The effects of government expenditure on imports in the Eurozone reconsidered: evidence from panel data

This study examines the impact of government expenditure on imports. The empirical analysis is based on annual data of the euro area countries for the period 1995–2015. We employ econometric methods that mitigate heterogeneity and cross-sectional dependence. We provide empirical indications that the components of expenditure have different impact on imports demand. The findings of this study indicate that the import context of government expenditure is lower than the import context of others expenditure components. Finally, we find that an increase in government expenditure leads to an increase in imports; this implies that, ceteris paribus that it can lead to a deterioration of the trade balance.     

Neutral agents and social tolerance: a dynamic perspective based on economic interaction model with local social cost functions

The influence of neutral agents on the evolutionary dynamics of social tolerance is discussed based on a recently proposed economic interaction model with local social cost functions. We show that the dynamical structure of social tolerance is completely changed even if a few neutral agents are introduced into the society. Especially, the full intolerance steady state, which is stable in previous works, becomes unstable and avoidable due to the economic incentive introduced by the neutral agents. The necessary condition of achieving full tolerance is also reduced compared to previous works without neutral agents.     

Investigating properties of commodity price responses to real and nominal shocks

This paper studies dynamic adjustments of 49 world commodity prices in response to innovations in the nominal exchange rate and the world real GDP. After we estimate the dynamic elasticity of the prices with respect to these shocks, we obtain the kernel density of our estimates to establish stylized facts on the adjustment process of the commodity price toward a new equilibrium path. Our empirical findings imply, on average, that the law of one price holds in the long-run, whereas the substantial degree of short-run price rigidity was observed in response to the nominal exchange rate shock. The real GDP shock tends to generate substantial price fluctuations in the short-run because adjustments of the supply can be limited, but have much weaker effects in the long-run as the supply eventually counterbalances the increase in the demand. Overall, we report persistent long-lasting effects of the nominal exchange rate shock on commodity prices relative to those of the real GDP shock.     

知识交流视角下孵化网络动态演化与层级关联研究——以T孵化器网络为例

基于知识交流视角，构建孵化载体、在孵企业、服务机构分层嵌入的孵化网络模型框架，通过调研数据分析以T孵化器为核心节点的孵化网络拓扑结构动态演化过程，探究孵化网络不同层级之间知识交流相互关联的特点。结果表明，T孵化器网络密度呈指数下降，平均度稳中有升、增幅缓慢，平均路径长度和网络中心势呈对数增长，平均聚类系数在高值区间缓慢下降至稳定状态；T孵化器网络在演化过程中表现出自组织、无标度和小世界的复杂网络特性；“T孵化器-在孵企业”网络与“在孵企业-服务机构”网络之间、“T孵化器-服务机构”网络与“在孵企业-服务机构”网络之间的知识交流频次显著正相关。     

A secure hash function based on feedback iterative structure

Rapid growth of internet data has created enormous security challenges on authenticity, availability and integrity protection of these outsourced data. Hash function is one of the main solutions to face these challenges. This paper proposes a feedback iterative structure of hash function which utilises the variable feedback to resist attacks. Furthermore, to accelerate message diffusion, two novel modules are designed, one for iteration and the other for truncation. Experimental results show that the proposed hash function can effectively resist existing attacks. Moreover, comparing with other existing hash functions, it displays better on statistical performance, collision resistance and avalanche.     

Compound trend renewal process with discounted claims: a unified approach

We derive recursive formulas for the moments of compound trend renewal sums with discounted claims. An integral expression for the moment generating function of this risk process is then obtained, from which particular distribution functions are found. We extend the compound (deterministic) trend renewal process by assuming a stochastic trend, a stochastic force of net interest and a stochastic dependence between the inter-occurrence times and the severities of the claims. Finally, stochastic dominance ordering is also observed between the compound trend renewal process and an associated non-homogeneous Poisson process.     

WHAT AFFECTS EFFICIENCY IN LABOUR MARKET MATCHING AT DIFFERENT TERRITORIAL AGGREGATION LEVELS IN POLAND?

We analyse efficiency of a labour market matching process at different levels of territorial aggregation and in different time horizons. We apply a stochastic matching frontier to random, job queuing and stock‐flow models. We use data for Poland, a country with highly regionally diversified unemployment. We find that search and matching mechanisms significantly differ with territorial aggregation. Higher efficiency in small regions results from lower frictions. Larger regions enjoy better structural matching if only labour market participants have time to search. Different policy measures should be designed to improve labour market matching efficiency at different regional levels.     

INTERACTION TERMS IN POISSON AND LOG LINEAR REGRESSION MODELS

This paper develops a difference‐in‐semielasticities (DIS) interpretation for the coefficients of dichotomous variable interaction terms in nonlinear models with exponential conditional mean functions, including but not limited to Poisson, Negative Binomial, and log linear models. We show why these interaction term coefficients cannot be interpreted as a DIS or semielasticity in the same manner as continuous coefficients, which has been overlooked by some empirical researchers. Then we show how interaction terms can be easily transformed into a DIS and derive the asymptotic distribution of this estimator. We illustrate the discrepancy between the interaction term coefficient and the DIS using an empirical example evaluating the relationship between employment, private health insurance and physician office visits. Our results can be applied in treatment effect models when the outcome variable is logged and the dichotomous variables indicating treatment participation and the post‐treatment time period.     

SOCIAL DISCOUNTING AND THE LONG RATE OF INTEREST

The well‐known theorem of Dybvig, Ingersoll, and Ross shows that the long zero‐coupon rate can never fall. This result, which, although undoubtedly correct, has been regarded by many as surprising, stems from the implicit assumption that the long‐term discount function has an exponential tail. We revisit the problem in the setting of modern interest rate theory, and show that if the long “simple” interest rate (or Libor rate) is finite, then this rate (unlike the zero‐coupon rate) acts viably as a state variable, the value of which can fluctuate randomly in line with other economic indicators. New interest rate models are constructed, under this hypothesis and certain generalizations thereof, that illustrate explicitly the good asymptotic behavior of the resulting discount bond systems. The conditions necessary for the existence of such “hyperbolic” and “generalized hyperbolic” long rates are those of so‐called social discounting, which allow for long‐term cash flows to be treated as broadly “just as important” as those of the short or medium term. As a consequence, we are able to provide a consistent arbitrage‐free valuation framework for the cost‐benefit analysis and risk management of long‐term social projects, such as those associated with sustainable energy, resource conservation, and climate change.     

Granger causality detection in high-dimensional systems using feedforward neural networks

This paper proposes a novel methodology to detect Granger causality on average in vector autoregressive settings using feedforward neural networks. The approach accommodates unknown dependence structures between elements of high-dimensional multivariate time series with weak and strong persistence. To do this, we propose a two-stage procedure: first, we maximize the transfer of information between input and output variables in the network in order to obtain an optimal number of nodes in the intermediate hidden layers. Second, we apply a novel sparse double group lasso penalty function in order to identify the variables that have the predictive ability and, hence, indicate that Granger causality is present in the others. The penalty function inducing sparsity is applied to the weights that characterize the nodes of the neural network. We show the correct identification of these weights so as to increase sample sizes. We apply this method to the recently created Tobalaba network of renewable energy companies and show the increase in connectivity between companies after the creation of the network using Granger causality measures to map the connections.