Big data from dynamic pricing: A smart approach to tourism demand forecasting

Suppliers of tourist services continuously generate big data on ask prices. We suggest using this information, in the form of a price index, to forecast the occupation rates for virtually any time-space frame, provided that there are a sufficient number of decision makers “sharing” their pricing strategies on the web. Our approach guarantees great transparency and replicability, as big data from OTAs do not depend on search interfaces and can facilitate intelligent interactions between the territory and its inhabitants, thus providing a starting point for a smart decision-making process. We show that it is possible to obtain a noticeable increase in the forecasting performance by including the proposed leading indicator (price index) into the set of explanatory variables, even with very simple model specifications. Our findings offer a new research direction in the field of tourism demand forecasting leveraging on big data from the supply side.     

Combination of long term and short term forecasts, with application to tourism demand forecasting

Forecast combination is a well-established and well-tested approach for improving the forecasting accuracy. One beneficial strategy is to use constituent forecasts that have diverse information. In this paper we consider the idea of diversity being accomplished by using different time aggregations. For example, we could create a yearly time series from a monthly time series and produce forecasts for both, then combine the forecasts. These forecasts would each be tracking the dynamics of different time scales, and would therefore add diverse types of information. A comparison of several forecast combination methods, performed in the context of this setup, shows that this is indeed a beneficial strategy and generally provides a forecasting performance that is better than the performances of the individual forecasts that are combined.As a case study, we consider the problem of forecasting monthly tourism numbers for inbound tourism to Egypt. Specifically, we consider 33 individual source countries, as well as the aggregate. The novel combination strategy also produces a generally improved forecasting accuracy.     

分散型供应链需求预测动力方程模型及实证分析

为提高分散供应链计划的有效性,基于微分方程理论,建立了适合于历史数据资料、具有曲线增长特征的产品需求预测动力方程模型,并进行了实证分析。     

城市物流需求供给研究

对物流需求进行分析的同时,构建了较为全面的物流需求指标体系,同时利用多种预测方法对各个指标进行了动态预测,最后建立了物流需求供给匹配模型。     

Contagion effects in ASEAN-5 exchange rates during the Covid-19 pandemic

The aim of this study is to examine the pure and fundamentals-based contagion effects in ASEAN-5 exchange rates during Covid-19 period using daily exchange rates from June 2019 to December 2020. We adopt VECM within the structural VAR framework and higher time–frequency wavelet analysis. The VECM findings show that ASEAN-5 exchange rates are cointegrated during this pandemic and should there be any disequilibrium, daily rate of adjustments in the Indonesian rupiah, Malaysian ringgit and Singapore dollar are 6.58%, 1.47% and 2.45% respectively. The wavelet power spectrum implies that Indonesia, Malaysia and Singapore experience prolonged high degree of exchange rates volatility, Thailand experiences mild volatility in the short run and high volatility in the long run and only Philippines experiences mild volatility in the short run and no heightened long run volatility. The wavelet coherence shows Indonesian rupiah reacts first to the Covid-19 shock leading to fundamentals- based contagion to Malaysia and Thailand, and temporary pure contagion based on sentimental to Philippines and Singapore. Only the Philippine peso that insulates itself from the long run shocks. These findings are important as it gives insights into the nature of contagion among ASEAN-5 exchange rates due to global shock of Covid-19 and the need for timely intervention to prevent the short run contagion turning into the long run.     

Equity market and money supply spillovers and economic growth in BRICS economies: A global vector autoregressive approach

Previous studies report that private credit as a proxy of financial development contributes to economic growth in BRICS economies. This paper employs three additional measures of financial development, namely equity market, money supply and market capitalization, and further investigates cross-country evidence on the impact of equity market and money supply spillovers on economic growth in BRICS economies. Utilizing a Global Vector Autoregressive (GVAR) framework and quarterly data from 1989Q1 and to 2012Q4 from BRICS economies, we find that equity market and money supply variables do not predict the contributions of financial development in each BRICS member in boosting economic growth in the other member countries. However, market capitalization significantly influences economic growth. These results suggest that, besides private credit, market capitalization is another key channel of promoting growth in individual economies and the region. Policy implications of the findings are discussed.     

Text-based crude oil price forecasting: A deep learning approach

This study proposes a new, novel crude oil price forecasting method based on online media text mining, with the aim of capturing the more immediate market antecedents of price fluctuations. Specifically, this is an early attempt to apply deep learning techniques to crude oil forecasting, and to extract hidden patterns within online news media using a convolutional neural network (CNN). While the news-text sentiment features and the features extracted by the CNN model reveal significant relationships with the price change, they need to be grouped according to their topics in the price forecasting in order to obtain a greater forecasting accuracy. This study further proposes a feature grouping method based on the Latent Dirichlet Allocation (LDA) topic model for distinguishing effects from various online news topics. Optimized input variable combination is constructed using lag order selection and feature selection methods. Our empirical results suggest that the proposed topic-sentiment synthesis forecasting models perform better than the older benchmark models. In addition, text features and financial features are shown to be complementary in producing more accurate crude oil price forecasts.     

房价形成机制与定量分析初探

本文探讨了房价形成机制,认为供求关系决定房价,基本面、资金面、政策面等各种影响因子通过影响有效需求和有效供给最终实现对房价趋势的作用,形成房价波动和周期规律。在此理论基础上,利用实证数据对房价定量分析模型进行研究,论述了"房价是供求比的函数"的推导过程和逻辑关系,为今后房价分析模型的研究抛砖引玉。     

建筑业与经济增长关系的实证研究——以山东省临沂市为例

建筑业是古老而又传统的产业,建筑业的发展能够带动对建筑材料、居家用品的需求,吸纳大量低层次的农村劳动力就业,故2010年的中央"一号文件"更是提出要采取有效措施推动"建材下乡"来启动内需。文章以近年来新兴起的长江以北最大的商贸城———临沂市为例,对建筑业与经济增长的关系进行了实证分析,发现在研究期间内临沂市建筑业与经济增长之间存在长期均衡的协整关系;Granger因果关系检验表明研究期间内临沂市的经济增长对建筑业的影响不明显,但建筑业对经济增长的影响显著。以此为基础,提出了相应的结论与政策建议。     

基于因果反馈分析的机场旅客吞吐量预测方法研究——以北京首都国际机场为例

为寻求一种预测精度较高的、适应我国民用机场特点的预测方法,在机场供需分析的基础上对机场建设与社会经济发展进行了因果关系分析,提出社会经济和机场供给能力对机场旅客吞吐量的影响同样重要,进而从中提取有效数据,构建BP神经网络模型,实现经济-航空运输需求的转换,从而使预测结果更加精确。通过首都国际机场实例证明,基于因果反馈分析的BP神经网络预测方法更具有独特优势,预测精度得到了大幅提高。     

House price convergence in the euro zone: A pairwise approach

The adoption of the euro common currency was expected to lead to convergence for a number of economic and financial variables across national borders, including house prices. We apply a probabilistic pairwise approach to the question of whether home values converge across eight euro zone housing markets. Contrary to previous studies, we find only marginal evidence that euro housing markets converge. Moreover, for what convergence there appears to be, there is no evidence that the adoption of the euro itself played a role in creating such convergence. Finally, Germany, the largest and most dominant economy of the currency union, is rarely found to be convergent with other nations.     

Resolving operational paradox of sustainable supply chain: A decision framework approach

This study helps to reconcile the trade-offs between sustainable supply chain management (SSCM) and operational performance. Inspired by the paradox theory, the study advocates avoiding the contradictory elements and accepting the complementary elements between SSCM and Operational Performance. Accordingly, the study identifies various SSCM and operational performance elements followed by their evaluation to understand their complementary and contradictory nature. The study is conducted in two stages. First, the study constructs a list of SSCM features through exploratory factor analysis. Second, a unique decision framework of MACBETH (Measuring Attractiveness by Categorical-Based Evaluation Technique) and TODIM (Tomada de Decisión Inerativa Multicritero) methods is used to evaluate the SSCM features based on their impact on operational performance criteria. The proposed framework is validated in the context of the Indian Automobile Industry. The study results provide an empirically validated and prioritized list of SSCM features. In the list, the top-ranked features complement the operational performance criteria. In contrast, the lower-ranked features compromise the operational criteria, at least in the short term. Thus, this study reduces the skepticism around the adoption of SSCM by focusing on the top-ranked features of the list and avoiding the lower-ranked features during the early phases of SSCM adoption. Additionally, the study provides guidance to supply chain managers on achieving sustainability in a supply chain without compromising on its traditional goals. The results are of practical importance as supply chain managers may now choose to implement specific SSCM features that exhibit minimum negative impact on operational performance. Consequently, the study encourages an assertive adoption of SSCM even in developing countries.     

Estimating the output gap in real time: A factor model approach

By using a dynamic factor model, we can substantially improve the reliability of real-time output gap estimates for the U.S. economy. First, we use a factor model to extract a series for the common component in GDP from a large panel of monthly real-time macroeconomic variables. This series is immune to revisions to the extent that revisions are due to unbiased measurement errors or idiosyncratic news. Second, our model is able to handle the unbalanced arrival of the data. This yields favorable nowcasting properties and thus starting conditions for the filtering of data into a trend and deviations from a trend. Combined with the method of augmenting data with forecasts prior to filtering, this greatly reduces the end-of-sample imprecision in the gap estimate. The increased precision has economic importance for real-time policy decisions and improves real-time inflation forecasts.     

A financial accelerator in the business sector of a macroeconometric model of a small open economy

We have incorporated a financial accelerator mechanism operating through investments in the business sector in a dynamic macroeconometric model of the Norwegian economy. In this new and amended model aggregated credit and equity prices are determined simultaneously in a system characterized by a two-directional contemporaneous causal link, which has been designed and estimated by a new procedure for simultaneous structural model design. Combined with a mechanism where credit and asset prices are mutually influenced by real investments, this creates a financial accelerator amplified by a credit-asset price spiral. Simulations illustrate how the introduction of a financial accelerator significantly reinforces and extends the economic cycles in projections and forecasts, in particular when confronted by a severe shock. Furthermore, monetary policy has a markedly stronger effect in the short and medium term, while the impact of fiscal policy is affected to a relatively small degree as it is more remotely linked to financial markets.     

专业技术人才胜任能力模型的实证研究——以胜利油田为例

运用问卷调查法对各类专业技术人才进行了问卷调查,依据因素分析得出专业技术人才胜任能力四因素模型。专业技术人才胜任能力中组织认同水平较高,而人格特质、专业知识技能、一般能力水平则相对较低,同时也发现不同职称人才胜任能力各因素水平上的差异。     

Efficiency changes in long-term care in OECD countries: A non-parametric Malmquist Index approach

Future demographic projections indicate that long-term care (LTC) demand will put significant pressures on the long-term sustainability of public finances. Efficiency gains could potentially compensate for increasing LTC expenditures. Nevertheless, among the factors that drive LTC expenditures, efficiency, or productivity discussions have received little attention. This paper provides the first analysis of the efficiency levels of LTC for a subset of OECD countries. In doing so, the study adopts a non-parametric approach and performs a Data Envelopment Analysis (DEA). The results of the DEA show that average efficiency scores have a slight tendency to increase. There was a 0.1% increase in productivity in the provision of LTC between 2009 and 2014, which mainly resulted from an increase in technological change. However, the slight increase in efficiency is insufficient to offset fiscal pressures led by the increasing number of LTC beneficiaries and amount spent on LTC. The second stage Tobit analysis results further indicate that the productivity of LTC provision does not differ across countries with different public LTC financing arrangements.     

Eco-efficiency changes of the electricity and gas sectors across 28 European countries: A value-based data envelopment analysis productivity approach

This paper assesses the relative eco-efficiency changes in the electricity and gas sector (E&G)' production and consumption chains in 28 European countries. We propose a novel robustness assessment for the productivity index, specifically adjusted to value-based data envelopment analysis. Overall, results indicate that total factor productivity gains have been mainly driven by the catch-up effect across all chains of the E&G sector. When a more demanding perspective concerning negative environmental externalities is adopted, we find that the number of European countries that achieved productivity gains across all chains decreases. Besides, results depict the existence of lower productivity gains for the direct production chain when compared with the direct and indirect supply chains of the E&G sector. Germany, Luxembourg, and Belgium were consistently viewed as innovators across all chains, according to the environmental perspective. Several Eastern Europe countries usually viewed as policy laggards that resisted adopting the ambitious European decarbonization targets, showed total factor productivity gains in the supply chain of the E&G sector under a more environmental demanding perspective. Czechia was the only country with productivity losses across all chains, due to increasing coal-fired electricity generation in the time horizon assessed. The current partial return to coal as a source of electricity, due to the geopolitical tensions between Russia and Europe, brings additional challenges to the enhancement of the eco-efficiency of the European E&G sector.     

从供需层面看中国第三方物流问题

随着中国经济的发展，提供第三方物流服务的物流公司纷纷涌现出来。同时，与第三方物流相关的一些问题也随之出现．针对这种情况．我们建立了第三方物流经济模型，从供需层面来分析中国第三方物流中存在的问题．找出问题存在的根本原因，并且对物流企业的建设提出一些建议和解决方案。