低温条件下SBR法污水处理技术

寒冷地区的低温条件影响了SBR法处理废水的处理效果,近年来通过修正和校核常温条件下的SBR的设计参数,保证了SBR运行稳定、微生物活性良好、出水水质达标,优化了系统的性能。低温条件下SBR法污水处理技术在处理城市污水方面研究较多,而在处理难降解有机废水方面仍待进一步探索。     

Consumer Preferences and Willingness to Pay for Bundled Fresh Produce Claims at Farmers’ Markets

This study uses a conditional logit with systematically varying parameters model to analyze consumer survey data collected at farmers’ markets in Utah. The purpose of this study is to examine consumption preferences and willingness to pay for differentiated fresh produce among farmers’ market shoppers in Utah. Three primary differentiating claims, namely conventionally grown of unknown origin, conventionally grown local (in-state), and organically grown of unknown origin, are investigated. Results indicate that consumer preferences and willingness to pay are higher for the conventionally grown local origin claim, emphasizing the importance of local origin labeling for directed marketed fresh produce. This claim was especially preferred among frequent farmers’ market shoppers and those who spend above average. Those who receive WIC and/or food stamp benefits show lower preferences for fresh produce. The shoppers are significantly sensitive to price increases. This study implies that marketing strategies and policy regulations aiming at promoting local, organic, labeling, and reinforcing repeated farmers’ market customers can positively impact purchasing preferences and willingness to pay for production-method-based and origin labeled products.     

Automatic variance ratio test under conditional heteroskedasticity

An extensive Monte Carlo experiment is conducted to evaluate small sample properties of the automatic variance ratio test under conditional heteroskedasticity. It is found that the test shows serious size distortion in small samples. For improved small sample performance, this paper proposes the use of wild bootstrap. When wild bootstrapped, the automatic variance ratio test shows no size distortion, and it has power substantially higher than its competitors such as the Chen–Deo test and wild bootstrap Chow–Denning test.     

灰色组合预测模型优化及科技人才需求预测

科技人才需求预测是国家合理制订人才政策的重要依据。为此,本文基于科技人才需求的数据特征,构建适用于科技人才需求预测的新型离散灰色模型FODGM(r,1,kθ,u),该模型实现了系统发展灰信息非线性规律的较好反映以及累加阶数作用范围全实域拓展,缓解了原始序列中极值对模型性能的影响,能够有效模拟科技人才需求的发展趋势与演变规律。应用该模型对我国科技人才需求量进行预测,结果显示未来我国科技人才需求量呈逐步上升趋势,预计2026年我国科技人员全时当量将达729.258万人年,科技人才需求端压力较大。相关部门可以根据预测结果制定缓解我国科技人才需求端压力的对策。     

The time varying effect of oil price shocks on euro-area exports

In this paper we provide novel evidence on changes in the relationship between the real price of oil and real exports in the euro area. By combining robust predictions on the sign of the impulse responses obtained from a theoretical model with restrictions on the slope of the oil demand and oil supply curves, we identify oil supply and foreign productivity shocks in a time varying VAR with stochastic volatility. We find that from the 1980s onwards the relationship between oil prices and euro area exports has become less negative conditional on oil supply shortfalls and more positive conditional on foreign productivity shocks. Using the theoretical model we show that our empirical findings can be accounted for by (i) stronger trade relationship between the euro area and emerging economies (ii) a decrease in the share of oil in production and (iii) increased competitive pressures in the product market.     

Pseudo maximum likelihood estimation of spatial autoregressive models with increasing dimension

Pseudo maximum likelihood estimates are developed for higher-order spatial autoregressive models with increasingly many parameters, including models with spatial lags in the dependent variables both with and without a linear or nonlinear regression component, and regression models with spatial autoregressive disturbances. Consistency and asymptotic normality of the estimates are established. Monte Carlo experiments examine finite-sample behaviour.     

New perspectives on forecasting inflation in emerging market economies: An empirical assessment

We use a broad-range set of inflation models and pseudo out-of-sample forecasts to assess their predictive ability among 14 emerging market economies (EMEs) at different horizons (1–12 quarters ahead) with quarterly data over the period 1980Q1-2016Q4. We find, in general, that a simple arithmetic average of the current and three previous observations (the RW-AO model) consistently outperforms its standard competitors—based on the root mean squared prediction error (RMSPE) and on the accuracy in predicting the direction of change. These include conventional models based on domestic factors, existing open-economy Phillips curve-based specifications, factor-augmented models, and time-varying parameter models. Often, the RMSPE and directional accuracy gains of the RW-AO model are shown to be statistically significant. Our results are robust to forecast combinations, intercept corrections, alternative transformations of the target variable, different lag structures, and additional tests of (conditional) predictability. We argue that the RW-AO model is successful among EMEs because it is a straightforward method to downweight later data, which is a useful strategy when there are unknown structural breaks and model misspecification.     

Default Patterns in Seven EU Countries: A Random Forest Approach

This study uses the relatively new “random forest” (RF) approach, which is based on decision-tree analysis by combining the results of a large set of decision trees. RFs have so far been little used for default prediction but offer an interesting alternative to well-established default prediction techniques. Based on accounting data from 945,062 observed European firms from seven countries in 2010 and 1,019,312 firms in 2011, we provide evidence on the country-specific default patterns. Because of the strong imbalance of the data sets with regard to the solvency status, standard RF implementations have to be modified to allow the estimation of realistic default propensities. We find that by far most accurate out-of-sample default propensities can be obtained for Italy followed by Portugal and Spain and the least accurate for the UK and Finland. The debt ratio, rate of return on sales, dynamic gearing ratio, and the rate of return on assets are found to be the most important variables for default prediction. The variable importance rankings are rather country specific, pointing to heterogeneity in the default patterns across the countries studied.     

The effect of idiosyncratic price movements on short- and long-run performance of hotels

It is a common belief that businesses performance should not be evaluated by immediate fiscal returns, but rather based on an extended time horizon. While the literature implies that pricing decisions may exert lagged as well as contemporaneous effects on performance, a limited number of empirical studies have focused on such effects. The current study investigates effects of idiosyncratic price movements on short-run and long-run hotel performance, where idiosyncratic price movements refer to the changes in individual hotels’ room rates unexplained by price competition, product differentiation, and market conditions. By analyzing spatial panel data from the Houston lodging market between 2005 and 2014, we find that idiosyncratic price movements enhance hotel performance in the short-run and that adverse effects followed in the long-run. Findings of the study and implications for practitioners are discussed along with suggestions for future research.     

How does electronic trading affect efficiency of stock market and conditional volatility? Evidence from Toronto Stock Exchange

The present paper investigates informational efficiency and changes in conditional volatility of the TSX before and after the implementation of an automated trading system on April 23, 1997. Using a battery of unit root, stationarity, as well as linear tests, we find that the introduction of electronic trading led to an increase in linearity dependence in TSX daily returns. In addition, when we examined the nonlinearity dependences using powerful econometric tests, we find that electronic trading has increased nonlinear dependencies in return series, which is the main cause of rejecting the Random Walk Hypothesis (RWH). Our results suggest that the automated trading system has negatively affected informational efficiency of the TSX. We also find evidence of long memory following automation which suggests that the introduction of electronic trading has increased the level of persistence of information and trading shocks.