Efficient Estimation of Additive Partially Linear Models

I consider the problem of estimating an additive partially linear model using general series estimation methods with polynomial and splines as two leading cases. I show that the finite-dimensional parameter is identified under weak conditions. I establish the root-n-normality result for the finite-dimensional parameter in the linear part of the model and show that it is asymptotically more efficient than a semiparametric estimator that ignores the additive structure. When the error is conditional homoskedastic, my finite-dimensional parameter estimator reaches the semiparametric efficiency bound. Efficient estimation when the error is conditional heteroskedastic is also discussed.     

Choice is suffering: A Focused Information Criterion for model selection

In contrast to conventional measures, the Focused Information Criterion (FIC) allows the purpose-specific selection of models, thereby reflecting the idea that one kind of model might be appropriate for inferences on a parameter of interest, but not for another. Ever since its invention, the FIC has been increasingly applied in the realm of statistics, but this concept appears to be virtually unknown in the economic literature. Using a straightforward analytical example, this paper provides for a didactic illustration of the FIC and demonstrates its usefulness in economic applications.     

On Optimal Instrumental Variables Estimation of Stationary Time Series Models

In many time series models, an infinite number of moments can be used for estimation in a large sample. I supply a technically undemanding proof of a condition for optimal instrumental variables use of such moments in a parametric model. I also illustrate application of the condition in estimation of a linear model with a disturbance that is serially uncorrelated and conditionally heteroskedastic.     

Valid Inference in Partially Unstable Generalized Method of Moments Models

This paper considers time series Generalized Method of Moments (GMM) models where a subset of the parameters are time varying. We focus on an empirically relevant case with moderately large instabilities, which are well approximated by a local asymptotic embedding that does not allow the instability to be detected with certainty, even in the limit. We show that for many forms of the instability and a large class of GMM models, usual GMM inference on the subset of stable parameters is asymptotically unaffected by the partial instability. In the empirical analysis of presumably stable parameters—such as structural parameters in Euler conditions—one can thus ignore moderate instabilities in other parts of the model and still obtain approximately correct inference.     

Alternative Techniques for Estimation of Cross-Section Gravity Models

This paper compares four different estimators with respect to their suitability for cross‐section gravity model estimation. In many circumstances, a Hausman–Taylor approach can be recommended. This framework may provide consistent parameter estimates, when OLS or the traditional random‐effects model are biased. In contrast to the fixed‐effects approach, it allows to estimate parameters of variables such as GDP or GDP per capita, which vary only in a single dimension. The Hausman–Taylor model deserves attention in the estimation of cross‐sectional gravity models.     

Estimation and Forecasting in Models with Multiple Breaks

This paper develops a new approach to change-point modelling that allows the number of change-points in the observed sample to be unknown. The model we develop assumes that regime durations have a Poisson distribution. It approximately nests the two most common approaches: the time-varying parameter (TVP) model with a change-point every period and the change-point model with a small number of regimes. We focus considerable attention on the construction of reasonable hierarchical priors both for regime durations and for the parameters that characterize each regime. A Markov chain Monte Carlo posterior sampler is constructed to estimate a version of our model, which allows for change in conditional means and variances. We show how real-time forecasting can be done in an efficient manner using sequential importance sampling. Our techniques are found to work well in an empirical exercise involving U.S. GDP growth and inflation. Empirical results suggest that the number of change-points is larger than previously estimated in these series and the implied model is similar to a TVP (with stochastic volatility) model.     

广义变系数模型的B样条估计及R软件实现

结合基函数逼近技术以及拟似然方法,对广义变系数模型,提出了一个B样条估计方法,并结合R统计软件,对所提出的估计方法给出了一个基于R软件的实现过程。数据模拟表明所提出的B样条估计方法以及R软件的实现过程均是可行的。     

Estimation of Asymmetric Stochastic Volatility Models for Stock-Exchange Index Returns

The objective of this paper is to put forward a new autoregressive asymmetric stochastic volatility model for modeling volatility and to compare results obtained for this model with an autoregressive stochastic model and another asymmetric volatility model, such as, asymmetric generalized autoregressive conditional heteroskedasticity model. The results obtained from the estimation by maximum likelihood have shown the volatility behavior is asymmetric in the majority of cases. This fact is better shown by the ARSVA model, than the rest of alternative models. Moreover, the ARSVA model is able to reproduce other stylized facts of such series, such as high kurtosis, no autocorrelation of returns, slow decreasing of the autocorrelation function of the squared returns and high persistence.

证券信息公开原则的基本标准

所谓信息公开原则,即信息披露制度,是指在证券发行和交易的过程中,证券发行人和其他有关当事人必须向社会公众披露能够影响投资者做出决定的一切信息资料。公开原则是公正、公平的前提和保障,是实现公平、公正的必要措施,是证券法的精髓所在。实行公开原则有利于投资者在全面了解情况的基础上做出投资决定,以维护投资者的利益;有利于发行公司受到广大股东和社会公众的监督,从而改善自身的经营管理;有利于国家及时掌握证券发行和交易信息,对证券市场实行统一管理和监督。 　　一、证券信息公开原则适用的界域 　　证券信息公开原…     

Generalized Spectral Tests for Conditional Mean Models in Time Series with Conditional Heteroscedasticity of Unknown Form

Economic theories in time series contexts usually have implications on and only on the conditional mean dynamics of underlying economic variables. We propose a new class of specification tests for time series conditional mean models, where the dimension of the conditioning information set may be infinite. Both linear and nonlinear conditional mean specifications are covered. The tests can detect a wide range of model misspecifications in mean while being robust to conditional heteroscedasticity and higher order time-varying moments of unknown form. They check a large number of lags, but naturally discount higher order lags, which is consistent with the stylized fact that economic behaviours are more affected by the recent past events than by the remote past events. No specific estimation method is required, and the tests have the appealing „nuisance parameter free” property that parameter estimation uncertainty has no impact on the limit distribution of the tests. A simulation study shows that it is important to take into account the impact of conditional heteroscedasticity; failure to do so will cause overrejection of a correct conditional mean model. In a horse race competition on testing linearity in mean, our tests have omnibus and robust power against a variety of alternatives relative to some existing tests. In an application, we find that after removing significant but possibly spurious autocorrelations due to nonsynchronous trading, there still exists significant predictable nonlinearity in mean for S&P 500 and NASDAQ daily returns.     

信息化环境下企业成本管理模式初探

文章首先分析目前企业成本管理面临的主要问题,分析在信息化环境下成本管理理论的发展,进而提出作业成本法将成为信息化环境下成本管理的主要手段,并简要构建了作业成本管理的基本运行流程和应用前景。     

On Efficient Estimation and Correct Inference in Models with Generated Regressors: a General Approach

Two approaches have been developed for deriving the properties of efficiency and consistency of standard errors of two step estimators of linear models containing current or lagged unobserved expectations of a single variable. One method is based on the derivatives of the likelihood function and information matrix, while the other uses the true covariance matrix of the disturbance vector when unknown parameters or variables are replaced by corresponding estimates. In this paper, the second approach is extended to cases where the structural equation is nonlinear and the model contains expectations of more than one variable or expectations of future variables. The properties of a frequently used estimator to deal with missing observations problems, a model involving a variance as an explanatory variable, and a recently developed estimator for autoregressive moving average models can be easily derived using the results of the paper. Methods for improving the efficiency of two step estimators are outlined.
JEL Classification Number: C13     

Review of Solution and Estimation Methods for Nonlinear Dynamic Stochastic General Equilibrium Models with the Zero Lower Bound

We review solution and estimation methods for nonlinear dynamic stochastic general equilibrium models and their application, with a special focus on the zero lower bound on the nominal interest rate. In a fully nonlinear setting, both the solution and estimation methods involve iterative procedures, and their computational expense grows rapidly with an increase in the dimensionality of state variables and parameters. We describe how the procedures deal with the dimensionality problem.     

Testing for Asymmetries in UK Macroeconomic Time Series

This paper examines the asymmetric properties of a broad range of quarterly postwar UK macroeconomic time series using recently developed test statistics for contractionary 'deepness' and 'steepness' relative to trend. We also examine the robustness of these test statistics to two alternative methods of detrending, namely Hodrick-Prescott filtering and structural time series modelling. We find strong corroborative evidence of asymmetric steepness relative to trend in durable consumption, total investment, investment in plant and machinery, exports and unemployment. We find weaker evidence of asymmetric deepness in savings, exports, labour hours, consumption and unemployment.     

Long-Memory,Asymmetry and Fat-Tailed GARCH Models in Value-at-Risk Estimation: Empirical Evidence from the Global Real Estate Markets

In this paper, we address the question of whether long memory, asymmetry, and fat-tails in global real estate markets volatility matter when forecasting the two most popular measures of risk in financial markets, namely Value-at-risk (VaR) and Expected Shortfall (ESF), for both short and long trading positions. The computations of both VaR and ESF are conducted with three long memory GARCH-class models including the Fractionally Integrated GARCH (FIGARCH), Hyperbolic GARCH (HYGARCH), and Fractionally Integrated Asymmetric Power ARCH (FIAPARCH). These models are estimated under three alternative innovation’s distributions: normal, Student, and skewed Student. To test the efficacy of the forecast, we employ various backtesting methodologies. Our empirical findings show that considering for long memory, fat-tails, and asymmetry performs better in predicting a one-day-ahead VaR and ESF for both short and long trading positions. In particular, the forecasting ability analysis points out that the FIAPARCH model under skewed Student distribution turns out to improve substantially the VaR and ESF forecasts. These results may have several potential implications for the market participants, financial institutions, and the government.

     

On the Need for Gender in Dynamic Models

In this paper we emphasize the need for more theoretical research using dynamic models that include gender as a variable of analysis. We begin by summarizing some of the main observations characterizing fertility, gender, and economic growth. We then explore three types of theoretical models: one-sex dynamic, two-sex static, and two-sex dynamic. We conclude that more models of the last type – dynamic models that include gender in the analysis – are needed to analyze issues that involve both an intertemporal and a gender dimension.     

国有企业改制中权力激励的帕累托最优标准

权力是一种巨大的激励力量,而获得权力或授予权力则是激励问题产生的前提。我国正值国有企业改制时期,需要建立企业的激励机制,需要探讨作为企业决定性激励资源的权力在不同岗位上的有效配置问题。构建具有坚实经济理论支持的合理配置权力资源的经济理论标准,具有十分重要的意义。