Food beliefs and food supply chains: The impact of religion and religiosity in Israel

This paper demonstrates that religion and religiosity affect norms, which affect food consumption patterns and production. Heterogeneity and asymmetric information lead to multiple certification channels as well as multiple supply chains. Major supply chains may address multiple constituencies that are secular or less religious. Technological change affects norms and thus the food system. We obtain these results by analyzing the food systems for meat products in Israel where there are three religions – Jews, Muslims, and Christians – and people assign themselves three levels of religiosity – secular, conservative, and orthodox. Israel has multiple Kosher and Halal certifiers and several specialized supermarket chains for orthodox groups. Its main supermarket chains serve secular and some conservative segments. The immigration of secular Jews from Russia led to the proliferation of non-Kosher supply chains and products, and increased consumption of pork. New technologies and higher incomes led to emergence of fast food chains serving orthodox Jews that had previously tended to eat at home.     

中国大中城市商业地产开发环境评价——基于时序全局主成分分析

2007年爆发的金融危机不但改变了全球经济格局,也增加了中国房地产市场的不确定性因素。随着住宅开发的竞争越来越激烈,其利润率呈现走低趋势。越来越多的房地产开发商从住宅地产转向商业地产领域,以期获得更加丰厚的回报。选取中国35个大中城市的42个变量共计近6000个数据,利用时序全局主成分分析法对后金融危机时期的这些城市商业地产开发环境进行了系统研究,建立了比较科学的评价指标体系。     

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

We extend neural basis expansion analysis (NBEATS) to incorporate exogenous factors. The resulting method, called NBEATSx, improves on a well-performing deep learning model, extending its capabilities by including exogenous variables and allowing it to integrate multiple sources of useful information. To showcase the utility of the NBEATSx model, we conduct a comprehensive study of its application to electricity price forecasting tasks across a broad range of years and markets. We observe state-of-the-art performance, significantly improving the forecast accuracy by nearly 20% over the original NBEATS model, and by up to 5% over other well-established statistical and machine learning methods specialized for these tasks. Additionally, the proposed neural network has an interpretable configuration that can structurally decompose time series, visualizing the relative impact of trend and seasonal components and revealing the modeled processes’ interactions with exogenous factors. To assist related work, we made the code available in a dedicated repository.     

Quantile cointegrating regression

Quantile regression has important applications in risk management, portfolio optimization, and asset pricing. The current paper studies estimation, inference and financial applications of quantile regression with cointegrated time series. In addition, a new cointegration model with quantile-varying coefficients is proposed. In the proposed model, the value of cointegrating coefficients may be affected by the shocks and thus may vary over the innovation quantile. The proposed model may be viewed as a stochastic cointegration model which includes the conventional cointegration model as a special case. It also provides a useful complement to cointegration models with (G)ARCH effects. Asymptotic properties of the proposed model and limiting distribution of the cointegrating regression quantiles are derived. In the presence of endogenous regressors, fully-modified quantile regression estimators and augmented quantile cointegrating regression are proposed to remove the second order bias and nuisance parameters. Regression Wald tests are constructed based on the fully modified quantile regression estimators. An empirical application to stock index data highlights the potential of the proposed method.     

Consistent estimation of a general nonparametric regression function in time series

We propose an estimator of the conditional distribution of X_t|X_t−1,X_t−2,…, and the corresponding regression function , where the conditioning set is of infinite order. We establish consistency of our estimator under stationarity and ergodicity conditions plus a mild smoothness condition.     

The Feldstein–Horioka puzzle in Latin American and Caribbean countries: a panel cointegration analysis

In this paper, the recently developed panel unit root and the Pedroni cointegration tests are applied to empirically examine the validity of the Feldstein–Horioka puzzle (F–H puzzle) for a heterogeneous panel of 14 Latin American and five Caribbean countries over the period, 1960–2002. The findings indicate that in these countries, the long-run solvency condition is maintained. Finally, employing the Pedroni panel group FM-OLS estimator (2000, 2001), it is found that the statistically significant estimated savings-retention coefficient for the panel is relatively small indicating that the F–H Puzzle is not valid and thus implying the prevalence of a moderate degree of capital mobility.

Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction

This paper reports the results of the NN3 competition, which is a replication of the M3 competition with an extension of the competition towards neural network (NN) and computational intelligence (CI) methods, in order to assess what progress has been made in the 10 years since the M3 competition. Two masked subsets of the M3 monthly industry data, containing 111 and 11 empirical time series respectively, were chosen, controlling for multiple data conditions of time series length (short/long), data patterns (seasonal/non-seasonal) and forecasting horizons (short/medium/long). The relative forecasting accuracy was assessed using the metrics from the M3, together with later extensions of scaled measures, and non-parametric statistical tests. The NN3 competition attracted 59 submissions from NN, CI and statistics, making it the largest CI competition on time series data. Its main findings include: (a) only one NN outperformed the damped trend using the sMAPE, but more contenders outperformed the AutomatANN of the M3; (b) ensembles of CI approaches performed very well, better than combinations of statistical methods; (c) a novel, complex statistical method outperformed all statistical and CI benchmarks; and (d) for the most difficult subset of short and seasonal series, a methodology employing echo state neural networks outperformed all others. The NN3 results highlight the ability of NN to handle complex data, including short and seasonal time series, beyond prior expectations, and thus identify multiple avenues for future research.     

Solving the income fluctuation problem with unbounded rewards

This paper studies the income fluctuation problem without imposing bounds on utility, assets, income or consumption. We prove that the Coleman operator is a contraction mapping over the natural class of candidate consumption policies when endowed with a metric that evaluates consumption differences in terms of marginal utility. We show that this metric is complete, and that the fixed point of the operator coincides with the unique optimal policy. As a consequence, even in this unbounded setting, policy function iteration always converges to the optimal policy at a geometric rate.     

Optimal dividend strategies with time-inconsistent preferences

This paper studies the optimal dividend strategies of an insurance company when the manager has time-inconsistent preferences. We consider the problem for a naive manager and a sophisticated manager, and analytically derive the optimal dividend strategies when claim sizes follow an exponential distribution. Our results show that the manager with time-inconsistent preferences tends to pay out dividends earlier than her time-consistent counterpart and that the sophisticated manager is more inclined to pay out dividends than the naive manager. Furthermore, we extend these results to the case with claim sizes following a mixed exponential distribution, and provide a numerical analysis to reveal the sensitivity of the optimal dividend strategies to changes in the premium, claims and surplus volatility.     

Mean squared error of the empirical best linear unbiased predictor in an orthogonal finite discrete spectrum linear regression model

The mean squared error (MSE) of the empirical best linear unbiased predictor in an orthogonal finite discrete spectrum linear regression model is derived and a comparison with the MSE of the best linear unbiased predictor in this model is made. It is shown that under weak conditions these two mean square errors are asymptotically the same.