a parametric estimator. Hence, the grid estimator falls into the class of semiparametric estimators. The article used this representation to derive the relative efficiency of the nearest neighbor, grid, and OLS estimators. Under statistically perfect conditions, the OLS estimator dominated the grid estimator, which in turn dominated the nearest neighbor estimator. A Monte Carlo experiment verified the theoretical results. A second Monte Carlo experiment showed the fragility of the OLS superiority to misspecification. The results cast light upon appraisal practice.     

中国地区性行政垄断与区域经济绩效——基于工具变量法的实证研究

在我国,地方官员间的晋升竞争和行政权力所带来的地下经济收益,都会导致地区性行政垄断的出现。地区性行政垄断变量因而会受到GDP、地方财税水平等政绩指标和企业经济指标的影响,并在OLS回归估计中表现出内生性问题。文章构建了一个省级面板数据计量模型,以工具变量法对地区性行政垄断的区域经济绩效及其内生性问题进行实证检验。结果发现:地区性行政垄断损害了4个方面的区域经济绩效——生产效率、资本配置效率、技术创新能力以及国际竞争力。由于地区性行政垄断这种地方官员行为在一定程度上内生于经济系统,以往常用的OLS估计可能会低估地区性行政垄断的危害性。     

On estimating Armington elasticities for Japan's meat imports

By fully accounting for the distinct tariff regimes levied on imported meat, we estimate substitution elasticities of Japan's two-stage import aggregation functions for beef, chicken and pork. Although the regression analysis crucially depends on the price that consumers face, the post-tariff price of imported meat depends not only on ad valorem duties but also on tariff rate quotas and gate price system regimes. The effective tariff rate is consequently evaluated by utilising monthly transaction data. To address potential endogeneity problems, we apply exchange rates that we believe to be independent of the demand shocks for imported meat. The panel nature of the data allows us to retrieve the first-stage aggregates via time dummy variables, free of demand shocks, to be used as part of the explanatory variable and as an instrument in the second-stage regression.     

政府绩效评估指标设计研究中“失去的研究”的补充研究——指标的可加性探索

针对政府绩效评估指标可加性,如“辖区内三个代表思想掌握状况”与“辖区内土豆3000斤／亩”如何相加的问题,目前学界鲜有研究。对于这种高属性的政府绩效评估指标,在设计中必须按照高属性维对象的聚类特性,首先将高属性维降为低属性维。本研究在标准化方式之外,采用数据挖掘的关联规则与集合理论相结合,借助于二态变量的方式,如在“社会和谐”指标下有“辖区内18岁以上有意愿人口就业率（1／0）”、“辖区内每百平方米乞丐数量（1／0）”,用指标聚中度和指标聚中向量实现了海量数据中同级指标的可加性。