农地发展权流出、资本回流与区域经济增长*——基于重庆市的实证研究

[目的]以重庆市为研究区域,以农地发展权资本化的地票指标为研究对象,从理论和实证层面探讨农地发展权交易与资本要素的城乡互动关系及其对区域经济增长的影响,为进一步优化农地发展权交易政策、促进区域均衡发展提供相关建议。[方法]基于2008—2017年城乡统筹改革试验区重庆市地票指标交易的面板数据,运用要素禀赋理论和差分GMM模型从理论和实证层面分析农地发展权流出区土地与资本要素的互动关系及其对区域经济增长的影响。[结果]基于区域间资源禀赋的差异及要素相对价格的分化,在市场机制作用下,农地发展权指标将以配置效率优化为导向在不同区域间自由流动。[结论]短期内,由于资本约束,流出农地发展权的区域其经济增长会受到抑制;而当农地发展权指标资本化后,流出区获得了较为稀缺的货币资本,农地发展权流出将显著促进地方经济增长;同时,在研究时期内,由于农地发展权市场交易机制的构建,重庆市地方经济增长存在显著的收敛效应,区域间经济发展差距将会逐步缩小。     

Testing vertical price transmission for Vietnam's Robusta coffee

Vietnam has undergone market reforms over the last three decades; and as a consequence, the coffee sector has become increasingly market‐driven. The success of the government's liberalisation policies in terms of market efficiency is investigated by examining the transmission of both positive and negative price changes for Robusta coffee between export and farmgate prices. We used a threshold vector error correction model and high‐frequency daily data. The primary result here is that of a symmetric price transmission between export and farm‐level prices. This result holds when tested with weekly price data, derived from the daily data. Farmgate prices respond faster to decreases than increases in export prices when the long‐run deviation exceeds a certain threshold. These price changes are transmitted within several days. This research also confirms the importance of transaction costs, and other price frictions mostly ignored in prior analyses for coffee.     

Asymmetric effects of exchange rate changes on the demand for money in Africa

In order to account for currency substitution, the exchange rate is included in the demand for money. More recent studies have demonstrated that exchange rate changes could have asymmetric effects on the demand for money or domestic currency. In this paper, we consider the experiences of 18 African countries and show that in most countries, indeed exchange rate changes have short-run asymmetric effects on the demand for money. However, short-run effects translate to long-run asymmetric effects only in a limited number of African countries.     

一种基于预测值量测转换的卡尔曼滤波跟踪算法

在雷达目标跟踪中，系统量测信息通常在球坐标系下获得。为了采用经典卡尔曼滤波算法实现有效目标跟踪，通常采用量测转换方法将非线性量测信息转换到直角坐标系中。针对传统量测转换方法基于量测值计算转换误差统计特性而导致的估计结果有偏问题，提出了一种基于预测值的量测转换方法，并将其与卡尔曼滤波算法相结合，获得了一种基于预测值量测转换的卡尔曼滤波跟踪算法。仿真结果表明，与现有的基于量测转换的卡尔曼滤波算法相比，该算法能在不提高运算量的情况下有效改善目标跟踪效果，跟踪精度提升约20%。     

Are they telling the truth? Revealing hidden traits of satisfaction with a public bike-sharing service

Public bike-sharing systems (BSSs) are an emerging mode of transportation introduced by municipalities to solve congestion problems in metropolitan areas, especially when integrated with other types of transportation. In the last years, the number of public bike-sharing services has been constantly on the rise all over the world, and generally the overall satisfaction with them is high. However, satisfaction with public services is driven by mechanisms that can differ from those in the private sector. It is important to establish to what extent a high satisfaction is genuine or simply ephemeral. Even “old” public services (like public transportation) become “gold” when accompanied by the introduction of new technologies. In this paper we analyze this phenomenon using data from a satisfaction web-survey conducted among customers of the public BSS “BikeMi” in Milan, Italy, in a period when mobile technologies have been introduced to speed up the service. On analyzing the responses to satisfaction questions using simple summary statistics, the level of satisfaction resulted very high. However, our aim was to look for potential “darker” sides of the service by detecting possible hidden satisfaction components. For this purpose, we used the Nonlinear Principal Components Analysis, which is particularly powerful in this context. A simple textual analysis was also performed as a validating test. Results from our analysis indicated that satisfaction is flawed by a set of factors like the mechanics of the bikes, the picking and dropping system, and the apps used to organize the service. Less concern was detected for more general aspects of the service.     

Nonlinearities in the real exchange rates: new evidence from developed and developing countries

This paper investigates nonlinearities in the dynamics of real exchange rates. We use Monte Carlo simulations to establish the size properties of the Teräsvirta-Anderson test, when the dynamics of the real exchange rate is influenced by an exogenous process. In addition, we show that a modified nonlinearity test, which includes additional right-hand-side variables, performs much better than the original in both Monte Carlo exercises and in the actual data on 1431 bilateral real exchange rate series. Finally, we investigate the dynamics of the real exchange rate for both developed and developing countries using the modified test for the recent floating period. In general, the results find a greater incidence of nonlinear dynamics for developing country real exchange rates.     

Is health care a necessity or a luxury? Evidence from urban China

This article uses a smooth transition regression (STR) model to research the income elasticity of the health care expenditure of China’s urban residents in the 1990–2013 period. The results demonstrate that if the real income of China’s urban residents that lags a period is taken as a transition variable, urban residents’ health expenditure follows an LSTR1 nonlinear two-regime model. Here, the income elasticity of health care expenditure of China’s urban residents is 1.4919 in 1990–2002 and 1.2216 in 2003–2013. Overall, the income elasticity of health care of China’s urban residents is greater than 1, indicating that health care is a luxury.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

Tracing VARDI coefficients: a proposal

ABSTRACT

We propose a new approach for tracing the so-called ‘value-added-(re)distribution-important coefficients’ (in short the VARDI coefficients) in a world input–output model. From the perspective of a selected group of economies, VARDI coefficients may be defined as those elements in world input–output matrix in the case of which a small change in their levels leads to the maximization of a share of this group of economies in value added in global value chains. Due to the rapid development of the World Input Output Database, this approach may be easily applied in empirical research to different groups of countries and sectors in world IO models. In an illustrative empirical case study, we use the new approach in order to answer a question regarding what the main directions of the future macroeconomic policy of the U.S. could be in order to ensure the maximization of the country’s share in global value added.