基于农村物流需求量的组合预测分析

农村物流需求量的预测对于农村物流的发展有重要意义。文章把农村消费品零售总额作为农村物流需求量预测指标．通过分析各影响因素,建立了多元回归、双指数平滑及移动平均单预测模型。根据得出的单项预测误差数据,采用折扣系数法建立组合预测模型,使得组合预测模型预测误差平方和最小。预测能力明显优于单项预测模型。     

Nonlinear regression models with single‐index heteroscedasticity

We consider nonlinear heteroscedastic single‐index models where the mean function is a parametric nonlinear model and the variance function depends on a single‐index structure. We develop an efficient estimation method for the parameters in the mean function by using the weighted least squares estimation, and we propose a “delete‐one‐component” estimator for the single‐index in the variance function based on absolute residuals. Asymptotic results of estimators are also investigated. The estimation methods for the error distribution based on the classical empirical distribution function and an empirical likelihood method are discussed. The empirical likelihood method allows for incorporation of the assumptions on the error distribution into the estimation. Simulations illustrate the results, and a real chemical data set is analyzed to demonstrate the performance of the proposed estimators.     

Treating and Pruning: New approaches to forecasting model selection and combination using prediction intervals

We propose a new way of selecting among model forms in automated exponential smoothing routines, consequently enhancing their predictive power. The procedure, here addressed as treating, operates by selectively subsetting the ensemble of competing models based on information from their prediction intervals. By the same token, we set forth a pruning strategy to improve the accuracy of both point forecasts and prediction intervals in forecast combination methods. The proposed approaches are respectively applied to automated exponential smoothing routines and Bagging algorithms, to demonstrate their potential. An empirical experiment is conducted on a wide range of series from the M-Competitions. The results attest that the proposed approaches are simple, without requiring much additional computational cost, but capable of substantially improving forecasting accuracy for both point forecasts and prediction intervals, outperforming important benchmarks and recently developed forecast combination methods.     

Change-point analysis through integer-valued autoregressive process with application to some COVID-19 data

In this article, we consider the problem of change-point analysis for the count time series data through an integer-valued autoregressive process of order 1 (INAR(1)) with time-varying covariates. These types of features we observe in many real-life scenarios especially in the COVID-19 data sets, where the number of active cases over time starts falling and then again increases. In order to capture those features, we use Poisson INAR(1) process with a time-varying smoothing covariate. By using such model, we can model both the components in the active cases at time-point t namely, (i) number of nonrecovery cases from the previous time-point and (ii) number of new cases at time-point t. We study some theoretical properties of the proposed model along with forecasting. Some simulation studies are performed to study the effectiveness of the proposed method. Finally, we analyze two COVID-19 data sets and compare our proposed model with another PINAR(1) process which has time-varying covariate but no change-point, to demonstrate the overall performance of our proposed model.     

Ethical Management,Corporate Governance,and Abnormal Accruals

Recent research has linked the reduction of abnormal accruals to corporate governance metrics. The results of these studies, however, are based on samples taken from periods prior to promulgated board independence requirements. In other words, during this time period, management not only had discretion over accounting accruals, but also significant influence over the choice of membership on the board of directors. This study suggests that ethical management practices may be a correlated omitted variable in these studies, thus resulting in causal inference problems in the previous research. We argue that, rather than the board of directors monitoring and reducing abnormal accruals as has been posited, management who was not engaging in abusive earnings management was attempting to signal the market regarding the quality of the firm’s financial information through its choice of board membership.     

The Antinomies of ‘Financial Inclusion’: Debt,Distress and the Workings of Indian Microfinance

The concept of ‘financial inclusion’ has become a central trope that legitimates a wide range of contemporary development practices. By constructing a new object of development – the ‘financially excluded’ – it facilitates the expansion of an increasingly corporatized microfinance technocracy. The present paper problematizes the underlying binaries of inclusion/exclusion and formal/informal finance upon which this narrative is based. Through an examination of the 2010 Andhra Pradesh microfinance crisis, it demonstrates key contradictions within the discourse and practices of commercial microfinance. In so doing, it demonstrates why the narrative of financial inclusion and its correlate notion of ‘consumption smoothing’ are inadequate tools with which to conceptualize the political economy of contemporary agrarian change.     

Digital Financial Inclusion and Consumption Smoothing in China

In this paper, we investigate the effect of digital financial inclusion (DFI) on household consumption smoothing in China. We use four waves of the biennial China Family Panel Studies from 2010 to 2016, during which time DFI has significantly developed alongside financial technology across China. We split household income shocks into permanent and transitory components, and evaluate if DFI may help households to buffer against these shocks. We find that households are not able to insure against permanent shocks to income, but they can smooth approximately 70 percent of transitory shocks to income. We also find that DFI has diminished households’ ability to insure against transitory income shocks. This is partly because online purchase may lead to the oversensitivity of consumption to income. In addition, we find that contrary to DFI, traditional financial sector development contributes to better household consumption smoothing against transitory income shocks.     

SARIMA damp trend grey forecasting model for airline industry

The aim of this paper is to propose a new model that improves the Damp Trend Grey Model (DTGM) with a dynamic seasonal damping factor to forecast routes passengers demand (pax) in the air transportation industry. The model is called the SARIMA Damp Trend Grey Forecasting Model (SDTGM). In the DTGM, the damp trend factor is a static smoothing factor because it does not change over time, and therefore, it cannot capture the dynamic behavior of time series data. For this reason, the modification consists in using the trend and seasonality effects of time series data to calculate a dynamic damp trend factor as time grows. The DTGM damping factor is based on the forecasted data obtained by the GM(1,1) model; otherwise, the SDTGM calculates a seasonal damping factor based on historical data using a large amount of data points for short lead-times. The SDTGM has less uncertainty than the DTGM. The simulation results show that the SDTGM captures the seasonality effect and does not allow the forecast to exponentially grow. The SDTGM forecasts more reasonable routes pax for short lead-times when having a large amount of data points than the DTGM. The United States domestic air transport market data are used to compare the performance of the DTGM against the proposed SDTGM.     

A bootstrap-based non-parametric forecast density

Interest in density forecasts (as opposed to solely modeling the conditional mean) arises from the possibility of dynamics in higher moments of a time series, as well as in forecasting the probability of future events in some applications. By combining the idea of Markov bootstrapping with that of kernel density estimation, this paper presents a simple non-parametric method for estimating out-of-sample multi-step density forecasts. The paper also considers a host of evaluation tests for examining the dynamic misspecification of estimated density forecasts by targeting autocorrelation, heteroskedasticity and neglected non-linearity. These tests are useful, as a rejection of the tests gives insight into ways to improve a particular forecasting model. In an extensive Monte Carlo analysis involving a range of commonly used linear and non-linear time series processes, the non-parametric method is shown to work reasonably well across the simulated models for a suitable choice of the bandwidth (smoothing parameter). Furthermore, an application of the method to the U.S. Industrial Production series provides multi-step density forecasts that show no sign of dynamic misspecification.