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Given that the use of Google Trends data is helpful to improve forecasting performance, this study aims to investigate whether the precision of forecast combination can benefit from the use of Google Trends Web search index along with the encompassing set.

Grey prediction models generate single-model forecasts, while Google Trends index serves as an explanatory variable for multivariate models. Then, three combination sets, including sets of univariate models (CUGM), all constituents (CAGM) and constituents that survive the forecast encompassing tests (CSET), are generated. Finally, commonly used combination methods combine the individual forecasts for each combination set.

The tourism volumes of four frequently searched-for cities in Taiwan are used to evaluate the accuracy of three combination sets. The encompassing tests show that multivariate grey models play a role to be reckoned with in forecast combinations. Furthermore, the empirical results indicate the usefulness of Google Trends index and encompassing tests for linear combination methods because linear combination methods coupled with CSET outperformed that coupled with CAGM and CUGM.

With Google Trends Web search index, the tourism sector may benefit from the use of linear combinations of constituents that survive encompassing tests to formulate business strategies for tourist destinations. A good forecasting practice by estimating ex ante forecasts post-COVID-19 can be further provided by scenario forecasting.

To improve the accuracy of combination forecasting, this research verifies the correlation between Google Trends index and combined forecasts in tourism along with encompassing tests.

過去的研究顯示 Google 搜尋趨勢資料有助於改善旅遊需求預測的準確度，本研究就此進一步探討 Google 搜尋趨勢網頁搜尋指標與涵蓋性檢定的使用對於組合預測準確度所造成的影響。

本研究以 Google 搜尋趨勢指標做為多變量灰色預測模式的解釋變數，並以單變量與多變量灰色模式產生各別預測值。在分別產生由所有的單變量模式 (CUGM)所有的模式 (CAGM), 以及經過涵蓋性檢定所留存下來之模式 (CSET) 所組成之集合後，就各別的組合集以常用的組合方法產生預測值。

以台灣的四個熱搜旅遊城市的旅遊人數進行三個組合集的預測準確度分析。涵蓋性檢定顯示多變量灰色模式在組合預測中扮演重要的角色，而結果亦呈現線性組合方法在 CSET優於在 CUGM 與 CAGM 的準確度，突顯搜尋趨勢指標與涵蓋性檢定對於線性組合方法的有用性。

藉由 Google 搜尋趨勢網頁搜尋指標與涵蓋性檢定，旅遊部門應可透過線性組合方法的預測規劃旅遊目的地的經營策略。新冠疫情下於各季的事前預測亦可結合情境預測具體呈現。

為提升組合預測在旅遊需求的預測準確度，本研究結合涵蓋性檢定以分析 Google 搜尋趨勢指標與組合預測準確度之間的關聯性。

旅遊需求,涵蓋性檢定,Google 搜尋趨勢,灰色預測,組合預測

研究型论文

Dado que el uso de los datos de Google Trends es útil para mejorar la precisión de las predicciones, este estudio examina si el uso del índice de búsqueda web de Google Trends combinado con la agregación de relevancia puede mejorar la precisión del predictor.

El modelo predictivo gris genera predicciones bajo un único modelo, mientras que el modelomultivariado utiliza el indicador Google Trends como variable explicativa. Se generaron tresensamblajes generales, incluido el Modelo armónico único (CUGM), los ensamblajes de todos loscomponentes (CAGM) y la prueba de presencia de componentes con predicción (CSET). Laspredicciones individuales encada grupo luego se combinan utilizando métodos de correlación deuso común.

Utilizando el número de turistas en las cuatro ciudades más investigadas de Taiwán, los tresgrupos combinados se clasificaron según su precisión. Las pruebas incluidas muestran que losmodelos multivariados en escala de grises son importantes para la predicción. Además, losresultados de las pruebas muestran que el índice de Google Trends y las pruebas que incluyenmétodos de suma lineal son útiles porque los métodos combinados con CSET funcionan majorque los métodos combinados con CSET. CAGM y VCUG.

La industria de viajes puede usar el índice de búsqueda web de Google Trends para desarrollarestrategias comerciales para atracciones basadas en un conjunto lineal de componentes.

Con el objetivo de mejorar la precisión de los pronósticos agregados, este estudio investiga larelación entre el índice de tendencias de Google y las expectativas generales de viaje junto con laevidencia global.

Demanda de viajes, Experiencia global, Tendencias de Google, Predicción gris

Trabajo de investigación

While numerous empirical studies have tried to model and forecast the oil price volatility over the years, such attempts using the crude oil volatility index (OVX) rarely exist. In order to conceal this void, the purpose of this paper is to investigate whether including OVX in the realized volatility (RV) models improve the accuracy of predictions.

At the empirical stage, the authors employ several measures to frame the RV of crude oil futures returns. In particular, the authors use three different range-based RV estimators recommended by Parkinson (1980), Rogers and Satchell (1991) and Alizadeh et al. (2002), respectively.

The findings reveal that the information content of crude OVX helps to provide more accurate volatility predictions in comparison to the base-line RV model which contains only historical oil volatilities. Besides, the forecast encompassing test further suggests that the modified RV model (when OVX is introduced in the base-line RV model) forecast encompasses the conventional RV forecast in majority of the cases.

Since forecasting oil price volatility plays a vital role in portfolio optimization, derivatives pricing, optimum asset allocation decisions and risk management, the findings of this study thus carry important implications for energy economists, investors and policymakers.

This paper adds to the existing literature, since it is one of the initial studies to explore whether OVX is informative about the realized variance of the US oil market returns. The findings recommend that the information content of oil implied volatilities should be taken into account when modeling the US oil market volatility. In addition, range-based measures should be utilized while estimating the RV.

Develops a method of forecasting foreign exchange rate by normal mixture model (NMM). Initially establishes a set of exchange rate models and switches from one model to another probabilistically, depending on supply shocks or government policy changes. By assuming that the population distribution of foreign exchange rate is a mixture of normal distributions, these models can then be estimated simultaneously. Uses the estimated parameters of the model to forecast foreign exchange rate, and then four foreign exchange rate models are used to estimate the NMM. The out‐of‐sample forecasting results obtained show that we can decrease the mean squared error (MSE) of forecast error dramatically by using the NMM, compared with the MSE of the best forecast of each separate model.

This study aims to both test the asymmetric information hypothesis and explore the factors influencing the one‐ through four‐quarter‐ahead Federal Reserve inflation forecasts for 1983‐2002.

Encompassing tests are used to examine the asymmetric information hypothesis. In modeling the Federal Reserve inflation forecasts, the authors are mindful of alternative theories of inflation which emphasize such determinants as cost‐push, demand‐pull and inertial factors.

First, the Federal Reserve inflation forecasts embody useful predictive information beyond that contained in the private forecasts. Second, with the private forecasts controlled for, the near‐term Federal Reserve inflation forecasts make use of qualitative information, and the longer‐term forecasts are influenced by the forecasts of growth in both unit labor costs and aggregate demand as well as the preceding‐quarter inflation forecasts and monetary policy shifts.

The Federal Reserve forecasts are released to the public with a five‐year lag and are currently available up to the fourth quarter of 2002. This limits the use of the most up‐to‐date forecasts desirable for this study.

The factors influencing the Federal Reserve inflation forecasts are basically those emphasized publicly by monetary authorities. This finding points to the Fed's transparency and should thus help enhance its credibility with the public. Also, our results (which shed light on the predictive information in the Federal Reserve inflation forecasts not included in the private forecasts) are of value, since they can help the Fed better predict how inflation will respond to policy actions, and they can help the public form more informative inflationary expectations.

The purpose of this paper is to show that intentional demand forecast bias can improve warehouse capacity planning and labour efficiency. It presents an empirical methodology to detect and implement forecast bias.

A forecast model integrates historical demand information and expert forecasts to support active bias management. A non-linear relationship between labour productivity and forecast bias is employed to optimise efficiency. The business analytic methods are illustrated by a case study in a consumer electronics warehouse, supplemented by a survey among 30 warehouses.

Results indicate that warehouse management systematically over-forecasts order sizes. The case study shows that optimal bias for picking and loading is 30-70 per cent with efficiency gains of 5-10 per cent, whereas the labour-intensive packing stage does not benefit from bias. The survey results confirm productivity effects of forecast bias.

Warehouse managers can apply the methodology in their own situation if they systematically register demand forecasts, actual order sizes and labour productivity per warehouse stage. Application is illustrated for a single warehouse, and studies for alternative product categories and labour processes are of interest.

Intentional forecast bias can lead to smoother workflows in warehouses and thus result in higher labour efficiency. Required data include historical data on demand forecasts, order sizes and labour productivity. Implementation depends on labour hiring strategies and cost structures.

Operational data support evidence-based warehouse labour management. The case study validates earlier conceptual studies based on artificial data.

We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After documenting that forecast combinations provide gains in predictive accuracy and that these gains are statistically significant, we show that forecast combinations may substantially improve portfolio selection. We find that the best-performing forecast combinations are those that either avoid estimating the pooling weights or that minimize the need for estimation. In practice, we report that the best-performing combination schemes are based on the principle of relative past forecasting performance. The economic gains from combining forecasts in portfolio management applications appear to be large, stable over time, and robust to the introduction of realistic transaction costs.

This paper aims to better jointly estimate Value at Risk (VaR) and expected shortfall (ES) by using the joint regression combined forecasting (JRCF) model.

Combining different forecasting models in financial risk measurement can improve their prediction accuracy by integrating the individual models’ information. This paper applies the JRCF model to measure VaR and ES at 5%, 2.5% and 1% probability levels in the Chinese stock market. While ES is not elicitable on its own, the joint elicitability property of VaR and ES is established by the joint consistent scoring functions, which further refines the ES’s backtest. In addition, a variety of backtesting and evaluation methods are used to analyze and compare the alternative risk measurement models.

The empirical results show that the JRCF model outperforms the competing models. Based on the evaluation results of the joint scoring functions, the proposed model obtains the minimum scoring function value compared to the individual forecasting models and the average combined forecasting model overall. Moreover, Murphy diagrams’ results further reveal that this model has consistent comparative advantages among all considered models.

The JRCF model of risk measures is proposed, and the application of the joint scoring functions of VaR and ES is expanded. Additionally, this paper comprehensively backtests and evaluates the competing risk models and examines the characteristics of Chinese financial market risks.

The paper seeks to evaluate accuracy and efficiency of consensus forecasts for all property rents and total returns in the UK. The aim of the paper is to investigate whether consensus forecasts, containing a high degree of judgement, are better than forecasts produced by uncomplicated time‐series and econometric models that practitioners can easily estimate and use for forecasting.

This study estimates simple models of all property rents and returns and generates forecasts for one‐ and two‐year horizons on a rolling basis over the period 1999 to 2004. These forecasts are real time forecasts. That is they are made using information available to the analyst at the time of the forecast each year and no future knowledge is assumed. The forecasts made by these models are compared with the corresponding consensus forecasts. The comparative assessment is based on conventional tests for bias, variability and efficiency of forecasts.

If attention is focused on rents, the consensus forecast is ranked best for the one‐year horizon but it is outperformed by the regression model and a combination of the statistical models for the two‐year horizon. For the one‐year and two‐year forecasts of total returns a simple regression model with interest rates clearly improves upon the consensus forecasts. There is clear evidence that the consensus forecasts fail to incorporate the information contained in recent interest rate movements. Therefore subject to the sample period for this analysis the survey forecasts of total returns cannot be considered impartial. Analysts should include base rate information into their predictions.

This is one of the few attempts to formally evaluate consensus forecasts in the real estate field and perform a direct comparison with quantitative forecasts. It produces initial evidence suggesting that highly judgemental consensus forecasts do not necessarily outperform quantitative forecasts based on fundamentals. It prompts property forecasters and investors to engage in forecast evaluation and include missing information and past errors in their forecasts.

Circular economy (CE) initiatives are taking hold across both developed and developing nations. Central to these initiatives is the reconfiguration of core supply chain management (SCM) processes that underlie current production and consumption patterns. This conceptual article provides a detailed discussion of how supply chain processes can support the successful implementation of CE. The article highlights areas of convergence in hopes of sparking collaboration among scholars and practitioners in SCM, CE, and related fields.

This article adopts a theory extension approach to conceptual development that uses CE as a “method” for exploring core processes within the domain of SCM. The article offers a discussion of the ways in which the five principles of CE (closing, slowing, intensifying, narrowing, dematerialising loops) intersect with eight core SCM processes (customer relationship management, supplier relationship management, customer service management, demand management, order fulfilment, manufacturing flow management, product development and commercialization, returns management).

This article identifies specific ways in which core SCM processes can support the transition from traditional linear approaches to production and consumption to a more circular approach. This paper results in a conceptual framework and research agenda for researchers and practitioners working to adapt current supply chain processes to support the implementation of CE.

This article highlights key areas of convergence among scholars and practitioners through a systematic extension of CE principles into the domain of SCM. In so doing, the paper lays out a potential agenda for collaboration among these groups.