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The purpose of this paper is to contribute to the literature on the determinant factors of government budget balance forecast errors for Eurozone countries based on four different database sources from 1998 to 2011.

Besides the analysis on quality and efficiency of government budget balance projections, panel data analysis is made from different methods taking into account economic, political, institutional and governance factors, and lagged forecast errors for estimations of budget balance forecast errors.

The results show that even with the concern and pressure due to the fiscal crisis in the Eurozone, the bias in fiscal forecasts remains.

One contribution of this paper, in comparison to other studies, is the use of longer time periods for the analysis of forecast errors as well as the employment of different data sources for detecting systematic patterns of errors, and the use of various estimation methods for the fiscal forecast error determinants, which gives insights into the reliability and robustness of results obtained in earlier studies. In particular, the introduction of variables such as fiscal council and fiscal rules allows one to check whether institutional behavior may change the effect from debt on fiscal forecast errors.

This paper aims to extend the research into company financial forecasts by modelling naïve earnings forecasts derived from normalised historic accounting data disclosed during Australian initial public offerings (IPOs). It seeks to investigate naïve forecast errors and compare them against their management forecast counterparts. It also seeks to investigate determinants of differential error behaviour.

IPOs were sampled and their prospectus forecasts, historic financial data and subsequent actual financial performance were analysed. Directional and absolute forecast error behaviour was analysed using univariate and multivariate techniques.

Systematic factors associated with error behaviour were observed across the management forecasts and the naïve forecasts, the most notable being audit quality. In certain circumstances, the naïve forecasts performed at least as well as management forecasts. In particular, forecast interval was an important discriminator for accuracy, with the superiority of management forecasts only observed for shorter forecast intervals.

The results imply a level of “disclosure management” regarding company IPO forecasts and normalised historic accounting data, with forecast overestimation and error size more extreme in the absence of higher quality third‐party monitoring services via the audit process. The results also raise questions regarding the serviceability of normalised historic financial information disclosed in prospectuses, in that many of those data do not appear to enhance the forecasting process, particularly when accompanied by published management forecasts and shorter forecast intervals.

The purpose of this study is to examine the impact of auditor type on management’s choice of forecast precision and management forecast errors, including the effects of corporate governance. The authors use a different sample and a larger period of years to determine whether prior inferences are robust across these dimensions as well as various corporate governance and other control variables.

This quasi-experimental study uses archival data in regression-based analyses.

The authors find firms with Big 5 auditors issue forecasts that have larger forecast errors are biased downward and are less precise. The inferences of this study are robust to the inclusion of corporate governance variables, along with an extensive number of control variables found important in prior studies.

While the sample and time period may be limited, the authors have no evidence this biases the results.

More stringent auditing may have an unintended consequence of reducing the informativeness of management forecasts, as managers act strategically in regards to forecast accuracy, bias and precision.

The inferences of this study indicate that while higher quality audits could constrain earnings management, higher quality audits may induce management to provide forecasts that have greater errors, may be biased and may be less informative.

The results and inferences of this study suggest that the inferences in prior studies hold across a different sample and a different time period. This is important given concerns in the academic community regarding the extent to which prior studies can be replicated.

Financial analysts are frequently viewed as information intermediaries who process and interpret firms' financial reports for other market participants. Much recent research, however, has cast doubts on analysts' ability to fully utilize the information in firms' financial reports. Using an alternative approach, this study aims to provide evidence on how sophisticated analysts are at using information in firms' financial reports.

The paper estimates different measures of firms' operational efficiency, all of which are derived from financial statement data, and compares the strength of the association between these measures and analysts' absolute forecast errors. It then compares a sophisticated frontier‐based measure of firms' operational efficiency that evaluates firms' performance relative to their competitors with three more traditional efficiency measures; specifically the return on asset (ROA) ratio, industry‐adjusted ROA, and the return on equity ratio.

The results indicate that the more sophisticated frontier‐based measure is more strongly negatively associated with analysts' absolute forecast errors than the other three measures. The results thus suggest that analysts are capable of undertaking a sophisticated analysis of the information in firms' financial reports, at least as it pertains to operational efficiency.

To the extent that analysts serve as a key group of users of financial information, these results are likely to be of interest to accounting policy makers.

The purpose of this article is twofold. First, the article examines how managers can make more effective use of sales forecasts for strategic resource allocation decisions. Second, the article identifies those research issues in forecasting that must be addressed to better understand the managerial side of forecasting. Managers can improve resource planning by understanding the limitations of forecasts. These limitations are exemplified through several strategic forecasting paradoxes that managers must recognize. The paradoxes suggested here are: first, the most important managerial decisions a company can make are based on the least accurate forecasts; second, the most useful forecast information for resource planning is the least accurate; and, third, the organizations that need the most accurate forecast have the largest forecast error. By recognizing these paradoxes managers can devote their attention to improving the use and implementation of the forecast for better resource decisions. At the same time, future research should focus on broadening the understanding of the role of forecasts in strategic decision making.

The purpose of this paper is to investigate the impact of audit quality and ownership structure on the degrees of accuracy and bias in earnings forecasts issued in initial public offering (IPO) prospectuses in a frontier market, Bangladesh.

The paper uses both univariate and multivariate tests on the sample of 75 IPOs. The paper employs the tests to see the association between the degree of forecast bias and three corporate governance variables.

The results reveal that the magnitude of earnings forecast bias is significantly explained by issuer, auditor reputation, proportions of capital raised from domestic as well as foreign investors, and whether the IPO firm is a start-up venture. Underwriter prestige, length of the issuing firms' operating history, leverage, whether the firm went public during a stock market boom, and forecast horizon do not appear to be statistically significant in explaining the degree of forecast bias.

Although auditor reputation and the proportion of equity retained by pre-IPO owners have been investigated in several studies on IPO forecast accuracy and/or bias, no study has attributed them to corporate governance as a whole by combining auditor reputation, and ownership categories held by small private investors and foreign portfolio investors.

Forecasting techniques improve supply chain resilience by ensuring that the correct parts are available when required. In addition, accurate forecasts conserve precious resources and money by avoiding new start contracts to produce unforeseen part requests, reducing labor intensive cannibalization actions and ensuring consistent transportation modality streams where changes incur cost. This study explores the effectiveness of the United States Air Force’s current flying hour-based demand forecast by comparing it with a sortie-based demand forecast to predict future spare part needs.

This study employs a correlation analysis to show that demand for reparable parts on certain aircraft has a stronger correlation to the number of sorties flown than the number of flying hours. The effect of using the number of sorties flown instead of flying hours is analyzed by employing sorties in the United States Air Force (USAF)’s current reparable parts forecasting model. A comparative analysis on D200 forecasting error is conducted across F-16 and B-52 fleets.

This study finds that the USAF could improve its reparable parts forecast, and subsequently part availability, by employing a sortie-based demand rate for particular aircraft such as the F-16. Additionally, our findings indicate that forecasts for reparable parts on aircraft with low sortie count flying profiles, such as the B-52 fleet, perform better modeling demand as a function of flying hours. Thus, evidence is provided that the Air Force should employ multiple forecasting techniques across its possessed, organically supported aircraft fleets. The improvement of the forecast and subsequent decrease in forecast error will be presented in the Results and Discussion section.

This study is limited by the data-collection environment, which is only reported on an annual basis and is limited to 14 years of historical data. Furthermore, some observations were not included because significant data entry errors resulted in unusable observations.

There are few studies addressing the time measure of USAF reparable component failures. To the best of the authors’ knowledge, there are no studies that analyze spare component demand as a function of sortie numbers and compare the results of forecasts made on a sortie-based demand signal to the current flying hour-based approach to spare parts forecasting. The sortie-based forecast is a novel methodology and is shown to outperform the current flying hour-based method for some aircraft fleets.

The purpose of this study is to select the appropriate forecasting model at the retail stage for selected vegetables on the basis of performance analysis.

Various forecasting models such as the Box–Jenkins-based auto-regressive integrated moving average model and machine learning-based algorithms such as long short-term memory (LSTM) networks, support vector regression (SVR), random forest regression, gradient boosting regression (GBR) and extreme GBR (XGBoost/XGBR) were proposed and applied (i.e. modeling, training, testing and predicting) at the retail stage for selected vegetables to forecast demand. The performance analysis (i.e. forecasting error analysis) was carried out to select the appropriate forecasting model at the retail stage for selected vegetables.

From the obtained results for a case environment, it was observed that the machine learning algorithms, namely LSTM and SVR, produced the better results in comparison with other different demand forecasting models.

The results obtained from the case environment cannot be generalized. However, it may be used for forecasting of different agriculture produces at the retail stage, capturing their demand environment.

The implementation of LSTM and SVR for the case situation at the retail stage will reduce the forecast error, daily retail inventory and fresh produce wastage and will increase the daily revenue.

The demand forecasting model selection for agriculture produce at the retail stage on the basis of performance analysis is a unique study where both traditional and non-traditional models were analyzed and compared.

Discusses the development and evaluation of a forecasting model for inventory management in an advanced technology batch production environment. Traditional forecasting and inventory management do not adequately address issues relating to a short life cycle and to non‐seasonal products with a relatively long lead time. Limited historical data (fewer than 100 observations) is also a problem in predicting short‐term dynamic or unstable time series. A Bayesian dynamic linear time series model is proposed as an alternative technique for forecasting demand in a dynamically changing environment. Provides details of the important characteristics and development process of the forecasting model. A case study is then presented to illustrate the application of the model based on data from a multinational company in Singapore. It also compares the Bayesian dynamic linear time series model with a classical forecasting model (auto‐regressive integrated moving average (ARIMA) model).

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.