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The purpose of this paper is to review the current literature in the field of tourism demand forecasting.

Published papers in the high quality journals are studied and categorized based their used forecasting method.

There is no forecasting method which can develop the best forecasts for all of the problems. Combined forecasting methods are providing better forecasts in comparison to the traditional forecasting methods.

This paper reviews the available literature from 2007 to 2017. There is not such a review available in the literature.

The purpose of this report was to evaluate the effectiveness and practicality of system dynamics modeling in integrating econometric equations to describe the effects of supply chain material and information delays on pricing decisions and consequent financial results in an animal feed export business.

An empirical dynamic model, loaded with econometric theory of price effect on competitive demand, was used to describe the input data.

The model simulation outputs proved themselves relevant in analyzing the complex interconnections of multiple variables affecting the profitability in a commercial routine, supporting the decision process among sales managers. The impact of information delay on price decisions and business financial results were estimated using the model proposed.

This paper describes an empirical model, based on system dynamics, that predicts operating contribution margins and cash conversion cycles based on estimation of information and material delays in a supply chain. The method is pragmatic and simple for business routine implementation.

This paper aims to propose a hybrid-forecasting model for long-term tourism demand forecasting. As such, it attempts to model the tourism demand in the Philippines, which is a relatively underrepresented area in the literature, despite its tourism sector’s growing economic progress.

A hybrid support vector regression (SVR) – seasonal autoregressive integrated moving averages (SARIMA) model is proposed to model the seasonal, linear and nonlinear components of the tourism demand in a destination country. The paper further proposes the use of multiple criteria decision-making (MCDM) approaches in selecting the best forecasting model among a set of considered models. As such, a preference ranking organization method for enrichment of evaluations (PROMETHEE) II is used to rank the considered forecasting models.

The proposed hybrid SVR-SARIMA model is the best performing model among a set of considered models in this paper using performance criteria that evaluate the errors of magnitude, directionality and trend change, of a forecasting model. Moreover, the use of the MCDM approach is found to be a relevant and prospective approach in selecting the best forecasting model among a set of models.

The novelty of this paper lies in several aspects. First, this paper pioneers the demonstration of the SVR-SARIMA model’s capability in forecasting long-term tourism demand. Second, this paper is the first to have proposed and demonstrated the use of an MCDM approach for performing model selection in forecasting. Finally, this paper is one of the very few papers to provide lenses on the current status of Philippine tourism demand.

This study aims to predict overnight stays in Italy at tourist accommodation facilities through a nonlinear, single factor, stochastic model called CIR#. The contribution of this study is twofold: in terms of forecast accuracy and in terms of parsimony (both from the perspective of the data and the complexity of the modeling), especially when a regular pattern in the time series is disrupted. This study shows that the CIR# not only performs better than the considered baseline models but also has a much lower error than other additional models or approaches reported in the literature.

Typically, tourism demand tends to follow regular trends, such as low and high seasons on a quarterly/monthly level and weekends and holidays on a daily level. The data set consists of nights spent in Italy at tourist accommodation establishments as collected on a monthly basis by Eurostat before and during the COVID-19 pandemic breaking regular patterns.

Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. In addition, given the importance of accurate forecasts, many studies have proposed novel hybrid models or used various combinations of methods. Thus, although there are clear benefits in adopting more complex approaches, the risk is that of dealing with unwieldy models. To demonstrate how this approach can be fruitfully extended to tourism, the accuracy of the CIR# is tested by using standard metrics such as root mean squared errors, mean absolute errors, mean absolute percentage error or average relative mean squared error.

The CIR# model is notably simpler than other models found in literature and does not rely on black box techniques such as those used in neural network (NN) or data science-based models. The carried analysis suggests that the CIR# model outperforms other reference predictions in terms of statistical significance of the error.

The proposed model stands out for being a viable option to the Holt–Winters (HW) model, particularly when dealing with irregular data.

The proposed model has demonstrated superiority even when compared to other models in the literature, and it can be especially useful for tourism stakeholders when making decisions in the presence of disruptions in data patterns.

The novelty lies in the fact that the proposed model is a valid alternative to the HW, especially when the data are not regular. In addition, compared to many existing models in the literature, the CIR# model is notably simpler and more transparent, avoiding the “black box” nature of NN and data science-based models.

一般来说, 旅游需求往往遵循规律的趋势, 例如季度/月的淡季和旺季, 以及日常的周末和假期。该数据集包括欧盟统计局在打破常规模式的2019冠状病毒病大流行之前和期间每月收集的在意大利旅游住宿设施度过的夜晚。

本研究旨在通过一个名为cir#的非线性单因素随机模型来预测意大利游客住宿设施的过夜住宿情况。这项研究的贡献是双重的:在预测准确性方面和在简洁方面(从数据和建模复杂性的角度来看), 特别是当时间序列中的规则模式被打乱时。我们表明, cir#不仅比考虑的基线模型表现更好, 而且比文献中报告的其他模型或方法具有更低的误差。

当大量搜索强度指标被作为旅游需求指标时, 传统的旅游需求预测模型将面临挑战。此外, 鉴于准确预测的重要性, 许多研究提出了新的混合模型或使用各种方法的组合。因此, 尽管采用更复杂的方法有明显的好处, 但风险在于处理难使用的模型。为了证明这种方法能有效地扩展到旅游业, 使用RMSE、MAE、MAPE或AvgReIMSE等标准指标来测试cir#的准确性。

cir#模型明显比文献中发现的其他模型简单, 并且不依赖于黑盒技术, 例如在神经网络或基于数据科学的模型中使用的技术。所进行的分析表明, cir#模型在误差的统计显著性方面优于其他参考预测。

这个模型作为Holt-Winters模型的一个拟议模型, 特别是在处理不规则数据时。

即使与文献中的其他模型相比, 所提出的模型也显示出优越性, 并且在数据模式中断时对旅游利益相关者做出决策特别有用。

创新之处在于所提出的模型是Holt-Winters模型的有效替代方案, 特别是当数据不规律时。此外, 与文献中的许多现有模型相比, cir#模型明显更简单、更透明, 避免了神经网络和基于数据科学的模型的“黑箱”性质。

Normalmente, la demanda turística tiende a seguir tendencias regulares, como temporadas altas y bajas a nivel trimestral/mensual y fines de semana y festivos a nivel diario. El conjunto de datos consiste en las pernoctaciones en Italia en establecimientos de alojamiento turístico recogidas mensualmente por Eurostat antes y durante la pandemia de COVID-19, rompiendo los patrones regulares.

El presente estudio pretende predecir las pernoctaciones en Italia en establecimientos de alojamiento turístico mediante un modelo estocástico no lineal de un solo factor denominado CIR#. La contribución de este estudio es doble: en términos de precisión de la predicción y en términos de parsimonia (tanto desde la perspectiva de los datos como de la complejidad de la modelización), especialmente cuando un patrón regular en la serie temporal se ve interrumpido. Demostramos que el CIR# no sólo aplica mejor que los modelos de referencia considerados, sino que también tiene un error mucho menor que otros modelos o enfoques adicionales de los que se informa en la literatura.

Los modelos tradicionales de previsión de la demanda turística pueden enfrentarse a desafíos cuando se adoptan cantidades masivas de índices de intensidad de búsqueda como indicadores de la demanda turística. Además, dada la importancia de unas previsiones precisas, muchos estudios han propuesto modelos híbridos novedosos o han utilizado diversas combinaciones de métodos. Así pues, aunque la adopción de enfoques más complejos presenta ventajas evidentes, el riesgo es el de enfrentarse a modelos poco manejables. Para demostrar cómo este enfoque puede extenderse de forma fructífera al turismo, se comprueba la precisión del CIR# utilizando métricas estándar como RMSE, MAE, MAPE o AvgReIMSE.

El modelo CIR# es notablemente más sencillo que otros modelos encontrados en la literatura y no se basa en técnicas de caja negra como las utilizadas en los modelos basados en redes neuronales o en la ciencia de datos. El análisis realizado sugiere que el modelo CIR# supera a otras predicciones de referencia en términos de significación estadística del error.

El modelo propuesto destaca por ser una opción viable al modelo Holt-Winters, sobre todo cuando se trata de datos irregulares.

El modelo propuesto ha demostrado su superioridad incluso cuando se compara con otros modelos de la bibliografía, y puede ser especialmente útil para los agentes del sector turístico a la hora de tomar decisiones cuando se producen alteraciones en los patrones de datos.

La novedad radica en que el modelo propuesto es una alternativa válida al Holt-Winters especialmente cuando los datos no son regulares. Además, en comparación con muchos modelos existentes en la literatura, el modelo CIR# es notablemente más sencillo y transparente, evitando la naturaleza de “caja negra” de los modelos basados en redes neuronales y en ciencia de datos.

The purpose of the paper is to test and analyze the equilibrium economic relationships of the East Africa Community (EAC).

To attain the study's purpose the authors applied the Johansen cointegration test, including long-run structural modeling (LRSM), vector-error-correlation-model (VECM) and variance-decomposition (VDC).

At I(1), both Philips‐Peron (PP) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests show that the East Africa member states' economies are cointegrated. The result was further substantiated by the tests based on Johansen cointegration and VECM procedures, showing significant long-run and short-run economic relations. The result further reveals that despite some uncommon issues among member states such as Tanzania and Kenya, however, their economic relationships remain significant though it is negative. Moreover, the finding revealed positive and significant short-run economic relationships between Kenya, Burundi and Rwanda.

The paper applies the cointegration techniques in the context of EAC. The result is likely to be adding value to the policymaker and also to the existing literature on the subject. This may trigger policy implications and open new research direction within the region and out.

In the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis.

All tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other.

Machine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities.

AVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical.

According to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained.

This paper aims to investigate the reciprocal nonlinear relationship between corporate social responsibility (CSR) and firm performance (FP).

The authors used a sample of 814 European firms over the period 2008–2017. The Panel Smooth Transition Regression (PSTR) model was performed as an econometric approach.

Firstly, results show a threshold effect in the CSR–FP relationships within the two directions. More specifically, the authors found that firms are more likely to engage in CSR by surpassing a threshold of 1.231% for return on assets (ROA) and 0.821% for Tobin’s Q ratio. Secondly, the authors also found that the impact of CSR on FP is positive and significant only if the environment, social and governance score surpasses the threshold of 56.780% when the dependent variable is ROA and 41.02% when Tobin’s Q ratio measures performance.

A significant part of the literature supports the linear relationship between CSR and FP from the unique direction (CSR → FP). This study comes to fill this gap by assessing the possible nonlinear relationship. In addition, this nonlinear relationship is tested under the two directions. Therefore, defining the threshold of FP that allows companies to engage in CSR, on the one hand, and the threshold of engagement in CSR that improves FP, on the other hand, could be an exciting topic.

To get the full benefit from CSR effects, firms should be with better financial performance to be socially responsible.

To the best of our knowledge, few studies have explored the nonlinear relationship between CSR and FP. In addition, this study raises the question of whether this relation is causal. The authors assess the two nonlinear relationships between CSR ? FP and FP ? CSR by determining the optimal thresholds.

本文旨在探究企業社會責任 (以下簡稱企社責) 與公司業績之間的相互非線 性關係。

研究所採用的樣本為814間歐洲公司, 涵蓋期為2008年至2017年。研究人 員使用縱橫平滑轉換模型、作為經濟計量方法和工具去進行研究。

研究結果顯示、在有關的兩個方向內, 企社責與公司業績之間的關聯上是 存在著閾值效應的。更具體地說, 研究人員發現, 若企業的資產報酬率超過1.231%的 水平, 以及托賓的Q比率 (Tobin’s Q Ratio) 0.821%的水平的話, 它們會更願意承擔企 社責。其次, 研究結果亦顯示, 企社責對企業的業績會產生積極的影響; 另外, 只有 當資產報酬率是因變數、而環境、社會和公司治理的分數 (ESGS) 超過56.780%, 以 及當托賓的Q比率用來測量績效、而數值為41.02%時, 企社責對企業的業績所產生的 影響會較為顯著。

過去的學術文獻、大部份都是以唯一的方向 (企社責 ->公司業績) 去確認 企社責與企業業績之間的線性關係。本研究評估了兩者之間可能存在的非線性關係; 而且, 這非線性關係是在有關的兩個方向下而進行測試的; 因此, 本研究一方面給可 讓公司以企社責的精神和理念去營運的企業業績的閾值下了定義; 另一方面, 又給參 與企社責為公司帶來業績的改善的閾值下了定義。這均為令人興奮的課題。

企業若想取得因參與企社責而帶來的完全好處, 它們必須擁有更佳 的財務績效、以能盡其社會責任。

盡我們所知, 探究企社責與企業業績之間的非線性關係的研究實在不 多; 而且, 本研究對這兩者的關係是否是因果關係提出了質疑; 就此, 我們藉著釐定 最佳的相對閾值、來評估企社責 ->企業業績與企業業績 ->企社責之間的兩個非線性的 關係。

This research paper aims to handle the effects of economic growth, corruption, energy consumption as well as trade openness on CO2 emissions for a sample of West African countries during the period 1980 and 2018.

The current work uses the pooled mean group (PMG)-autoregressive distributed lag (ARDL) panel model to estimate the dynamics among the different variables used in the short and long terms.

The findings demonstrate that all variables have long-term effects. These results suggest that gross domestic product (GDP) per capita exhibits a positive and prominent effect on CO2 emissions. Corruption displays a negative and outstanding effect on long-term CO2 emissions. In contrast, energy consumption in West African countries and trade openness create environmental degradation. Contrarily to long-term results, short-term results demonstrate that economic growth, corruption and trade openness do not influence the environmental quality.

Empirical findings provide useful information to explore deeper and better the link between the used variables. They stand for a theoretical basis as well as an enlightening guideline for policymakers to set strategies founded on the analyzed links.

The purpose of this paper is to summarize different methods of constructing the financial conditions index (FCI) and analyze current studies on constructing FCI for China. Due to shifts of China’s financial mechanisms in the post-crisis era, conventional ways of FCI construction have their limitations.

The paper suggests improvements in two aspects, i.e. using time-varying weights and introducing non-financial variables. In the empirical study, the author first develops an FCI with fixed weights for comparison, constructs a post-crisis FCI based on time-varying parameter vector autoregressive model and finally examines the FCI with time-varying weights concerning its explanatory and predictive power for inflation.

Results suggest that the FCI with time-varying weights performs better than one with fixed weights and the former better reflects China’s financial conditions. Furthermore, introduction of credit availability improves the FCI.

FCI constructed in this paper goes ahead of inflation by about 11 months, and it has strong explanatory and predictive power for inflation. Constructing an appropriate FCI is important for improving the effectiveness and predictive power of the post-crisis monetary policy and foe achieving both economic and financial stability.

This paper aims to investigate the nonlinear effects of bank regulation stringency on bank lending in 23 sub-Saharan African (SSA) countries over the period 1997–2017.

This study employs the dynamic panel threshold regression (PTR) model, which addresses endogeneity and heterogeneity problems within a nonlinear framework. It also uses indices of entry barriers, mixing of banking and commerce restrictions, activity restrictions and capital regulatory requirements from the updated databases of the World Bank's Bank Regulation and Supervision Surveys as measures of bank regulation.

The linearity test results support the existence of nonlinear effects in the relationship between bank lending and entry barriers or capital regulations in the selected SSA economies. The dynamic PTR estimation results reveal that bank lending responds positively when the stringency of entry barriers is below the threshold of 62.8%. However, once the stringency of entry barriers exceeds that threshold level, bank credit reacts negatively and significantly. By contrast, changes in capital regulation stringency do not affect bank lending, either below or above the obtained threshold value of 76.5%.

These results can help policymakers design bank regulatory measures that will promote the resilience and safety of the banking system but at the same time not bring unintended effects to bank lending.

To the best of the authors’ knowledge, this is the first study to examine the nonlinear effects of bank regulatory measures on bank lending using the dynamic PTR model and SSA context.