This research applies a data-driven approach for identifying changes in time series to model the Olympic tourism legacy. Measuring potential legacy effects from mega-sporting events has been problematic in prior research. Issues revolve around how to measure tourism, how to control for pre-existing trends, and how to account for extraneous events affecting tourism unrelated to the Olympics. Most problematic for analytical models is determining when the tourism legacy begins and what is the functional form of the tourism legacy. Many of these issues interact and can confound results leading to erroneous conclusions. The seminal methodology developed by Tsay [(1988). Outliers, level shifts, and variance changes in time series. Journal of Forecasting, 7(1), 1-20] requires no prior assumption about the timing or functional forms of the outliers, therefore solving these issues and provides a framework that can be used when analysing mega-sporting event legacies. Using this methodology, the research finds limited support for a short-term Olympic tourism legacy and no support for a long-term tourism legacy.

The extended producer responsibility system and government intervention are among the essential requirements for constructing sustainable economies. In a closed-loop supply chain consisting of an original equipment manufacturer (OEM) and a third-party remanufacturer (TPR), this paper develops four Stackelberg game models for four modes of government intervention under patent licensing: no intervention, tax and subsidy, tax and subsidy plus eco-design tax deduction, and eco-design tax deduction only. The impacts of the different government interventions on the optimal production decision, social welfare, and environment are analyzed and compared. The results indicate that, compared with no government intervention, government tax and subsidy decrease the supply of new products, increase supply of remanufactured products, and raise recycling rates of end-of-life products; on top of tax and subsidy, the government’s eco-design tax deduction can further increase OEM and TPR profits; the environmental impact under different government intervention depends on the ratio of the environmental impacts of remanufactured to new products and the level of eco-design effort. This study provides decision makers (i.e., OEM and TPR) and policy makers (i.e., governments) with new managerial insights on the benefits of eco-design and effective mechanisms to promote it.

The logistics and manufacturing industries’ co-agglomeration (LMCA) and deep integration, as well as the industries’ digital transformation and intelligent upgrading, are of great significance to enhance regional economic resilience (EcoResi). This paper establishes a theoretical framework for LMCA and EcoResi based on the economic development theory and the new economic geography theory, explores the spatial spillover effect of LMCA on EcoResi, and measures the levels of LMCA and EcoResi. The data set is consisted of the indicators of LMCA and GDP growth rate of 30 provinces, centrally administered municipalities, and autonomous regions in China from 2006 to 2020. Spatial econometric models were used to empirically analyze the impact of LMCA on EcoResi based on provincial panel data. The results show that the improvement in LMCA not only improves the resilience of local economy, but it also has a significant spatial spillover effect. Further regional analyses show that LMCA has significant stimulating effects and spatial spillover effects on EcoResi in the central and western regions of China. However, the same effects are not significant in the eastern region of China. This research enriches the literature by suggesting effective ways to enhance EcoResi through LMCA.

The outbreak of COVID-19 epidemic has had an unprecedented impact on global economy, the challenges caused by similar external shocks can be effectively addressed by enhancing regional economic resilience (EcoResile). Data of 30 provinces in China from 2006 to 2020 are used to construct a nonlinear panel model to explore the impact of the level of co-agglomeration between logistics and manufacturing industry (CoAgg) on EcoResile. Robustness tests are conducted using instrumental variable regression. The main conclusions are as follows: (1) The effect of CoAgg on EcoResile is non-linear and shows a U-shaped relationship; (2) The effect of CoAgg is not uniform across the regions of China, it is more significant in the eastern and western regions of China.

In this paper a semi-parametric approach is developed to model non-linear relationships in time series data using polynomial splines. Polynomial splines require very little assumption about the functional form of the underlying relationship, so they are very flexible and can be used to model highly non-linear relationships. Polynomial splines are also computationally very efficient. The serial correlation in the data is accounted for by modelling the noise as an autoregressive integrated moving average (ARIMA) process, by doing so, the efficiency in nonparametric estimation is improved and correct inferences can be obtained. The explicit structure of the ARIMA model allows the correlation information to be used to improve forecasting performance. An algorithm is developed to automatically select and estimate the polynomial spline model and the ARIMA model through backfitting. This method is applied on a real-life data set to forecast hourly electricity usage. The non-linear effect of temperature on hourly electricity usage is allowed to be different at different hours of the day and days of the week. The forecasting performance of the developed method is evaluated in post-sample forecasting and compared with several well-accepted models. The results show the performance of the proposed model is comparable with a long short-term memory deep learning model.

Cooperating in low-carbon linkage development is an inevitable choice for manufacturing and logistics enterprises in emerging economies and government plays an important role in the cooperation. This paper constructs a three-party evolutionary game theory model to study the behavior of the government, manufacturing, and logistics enterprises in such cooperation. Based on the game income matrix of strategy combinations, replicated dynamic equations are established and used to investigate the equilibrium state of the game; the local stability of the equilibrium state in various scenarios is analyzed using Jacobian matrix and stability theory, 3 D spatial replicated phase diagrams are used to show the strategy choice trends of participants. This paper also summarises the rules of game behavior under different income parameters. We found that additional developmental cost in low-carbon linkage is a key factors that directly affects the game results, and government plays an important role in the development: in the early stage of development when the investment is high, the government can promote cooperation by regulations or financial incentives. The findings are corroborated in numerical simulations. This paper enriches the literature on factors that affect decision-making in low-carbon linkage development, and provides useful insights to improve government intervention to promote a low-carbon economy.

The expectation–maximization (EM) algorithm is a seminal method to calculate the maximum likelihood estimators (MLEs) for incomplete data. However, one drawback of this algorithm is that the asymptotic variance–covariance matrix of the MLE is not automatically produced. Although there are several methods proposed to resolve this drawback, limitations exist for these methods. In this paper, we propose an innovative interpolation procedure to directly estimate the asymptotic variance–covariance matrix of the MLE obtained by the EM algorithm. Specifically we make use of the cubic spline interpolation to approximate the first-order and the second-order derivative functions in the Jacobian and Hessian matrices from the EM algorithm. It does not require iterative procedures as in other previously proposed numerical methods, so it is computationally efficient and direct. We derive the truncation error bounds of the functions theoretically and show that the truncation error diminishes to zero as the mesh size approaches zero. The optimal mesh size is derived as well by minimizing the global error. The accuracy and the complexity of the novel method is compared with those of the well-known SEM method. Two numerical examples and a real data are used to illustrate the accuracy and stability of this novel method.

This study examined millennial consumers’ relationships between status consumption and Sproles and Kendall’s (1986) Consumer Styles Inventory (CSI). It was found that status consumption was a positive antecedent to five of the eight CSI’s shopping style characteristics: brand conscious, novelty and fashion conscious, recreational and shopping conscious, impulsive/careless, and habitual/brand loyal, but not to the characteristics of perfectionist, confused by overchoice, and price conscious. The results suggest that those millennial consumers who are motivated to consume for status will utilize the shopping styles of being brand conscious, novelty/fashion conscious, recreational shoppers, impulsive shoppers, and brand loyal.

In this paper a class of nonparametric transfer function models is proposed to model nonlinear relationships between ‘input’ and ‘output’ time series. The transfer function is smooth with unknown functional forms, and the noise is assumed to be a stationary autoregressive-moving average (ARMA) process. The nonparametric transfer function is estimated jointly with the ARMA parameters. By modeling the correlation in the noise, the transfer function can be estimated more efficiently. The parsimonious ARMA structure improves the estimation efficiency in finite samples. The asymptotic properties of the estimators are investigated. The finite-sample properties are illustrated through simulations and one empirical example.

The focus of this paper is using nonparametric transfer function models in forecasting. Nonparametric smoothing methods are used to model the relationship between variables (the transfer function) and the noise is modeled as an Autoregressive Moving Average (ARMA) process. The transfer function is estimated jointly with the ARMA parameters. Nonparametric smoothing methods are flexible thus can be used to model highly nonlinear relationships between variables. In this paper polynomial splines are used to model the transfer function. Modeling noise term as an ARMA process removes the serial correlation so the transfer function can be estimated efficiently. As a result, the nonparametric transfer function model can generate accurate forecasts when the transfer function is highly nonlinear with unknown functional form. The proposed polynomial splines-based estimator is also highly computationally efficient. The performance of nonparametric transfer function models is demonstrated in this paper by forecasting river flow based on temperature and precipitation. A comparison of the results show that the performance of this model is better than some widely accepted benchmark models. Key–Words:Nonparametric smoothing, Time series, Forecast

In this paper we use the polynomial splines-based nonparametric transfer function method to study how river flow is affected by multiple factors. The highly nonlinear relationship between river flow and the independent variables (the transfer function) is modeled using polynomial spline, and the noise term assumed to follow a parametric Autoregressive (AR) model. The transfer function is modeled jointly with the AR parameters. Because of its flexibility, spline functions are ideal for modeling highly nonlinear relationships with unknown functional forms; by modeling the noise explicitly, the correlation in the data is removed so the transfer function can be estimated more efficiently. Additionally, the estimated AR parameters can be used to improve the forecasting performance. The proposed polynomial splines-based estimator is also highly computationally efficient. A comparison of the results show that the performance of this model is better than some widely accepted benchmark models.

In order to make full use of semi-worsted process to develop textile products suitable for market demand,this paper particularly introduced the feature,development of technology,as well as technological process and equipment of the semi-worsted process.Technique points and product information of materials selection and process design in semi-worsted production were summarized.The quality and equipment problem of hair-slip and pilling,color difference and color particle were concluded,and the solution was provided.According to the semi-worsted production condition,this paper pointed out that the lightweight,leisure,and functional textiles could be produced by adopting more modern equipment and methods,breaking the tradition professional restrictions,and establishing the concept of large textile.

In this paper we develop a semi-parametric approach to model nonlinear relationships in serially correlated data. To illustrate the usefulness of this approach, we apply it to a set of hourly electricity load data. This approach takes into consideration the effect of temperature combined with those of time-of-day and type-of-day via nonparametric estimation. In addition, an ARIMA model is used to model the serial correlation in the data. An iterative backfitting algorithm is used to estimate the model. Post-sample forecasting performance is evaluated and comparative results are presented. Copyright © 2006 John Wiley & Sons, Ltd.