Nonlinear deterministic forecasting of daily dollar exchange rates

We perform out-of-sample predictions on several dollar exchange rate returns by using time-delay embedding techniques and a local linear predictor. We compared our predictions with those by a mean value predictor. Some of our predictions of the exchange rate returns outperform the predictions of the same series by the mean value predictor. However, these improvements were not statistically significant. Another interesting result in this paper which was obtained by using a recently developed technique of nonlinear dynamics is that all exchange rate return series we tested have a very high embedding dimension. Additionally, evidence indicates that these series are likely generated by high dimensional systems with measurement noise or by high dimensional nonlinear stochastic systems, that is, nonlinear deterministic systems with dynamic noise.     

沪深300股指期货与现货市场的非线性信息溢出与价格发现功能研究

采用线性与非线性Granger因果检验、协整检验和VECM模型,研究了沪深300股指期货和现货市场的线性与非线性信息溢出,并检验了期货市场的价格发现功能发挥情况。研究结果显示:线性信息溢出方面,沪深300股指期货市场对现货市场只有线性均值信息溢出,现货市场对期货市场只存在线性方差信息溢出;非线性信息溢出方面,两个市场之间不存在非线性均值信息溢出,不过二者之间存在显著的非线性方差信息溢出;沪深300股指期、现货市场之间存在着长期均衡的关系,不过不同于成熟市场中期货市场在价格发现方面居于主导地位的结论,我国股指现货市场在价格发现方面占主导地位,而期货市场处于从属地位。     

Recent developments of the autoregressive distributed lag modelling framework

We review the literature on the autoregressive distributed lag (ARDL) model, from its origins in the analysis of autocorrelated trend stationary processes to its subsequent applications in the analysis of cointegrated non-stationary time series. We then survey several recent extensions of the ARDL model, including asymmetric and non-linear generalisations of the ARDL model, the quantile ARDL model, the pooled mean group dynamic panel data model and the spatio-temporal ARDL model.     

Coping with drought with innovative pricing systems: the case of two irrigation water management companies in France

In recent years, France has been facing particularly severe drought periods especially in summer. In a country where agriculture is the largest water user, some irrigation management companies have implemented innovative pricing systems to handle this situation. The objective of this article is to analyze the impact of these new management systems on farmers’ incomes, the revenue of the management companies, and the amount of water used. To do that, we develop a methodology using a stochastic model that simulates the representative farmer's optimal behavior in a context of climatic variability. An empirical application is made with a crop growth model and data collected from the Midi‐Pyrenees region. The results show that using these specific nonlinear pricing systems allow irrigation water managers to reduce the impact of drought on production. Moreover, the type of pricing implemented depends on the characteristics and capabilities of the water company. More particularly, these pricing systems can be used to anticipate agricultural water demand in order to avoid imbalance with water availability.     

Moment-based estimation of smooth transition regression models with endogenous variables

Nonlinear regression models have been widely used in practice for a variety of time series and cross-section datasets. For purposes of analyzing univariate and multivariate time series data, in particular, smooth transition regression (STR) models have been shown to be very useful for representing and capturing asymmetric behavior. Most STR models have been applied to univariate processes, and have made a variety of assumptions, including stationary or cointegrated processes, uncorrelated, homoskedastic or conditionally heteroskedastic errors, and weakly exogenous regressors. Under the assumption of exogeneity, the standard method of estimation is nonlinear least squares. The primary purpose of this paper is to relax the assumption of weakly exogenous regressors and to discuss moment-based methods for estimating STR models. The paper analyzes the properties of the STR model with endogenous variables by providing a diagnostic test of linearity of the underlying process under endogeneity, developing an estimation procedure and a misspecification test for the STR model, presenting the results of Monte Carlo simulations to show the usefulness of the model and estimation method, and providing an empirical application for inflation rate targeting in Brazil. We show that STR models with endogenous variables can be specified and estimated by a straightforward application of existing results in the literature.     

Optimizing the credit term decisions in supply chain finance

We study a credit term determination problem in the context of a supplier-buyer supply chain. The supplier's credit term decision is simultaneously made with its production and inventory decisions, and most importantly, it is impacted by the buyer's order quantity. We present a new game-theoretic framework to model this problem, which captures the interaction between the supplier's credit term decision and the buyer's order decision in a multi-period setting. An exact method based on nonlinear programming is implemented to obtain the optimal solutions. We apply our methodologies on a real world case. The computational results show that our approach significantly outperforms the heuristics with fixed credit terms, and either a short or a long credit term can be sub-optimal for the supplier in profitability. Our work offers the first data-driven model and solution approach that assists purchasing and supply managers to make optimal dynamic credit term decision in conjunction with production, ordering and inventory decisions in a game-theoretic setting.     

Impact of Financial Development on the Environmental Quality in Iran

In recent decades, undesirable environmental changes, such as global warming and greenhouse gases emission, have raised worldwide concerns. In order to achieve higher growth rate, environmental problems emerged from economic activities have turned into a controversial issue. The aim of this study is to investigate the effect of financial development on environmental quality in Iran. For this purpose, the statistical data over the period from 1970 to 2011 were used. Also by using the Auto Regression Model Distributed Lag （ARDL）, short-term and long-term relationships among the variables of model were estimated and analyzed. The results show that financial development accelerates the degradation of the environment; however, the increase in trade openness reduces the damage to environment in Iran. Error correction coefficient shows that in each period, 53% of imbalances would be justified and will approach their long-run procedure. Structural stability tests show that the estimated coefficients were stable over the period.     

The relationship between interest rate volatility and the shadow economy in OECD countries: An asymmetric analysis

Interest rate changes by central banks are a strong monetary policy tool that has a significant impact on the performance of the real economy via various channels. Despite extensive theoretical and empirical studies in this area, the current literature lacks a comprehensive assessment of the relationship between interest rate volatility and the shadow economy. This study explores the link between interest rate volatility and the shadow economy for 38 Organisation for Economic Co-operation and Development (OECD) member countries over the period 1991–2021 using both linear and non-linear ARDL models. The use of the non-linear ARDL specification will allow for the possibility of an asymmetric effect of interest rate volatility on the shadow economy. In addition to the examination of the potential asymmetric effects, we also discuss the ramifications for policymakers with respect to monetary and financial policies while considering each country's specific economic structure.     

Determinants in the forecast of the gross national income of China and India from 1952 to 2015

In 2015, China and India's population represented approximately 35.74% of the total number of people living in the world. Due to the historical context and behavior of the most relevant indicators, this study proposes to utilize a wide variety of demographic, economic, and production indicators from 1952 to 2015 to assess their impact on the GNI in China and India. A comprehensive and new fangled modeling process with stepwise, regularization and distributed lag regression approaches is presented. Accordingly, theoretical results were corroborated through extensive diagnostic tests and an empirical check of the models' predictive capacity. The findings show that GNI in China is most influenced by variables such as reserves in foreign currency and the dependency ratio; whereas, variables of energy production and birth rate were generated for India. Therefore, it's the timing for China to relax the universal two-child policy. Due to the current value below the substitution rate, a gloomy outlook for China's future population and economy is predicted. Conversely, a positive outlook is forecasted for India, given the low price in the future of oil- India's primary raw material.