Redundancy of Moment Conditions in Restricted GMM Estimation

Using a novel approach to calculating the rank of the difference of two asymptotic variance matrices, The author derives the necessary and sufficient conditions for an extra set of moment conditions to be redundant given a set of moment conditions in GMM estimation with general nonlinear restrictions. The necessary and sufficient conditions derived in this paper include as a special case the redundancy of moment conditions for GMM estimation without restrictions that was first derived by Breusch et al. （1999）. Therefore this paper advances the research on redundancy of moment conditions from unrestricted GMM estimation to a larger class of GMM estimation. To show their usefulness, the main results of the current paper are applied to instrumental variables estimation of linear regression models and the efficient estimation of seemingly unrelated regressions models, subject to restrictions.

A new model for predicting the total tree height for stems cut-to-length by harvesters in Pinus radiata plantations

A new model for predicting the total tree height for harvested stems from cut-to-length(CTL)harvester data was constructed for Pinus radiata(D.Don)following a conceptual analysis of relative stem profi les,comparisons of candidate models forms and extensive selections of predictor variables.Stem profi les of more than 3000 trees in a taper data set were each processed 6 times through simulated log cutting to generate the data required for this purpose.The CTL simulations not only mimicked but also covered the full range of cutting patterns of nearly 0.45×106 stems harvested during both thinning and harvesting operations.The single-equation model was estimated through the multipleequation generalized method of moments estimator to obtain effi cient and consistent parameter estimates in the presence of error correlation and heteroscedasticity that were inherent to the systematic structure of the data.The predictive performances of our new model in its linear and nonlinear form were evaluated through a leave-one-tree-out cross validation process and compared against that of the only such existing model.The evaluations and comparisons were made through benchmarking statistics both globally over the entire data space and locally within specifi c subdivisions of the data space.These statistics indicated that the nonlinear form of our model was the best and its linear form ranked second.The prediction accuracy of our nonlinear model improved when the total log length represented more than 20%of the total tree height.The poorer performance of the existing model was partly attributed to the high degree of multicollinearity among its predictor variables,which led to highly variable and unstable parameter estimates.Our new model will facilitate and widen the utilization of harvester data far beyond the current limited use for monitoring and reporting log productions in P.radiata plantations.It will also facilitate the estimation of bark thickness and help make harvester data a potential source of taper data to reduce the intensity and cost of the conventional destructive taper sampling in the fi eld.Although developed for P.radiata,the mathematical form of our new model will be applicable to other tree species for which CTL harvester data are routinely captured during thinning and harvesting operations.

Threshold Effects in the Capital Account Liberalization and Foreign Direct Investment Relationship

This paper examines how capital account liberalization （CAL） affects foreign direct investment （FDI） inflows. Authors use a dynamic panel model encompassing 14 Middle East countries over the period from 1985 to 2009. The findings suggest that countries that are able to reap the benefits of the capital openness policy satisfy certain threshold conditions regarding the level of financial development and institutional quality. Thus to promote FDI, governments in this region should develop a set of policies that not only focus on financial openness, but also on the improvement of the financial system and legal institutions.

Does nuclear energy reduce carbon emissions despite using fuels and chemicals?Transition to clean energy and finance for green solutions

Green power conversion is the shift away from traditional fuels towards clean energy sources such as nuclear power plants,hydroelectric dams,wind farms,and solar panels.This research examines the impact of clean energy demand and green financing on reducing carbon emissions in 29 economies in Europe and Asia from 2007 to 2020.The study used a two-step differenced GMM estimator for the available data set spanning 2007 to 2020.The study found that rising demand for nuclear power helps to achieve a carbon-neutral agenda,but insufficient funding for renewable energy leads to higher carbon emissions.The research suggests increasing investment in nuclear energy and green financing can improve regional environmental quality.The study found a causal link between fuel imports,nuclear power and regional growth.It also determined that fuel imports,chemical use,green financing and the need for nuclear energy will likely impact regional environmental quality.The research recommends allocating more resources toward innovation to boost energy efficiency and expanding investment in renewable and nuclear energy production industries via green finance.The study also highlights the need to encourage the development of renewable energy sources to cut carbon emissions and establish a sustainable society.

Efficient Estimation and Variable Selection in Dynamic Panel Data Partially Linear Varying Coefficient Models with Incidental Parameter

This paper is concerned with the statistical inference of partially linear varying coefficient dynamic panel data model with incidental parameter, including efficient estimation of the parametric and nonparametric components and consistent determination of the lagged order. For the parametric component, we propose an efficient semiparametric generalized method-of-moments（GMM） estimator and establish its asymptotic normality. For the nonparametric component, B-spline series approximation is employed to estimate the unknown coefficient functions, which are shown to achieve the optimal nonparametric convergence rate. A consistent estimator of the variance of error component is also constructed. In addition, by using the smooth-threshold GMM estimating equations, we propose a variable selection method to identify the significant order of lagged terms automatically and remove the irrelevant regressors by setting their coefficient to zeros. As a result, it can consistently determine the true lagged order and specify the significant exogenous variables. Further studies show that the resulting estimator has the same asymptotic properties as if the true lagged order and significant regressors were known prior, i.e., achieving the oracle property. Numerical experiments are conducted to evaluate the finite sample performance of our procedures. An example of application is also illustrated.

The impact of internet finance on commercial banks’risk-taking:Theoretical interpretation and empirical test

Through introducing internet finance’s“reducing management cost”and“raising capital cost”effects into bank risk-taking model,this paper systematically investigates the dynamic and heterogeneous influence of internet finance on commercial banks’risk-taking.Using internet finance index based on“text mining”and data of 36 commercial banks from 2003 to 2013,we makes an empirical test.The results show,firstly,the impact of internet finance on commercial banks’risk-taking is a“U”trend.The initial development of internet finance can help commercial banks reduce management cost and risk-taking,but then internet finance will raise capital cost,and turn to exacerbate banks’risk-taking.Secondly,the response of commercial banks’risk-taking is heterogeneous.Large commercial banks’response is slow,while small and medium banks’response is relatively sensitive.