Predictors of export performance in developing economies: a longitudinal analysis of Moroccan SMEs

Purpose: To help increase understanding of the export performance, we investigate the influence of firm size, research and development (R&D), advertising expenditures and network relationships, and develop a conceptual model to increase export intensity. Design/methodology/approach: The research involved a four-year longitudinal study of export performance of 168 exporting SMEs in Morocco (672 observations). The two-stage least square estimation (G2SLS) was used to test the hypotheses. Findings: The findings of the two-stage least square estimation (G2SLS) suggest that export sales and domestic sales are interdependent and influence each other. R&D expenditures and network affiliation have positive and significant impact on export sales, while advertising expenditure has a negative and significant influence on export sales. Research limitations/implications: Readers should use caution in generalizing the findings unless verified in other developing contexts. Practical implications: The findings of this study are useful for managers and export policy makers in developing countries. A proper understanding of the predictors of export performance may help them encourage the growth and diversification of exports that is so vital for developing countries. Originality/value: Export performance is a key element in the field of international marketing. Unfortunately, the literature exhibits a lack of empirical studies in developing countries, particularly in Maghreb regions. The empirical testing of the theoretical model in a developing economy is a significant contribution to fill the needed gap in the literature toward generalizing findings.     

一种改进的CPFSK信号频率估计方法

针对突发信号解调中多普勒频偏大的问题，提出了一种改进的连续相位移频键控信号(CPFSK)频率估计方法。首先接收信号平方运算使信号的调制指数加倍，再通过一次离散傅里叶变换(DFT)，用搜索信号平方谱谱峰的方法实现突发信号频率估计。仿真实验表明，在低信噪比条件下，该算法的频率估计精度比经典的相位差频率估计算法提高了20%；该算法与广泛用于高动态突发信号的频率插值估计算法相比，同道干扰信道下信干比改善可达4 dB，DFT长度缩短约1/3。     

Do housing prices promote total factor productivity? Evidence from spatial panel data models in explaining the mediating role of population density

The issue of what to promote in total factor productivity (TFP) in urban areas has been widely discussed in academia and housing prices and population density are confirmed to be two of the most essential driving factors. However, research into the interaction of housing prices and population density with TFP has been neglected, with no previous studies taking spatial factors into consideration, which may bias the results. From this perspective, using spatial panel data models and employing instrumental variables to solve the endogenous problem, this study examines the impact of housing prices on TFP through the mediating effect of population density for 283 Chinese cities during the period 2000–2013, and confirms that the mediating effect accounts for 18.70 % of the total effect. The results show the positive and significant association of housing prices with TFP and the inverted U-shape of population density. The underlying logic is that housing prices change population density by attracting people with high purchasing power and discouraging those unable to afford housing, whereas increased density helps to promote productivity since the settled inhabitants always have highly developed work skills and are well educated. The influencing mechanism of housing prices on TFP through population density is analyzed, namely the spillover effect. We find that the spillover effect exists in the eastern and central regions, as well as first, second, and third tier cities, while for western regions and fifth tier cities, population mobility and increased in housing prices slows their economic development. There is no evidence of any spillover effect in fourth tier cities. A discussion and suggested policy implications are also provided.     

Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High-Dimension Regime

In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high-dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real-data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression.     

Public spending,public governance and economic growth at the Vietnamese provincial level: A disaggregate analysis

This study examines the role of economic governance in the relationship between public spending, private investment, and economic growth in Vietnam at the provincial level. The study data consist of sixty-two Vietnamese provinces for the period 2006–2015. Some notable results are attained by applying a sequential (two-stage) estimation. First, the marginal benefits to economic growth of increased Vietnamese provincial government expenditures may be constrained because of the inefficiency of expenditures on education, business services, and public administration. Second, public spending and private investment are found to be substitutes at the provincial level. Third, based on the Provincial Competitiveness Index (PCI) ranking, used as a proxy for provincial public governance in Vietnam, the significance of public governance can be observed. The study concludes that good governance, characterized by different attributes, such as lower informal charges, greater transparency, and unbiased policy, plays a critical role in improving the impact of government expenditure on economic growth in Vietnamese provinces, particularly through its interactions with private sector investment.     

全球价值链下中国增加值贸易的核算及网络特征研究

基于投入产出模型和社会网络分析，利用世界投入产出表相关数据，对2000—2014年中国的增加值贸易进行核算，并分析全球增加值贸易的网络特征。研究结果显示：（1）从出口目的地来看，中国向美国、日本、德国等国家的增加值出口较大；（2）从行业来看，中国的纺织业、除汽车和摩托车之外的其他产品批发业、采矿业等行业的增加值出口较大；（3）根据网络密度的计算结果，世界范围的各国（地区）贸易联系程度在增强；（4）中国的相对点度数和点强度有上升的趋势，而日本和美国有下降的趋势，说明中国在世界增加值贸易格局中的地位在提升，不过美国仍在世界增加值贸易格局中占据主导地位；（5）核心边缘分析结果表明核心国家（地区）的数目经历了先增加后减少的过程，边缘国家（地区）数目则先减少后增加。其中，中国的核心度一直在增加，日本和美国的核心度呈现下降的趋势。因此，为了扩大增加值贸易，增强国际贸易的话语权，中国政府有必要采取调整进出口税率等政策，并重视与美国等国家的双边贸易合作，以实现双赢的结果。     

Arrow–Debreu equilibria for rank‐dependent utilities with heterogeneous probability weighting

We study Arrow–Debreu equilibria for a one‐period‐two‐date pure exchange economy with rank‐dependent utility agents having heterogeneous probability weighting and outcome utility functions. In particular, we allow the economy to have a mix of expected utility agents and rank‐dependent utility ones, with nonconvex probability weighting functions. The standard approach for convex economy equilibria fails due to the incompatibility with second‐order stochastic dominance. The representative agent approach devised in Xia and Zhou (2016) does not work either due to the heterogeneity of the weighting functions. We overcome these difficulties by considering the comonotone allocations, on which the rank‐dependent utilities become concave. Accordingly, we introduce the notion of comonotone Pareto optima, and derive their characterizing conditions. With the aid of the auxiliary problem of price equilibria with transfers, we provide a sufficient condition in terms of the model primitives under which an Arrow–Debreu equilibrium exists, along with the explicit expression of the state‐price density in equilibrium. This new, general sufficient condition distinguishes the paper from previous related studies with homogeneous and/or convex probability weightings.     

一种基于自适应相似矩阵的谱聚类算法

为了消除在构建谱聚类算法的相似矩阵时,高斯核函数中尺度参数的波动影响,构建了一种自适应相似矩阵,并应用到谱聚类算法中。自适应相似矩阵中数据点间的距离度量采用测地距离算法,相距较近的两点间的距离近似于欧氏距离,相距较远的两点则先根据欧氏距离得到每个数据点的k个近邻点,然后累加近邻点的测地距离,由此得到每对数据点间的最短距离。两点间的局部密度用共享近邻的定义来表示,更好地刻画了数据集的本征结构。在5个人工数据集和国际通用UCI数据库中的5个真实数据集上进行实验。实验结果表明,所提算法的聚类准确率高于对比算法的准确率,对复杂分布数据有很强的自适应能力。研究成果为数据挖掘及机器学习提供了思路和方法。     

What is the major determinant of cross-border banking flows?

This study examines the major determinant of cross-border credit flows through global banks across 70 countries. Employing a Bayesian dynamic latent factor model, we decompose volatilities of banking flows into the contribution of a global common factor, regional common factor, and country-specific factor. The results indicate that the global and regional common factor explains about 40–50 percent of volatility in overall cross-border banking flows. In particular, the contribution of the global common factor increased in the 2000s. Simultaneously, main determinants are largely heterogeneous across countries: this implies that the desirable policy response to credit inflows may differ for each host country.     

Forecasting portfolio-Value-at-Risk with nonparametric lower tail dependence estimates

We propose to forecast the Value-at-Risk of bivariate portfolios using copulas which are calibrated on the basis of nonparametric sample estimates of the coefficient of lower tail dependence. We compare our proposed method to a conventional copula-GARCH model where the parameter of a Clayton copula is estimated via Canonical Maximum-Likelihood. The superiority of our proposed model is exemplified by analyzing a data sample of nine different bivariate and one nine-dimensional financial portfolio. A comparison of the out-of-sample forecasting accuracy of both models confirms that our model yields economically significantly better Value-at-Risk forecasts than the competing parametric calibration strategy.