A new perspective on the exporter productivity premium: online trade

We use a unique firm-level data set including 9000 companies from 26 European Union countries covering four different sectors to take a close look at the relationship between online exports and productivity. The online exporter productivity premium is estimated using different techniques (ordinary least squares, quantile regressions and robust estimation). Results consistently indicate that the estimated online exporter productivity premium is statistically different from zero, positive and significant from an economic point of view. European online exporters, according to these results, are approximately 2% more productive than non-online exporters. Productivity differences between firms could be related to variables that are not included in the empirical model. More research would be needed to address this issue in the future.     

改进高阶容积粒子滤波的系统状态估计算法

传统粒子滤波（PF）直接采用状态转移先验分布作为重要性密度函数来近似后验概率密度函数，使得后验概率密度函数未包含量测信息。针对此问题，提出了一种改进高阶容积粒子滤波(CPF)的系统状态估计算法。算法采用七阶正交容积卡尔曼滤波（7th-CQKF）对PF的粒子进行传递，使得先验分布更新阶段融入最新量测信息；通过7th-CQKF设计重要性密度函数，提高对状态后验概率密度的逼近程度；通过反比例函数计算粒子权重，突出大噪声粒子与小噪声粒子权重差别，提高粒子有效性。仿真结果表明，改进高阶容积粒子滤波的估计精度高于容积粒子滤波（CPF）。     

Corrigendum to “Characterization of the asymptotic distribution of semiparametric M-estimators” [J. Econometrics 159 (2) (2010) 252–266]

This note provides correction to Ichimura and Lee (2010).     

稻鳅共生种养模式试验研究

[目的]稻鳅共生是典型的稻田综合利用模式,符合生态农业发展方向。通过对稻鳅共生生态系统的生理生态学机制开展试验研究,为进一步推广稻鳅共生种养模式提供科学依据。[方法]采用田间试验方法,以水稻单作为对照,研究了不同泥鳅养殖密度下的稻鳅共生对水稻农艺性状、土壤理化性质、水稻产量构成的影响,并对稻鳅共生种养模式进行了经济效益分析。[结果]与对照处理相比,水稻长势在株高、有效分蘖率、根长等方面有一定提高。养殖田水稻株高增高了3%,有效分蘖率提高了8%~11%,根长提高了8.8%~31.3%。在土壤理化性质方面,与对照相比,土壤容重降低了7.1%~21.2%,孔隙度增加了4.1%~14.7%。实验前后养殖田内土壤有机质增加了3.5%~26.5%,对照田降低了2.5%~5.8%。土壤肥力(氮、磷、钾)减少,但减少幅度小于对照处理。稻鳅共生种养模式下水稻产量提高了5%~25%,同时稻田增收泥鳅1 725~3 375kg/hm2,净收入为1.836 0万~2.307 0万元/hm2,经济效益提高了3.65~4.84倍。稻鳅共生种养模式中泥鳅的养殖密度为30万尾/hm2时稻田的生态效益和经济效益最佳。[结论]稻鳅共生有效改善了土壤理化性质,促进了水稻的生长,提高了稻田产量和产值。     

The Local Whittle Estimator of Long-Memory Stochastic Volatility

We propose a new semiparametric estimator of the degree of persistencein volatility for long memory stochastic volatility (LMSV) models.The estimator uses the periodogram of the log squared returnsin a local Whittle criterion which explicitly accounts for thenoise term in the LMSV model. Finite-sample and asymptotic standarderrors for the estimator are provided. An extensive simulationstudy reveals that the local Whittle estimator is much lessbiased and that the finite-sample standard errors yield moreaccurate confidence intervals than the widely-used GPH estimator.The estimator is also found to be robust against possible leverageeffects. In an empirical analysis of the daily Deutsche Mark/USDollar exchange rate, the new estimator indicates stronger persistencein volatility than the GPH estimator, provided that a largenumber of frequencies is used.     

Time Inhomogeneous Multiple Volatility Modeling

Price variations at speculative markets exhibit positive autocorrelationand cross correlation. Due to large parameter spaces necessaryfor joint modeling of variances and covariances, multivariateparametric volatility models become easily intractable in practice.We propose an adaptive procedure that identifies periods ofsecond-order homogeneity for each moment in time. To overcomethe high dimensionality of the problem we transform the multivariateseries into a set of univariate processes. We discuss thoroughlythe implementation of the adaptive technique. Theoretical andMonte Carlo results are given. We provide two applications ofthe new method. For a bivariate exchange rate series we comparethe multivariate GARCH approach with our method and find thelatter to be more in line with the underlying assumption ofindependently distributed innovations. Analyzing a 23-dimensionalvector of asset returns we underscore the case for adaptivemodeling in high-dimensional systems.     

Beyond Single-Factor Affine Term Structure Models

This article proposes a new approach to testing for the hypothesisof a single priced risk factor driving the term structure ofinterest rates. The method does not rely on any parametric specificationof the state variable dynamics or the market price of risk.It simply exploits the constraint imposed by the no-arbitragecondition on instantaneous expected bond returns. In order toachieve our goal, we develop a Kolmogorov-Smirnov test and applyit to data on Treasury bills and bonds for both the United Statesand Spain. We find that the single risk factor hypothesis cannotbe rejected for either dataset.     

What Determines Real Exchange Rates? The Nordic Countries

The model derived in this paper yields testable implications concerning the long‐run co‐movements of real exchange rates, relative labor productivity, the trade balance and terms of trade. Countries with relatively higher output growth, trade deficits or improved terms of trade are found to have more appreciated real exchange rates, with the main channel of transmission working through the relative price of nontraded goods. Exogenous terms‐of‐trade shocks are found to be the most important determinant of long‐run movements in the real exchange rate for Denmark and Norway, while demand shocks account for most of the long‐run variance in the real exchange rate for Finland and Sweden.