Regulation of relationships between heterogeneous farmers and an aquifer accounting for lag effects

Many environmental problems are due to damage caused by pollutants that accumulate with a time lag following their emission. In this study, we focus on nitrates used in agriculture, which can pollute groundwater many years after their initial application. A dynamic optimal control problem with heterogeneous farmers is proposed. The usual structural parameters such as the discount rate, the natural clearing rate and the lagged time interval between the occurrence of soil‐level pollution and the impact on groundwater are taken into account. We also examine pollution as caused by a continuous set of farms characterised by their individual performance index and by their individual marginal contribution to the pollution. The issue is further investigated by taking account of change in the information context, successively related to perfect information and to asymmetric information. As a result, when the delay between the spreading of N‐fertilizer and the impact on the aquifer increases, that is, the longer the lag, the steady‐state pollution stock and the steady‐state shadow price of the stock both increase. Moreover, we show that the optimal regulation may require a decreasing amount of fertilizer over time, even in the case of initial underpollution.     

The M4 Competition: Results,findings, conclusion and way forward

The M4 competition is the continuation of three previous competitions started more than 45 years ago whose purpose was to learn how to improve forecasting accuracy, and how such learning can be applied to advance the theory and practice of forecasting. The purpose of M4 was to replicate the results of the previous ones and extend them into three directions: First significantly increase the number of series, second include Machine Learning (ML) forecasting methods, and third evaluate both point forecasts and prediction intervals. The five major findings of the M4 Competitions are: 1. Out Of the 17 most accurate methods, 12 were “combinations” of mostly statistical approaches. 2. The biggest surprise was a “hybrid” approach that utilized both statistical and ML features. This method’s average sMAPE was close to 10% more accurate than the combination benchmark used to compare the submitted methods. 3. The second most accurate method was a combination of seven statistical methods and one ML one, with the weights for the averaging being calculated by a ML algorithm that was trained to minimize the forecasting. 4. The two most accurate methods also achieved an amazing success in specifying the 95% prediction intervals correctly. 5. The six pure ML methods performed poorly, with none of them being more accurate than the combination benchmark and only one being more accurate than Naïve2. This paper presents some initial results of M4, its major findings and a logical conclusion. Finally, it outlines what the authors consider to be the way forward for the field of forecasting.     

Riding the Ever-Rolling Stream: Time and the Ontology of Violent Conflict

In this paper, we examine the ways in which the passage of time is dealt with in econometric studies of violent conflict and civil war with empirical attention to the dynamics of ethnic conflict. We argue that the mainstream approach to econometric studies of civil war is based on a time-invariant ontology and that this is not an appropriate or adequate way of capturing the causal patterns of violent conflict. Based analysis of replication datasets using structural break analysis and rolling windows, we show how careful attention to the passage of time reveals important macro-historical changes in the coefficients on ethnic diversity in explaining conflict incidence. We conclude that econometric studies of civil war need to pay more careful attention to the limitations on the generalizations that they draw through attention to the passage of time and better iteration with qualitative and historical studies.     

Spiritual Functions of Leisure and Spiritual Well-Being: Coping with Time Pressure

The purpose of this study was to develop a model of leisure style and spiritual well-being relationships, and the processes (spiritual functions of leisure) by which leisure can influence spiritual well-being. Also, the role of leisure in ameliorating the effects of time pressure on spiritual well-being was examined. Structural equation modeling using AMOS was employed to test direct and indirect effects models of the relationships among components of leisure style (leisure activity participation, leisure motivation, and leisure time), spiritual functions of leisure (sacrilization, repression avoidance, sense of place) and spiritual well-being (both behavioral and subjective). The model developed suggests that some components of people's leisure styles lead to certain behaviors and experiences (spiritual functions of leisure) that maintain or enhance spiritual well-being. These spiritual functions of leisure may also serve as coping strategies to ameliorate the negative influence of time pressure on spiritual well-being.     

U.S. Virgin Islands National Park:

No abstract available for this article.     

Choice of Portfolios and Fee under Principal -Agent Framework with Uncertain Exit - Time

In the real world, an investor is usually not sure about when he or she will exit investment. She can only make estimation prior. With the growth of the scale of open - end funds which can be subscribed and redeemed at any time. Investors began to pay more attention on problems such as whether the fee is reasonable, the payment can encourage the agent or not. Others would consider if the portfolio that the agent chooses is the best one. This paper considers the principal - agent problem with uncertain exit - time under two cases： the action of the agent is observable and unobservable. We derive close - form expressions of the optimal portfolio and the best fee. Finally an empirical analysis is given based on the data of some open - end funds in China.     

WHAT IS THE EFFECT OF HOUSEWORK ON THE MARKET WAGE,AND CAN IT EXPLAIN THE GENDER WAGE GAP?

Abstract Does housework reduce the market wage, and if so, does it have a similar impact for males and females? In this paper, we survey and evaluate the recent and growing empirical literature on the linkages between housework and the wage rate. The review is motivated by unexplained gender wage gaps across studies, which consider personal and market‐related factors. We focus on this less‐studied aspect of wage determination. We consider the required modelling framework, and provide standardized estimated effects of housework on the hourly wage across studies. We evaluate how this literature has addressed potential estimation problems, in particular, the endogeneity of housework, concavity of the housework–wage function, threshold effects and work effort effects. We conclude that the evidence across ordinary least squares, instrumental variable, fixed effects and two‐stage least squares results casts serious doubt on the idea that the negative female housework–wage relationship is only driven by endogeneity bias or individual‐specific characteristics. Yet, much more needs to be done to address modelling and data requirements, and we point out likely and promising future research directions.     

A nonlinear long memory model, with an application to US unemployment

Two important empirical features of US unemployment are that shocks to the series seem rather persistent and that it seems to rise faster during recessions than that it falls during expansions. To jointly capture these features of long memory and nonlinearity, we put forward a new time series model and evaluate its empirical performance. We find that the model describes the data rather well and that it outperforms related competitive models on various measures of fit.     

论税收的时效性

时效性是税收强制性在时间上的体现，是税法赋予的一种规定性，是国家财政收支及时稳定的重要保证，是依法治税的必然要求，是衡量税务机关征管水平的基本依据。不论是税收法治的严肃性，还是国家各项事业急需财政资金的现实，都迫切需求提高税收的时效性。及时有效地组织好财政收入。     

A note on the Robust Trend and ARARMA methodologies used in the M3 Competition

This brief note describes two of the forecasting methods used in the M3 Competition, Robust Trend and ARARMA. The origins of these methods are very different. Robust Trend was introduced to model the special features of some telecommunications time series. It was subsequently found to be competitive with Holt’s linear model for the more varied set of time series used in the M1 Competition. The ARARMA methodology was proposed by Parzen as a general time series modelling procedure, and can be thought of as an alternative to the ARIMA methodology of Box and Jenkins. This method was used in the M1 Competition and achieved the lowest mean absolute percentage error for longer forecasting horizons. These methods will be described in more detail and some comments on their use in the M3 Competition conclude this note.