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The purpose of this paper is to analyze the ex ante projected future trajectories of real tourism exports and relative tourism export prices of the EU-15, conditional on expert real gross domestic product growth forecasts for the global economy provided by the Organisation for Economic Co-operation and Development for the years 2013-2017.

To this end, the global vector autoregression (GVAR) framework is applied to a comprehensive panel data set ranging from 1994Q1 to 2013Q3 for a cross-section of 45 countries. This approach allows for interdependencies between countries that are assumed to be equally affected by common global developments.

In line with economic theory, growing global tourist income combined with decreasing relative destination price ensures, in general, increasing tourism demand for the politically and macroeconomically distressed EU-15. However, the conditional forecast increases in tourism demand are under-proportional for some EU-15 member countries.

Rather than simply relying on increases in tourist income, the low price competitiveness of the EU-15 member countries should also be addressed by tourism planners and developers in order to counter the rising competition for global market shares and ensure future tourism export earnings.

One major contribution of this research is that it applies the novel GVAR framework to a research question in tourism demand analysis and forecasting. Furthermore, the analysis of the ex ante conditionally projected future trajectories of real tourism exports and relative tourism export prices of the EU-15 is a novel aspect in the tourism literature since conditional forecasting has rarely been performed in this discipline to date, in particular, in combination with ex ante forecasting.

What caused the mid-2000s world commodity price “bubble” and the recent commodity price growth? Some have suggested that rapid global industrial growth over the past decade is the key driver of price growth. Others have argued that high commodity prices are a result of excessively loose monetary policy. The purpose of this paper is to extend the current research in this area by incorporating emerging economies, the BRIC (Brazil, Russia, India, and China) nations specifically, into global measures.

The paper uses a vector error correction (VEC) model and computes variance decomposition and impulse response functions (IRFs).

The empirical analysis suggest that the “demand channel” plays a large part in explaining commodity price growth whether BRIC countries are included or excluded from the analysis. However, excess liquidity may also play a part in explaining price growth. In addition, factoring in BRIC country data leads to the conclusion that unexpected movements in liquidity eventually explain more of the variation in commodity prices than unexpected demand shocks. This specific result is not caught in the sample that only incorporates advanced economies.

Despite the theory of Frankel (1986) and the findings of previous global vector autoregression (VAR)/VEC analyses, interest rates, especially shocks, have a minimal impact on consumer and commodity prices. Perhaps future studies should include an interest rate in their analysis that more closely reflects interest rates associated with information used by commodity consumers, producers, and investors. Some analyses such as Hua (1998) use the LIBOR rate, which is highly associated with developed financial markets in the advanced economies. Data quality and availability in the BRIC countries severely limited the length of the time period analyzed and the frequency of the data. Finding longer sample periods or higher frequency data can help to minimize bias in future research. In this paper, monetary aggregates and short-term interest rates were loosely connected to monetary policy. It would also be interesting to directly examine how special programs like quantitative easing influenced global liquidity.

The results of the IRFs and variance decompositions confirm some of the previous findings reported in Belke et al. (2010), Hua (1998), and Swaray (2008) that suggest that positive shocks to liquidity positively impact commodity prices. In particular, both samples suggest that this is a short-run impact that occurs after two quarters. However, in the sample that includes information about liquidity from BRIC countries, excess liquidity positively affects commodity prices after six and seven quarters as well. The insignificant results of Granger causality tests of the effect of monetary variables on commodity prices suggests that this relationship is limited to movements in liquidity that is unexpected by agents in the system. These “shocks” could be attributed to a number of factors including exogenous monetary policy changes such as the unprecedented responses by the Federal Reserve during and after the 2008 global financial crisis.

First, empirical research that claims to analyze relationships at a “global” level needs to account for the growing influence of emerging economies and not simply the advanced economies. Otherwise, results may be biased as they were when too much of the forecast error variance in commodity prices was attributed to shocks to output when it should have been attributed to shocks to excess liquidity. Second, those who criticize expansionary monetary policy in the advanced countries, especially by the Federal Reserve, for pushing up commodity prices should also direct their attention toward monetary authorities elsewhere, especially the BRIC countries, since information on excess liquidity from these countries adds to the influence that global excess liquidity has on commodity prices. Third, monetary policymakers in the advanced countries need to closely monitor liquidity in the BRIC countries, since the discrepancies between the ALL and ADV samples suggests that BRIC excess liquidity affects commodity prices in a way that cannot be captured by examining advanced country data alone.

No other paper in this area looked at the BRIC countries.

The global financial crisis hit the economy of the Kyrgyz Republic by the third wave of its transmission in early 2009. The purpose of this study is to examine the impact of the global financial economic crisis on the transition economy of the Kyrgyz Republic. As there is a low level of the Kyrgyz Republic’s integration into the global financial and economic processes, it is obvious that channels of transmissions are different.

The empirical model is the vector autoregression approach. The quarterly data from 2005 to 2013 of the remittances from abroad, trade volumes, exchange rates, credits, deposits and liquidity of the banking system, gross domestic product (GDP) and foreign direct investment (FDI) were used in the empirical analysis.

The authors found a significant positive relation between transmission channels such as remittances flow, banking sector, international trade and GDP within the first six months. Thus, a decline in the aforementioned variables has a significant affirmative effect on the country’s GDP. Notwithstanding, the exchange-rate channel adversely influences GDP. Thereby, the depreciation of the national currency leads to an increase in GDP.

The study findings allow the Kyrgyz policymakers to foresee the global crisis transmission through the primary channels of transmission mechanism. Nevertheless, a decrease of the deposit level by 1 per cent leads to 2.91 per cent decline in FDI inflows. On the contrary, an increase of the exchange rate by 1 per cent leads to 1.54 per cent decrease in imports.

The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

A major concern for policymakers and researchers is to ascertain the movement of price levels and employment rates. Predicting the trends of these variables will assist the government in making policies to stabilize the economy. The objective of this chapter is to forecast the unemployment rate and Consumer Price Index (CPI) for the period 2022 to 2031 for the Indian economy. For this purpose, the authors analyse the prediction capability of the univariate auto-regressive integrated moving average (ARIMA) model and the vector autoregressive (VAR) model. The dataset for India's annual CPI and unemployment rate pertains to a 30-year time period from 1991 to 2021. The result shows that the inflation forecasts derived from the ARIMA model are more precise than that of the VAR model. Whereas, unemployment rate forecasts obtained from the VAR model are more reliable than that of the ARIMA model. It is also observed that predicted unemployment rates hover around 5.7% in the forthcoming years, while the forecasted inflation rate witnesses an increasing trend.

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 significant economic weight of the Eurozone in the globe caused the contagion of the Eurozone debt crisis on the emerging markets. The Eurozone debt crisis caused the sudden plummeting of the cross-border bank credit (BC) to India causing a significant impact on bank lending in India. Essentially, the purpose of this study is to find an answer to the question: Did the decline in cross-border cross-credit from Eurozone had an impact on domestic BC in India?

Using the data for the period from 2000 to 2013 sourced from Bank for International Settlements international banking statistics consolidated data sets, the novel specification of the study captures the impact of Eurozone cross-border credit on India by developing two regression frameworks that capture the pre-Euro debt crisis period scenario and post-Euro debt crisis period scenario.

The results offer a very interesting analogy of the behavior of BC and cross-border credit during the pre and post-Eurozone crisis scenarios of analysis. During the pre-Eurozone crisis period, cross-border credit displayed a significant negative relationship with BC indicating that cross-border credit to the Indian firms indirectly benefitted the banks by creating increased demand for domestic BC. The post-Eurozone crisis period witnessed a nexus between cross-border credit and BC during the pre-Eurozone crisis period, which gradually disappeared largely because of the onset of the Eurozone crisis.

This study is a first of its kind in investigating the impact of the Eurozone crisis on an emerging economy like India. This study supports the hypothesis of the existence of the transmission of financial shocks through the balance sheets of international banks. The findings conform to the policy concerns of most of the emerging economies that international banks transmit financial shocks from their home countries. The implication for India and other emerging economies is that international credit growth deserves careful monitoring.

This study aims to assess how real income, oil prices and gold prices affect housing prices in China from 2010 to 2021.

This study uses a novel bootstrap autoregressive distributed lag (ARDL) testing to empirically analyze the short and long links among the tested variables.

The ARDL estimations demonstrate a positive impact of oil price shocks and real income on housing market prices in both the phrases of the short and long run. Furthermore, the results reveal that gold price shocks negatively affect housing prices both in the short and long run. The result can be attributed to China’s housing market and advanced infrastructure, resulting in a drop in housing prices as gold prices increase. Additionally, the prediction of housing market prices will provide a base and direction for housing market investors to forecast housing prices and avoid losses.

To the best of the authors’ knowledge, this is the first attempt to analyze the effect of gold price shocks on housing market prices in China.

This paper aims to analyze the research highlights, evolutionary process and future research directions in the field of tourism forecasting.

This study used CiteSpace to conduct a bibliometric analysis of 1,213 tourism forecasting articles.

The results show that tourism forecasting research has experienced three stages. The institutional collaboration includes transnational collaboration and domestic institutional collaboration. Collaboration between countries still needs to be strengthened. The authors’ collaboration is mainly based on on-campus collaboration. Articles with high co-citation are primarily published in core tourism journals and other relevant publications. The research content mainly pertains to tourism demand, revenue management, hotel demand and tourist volumes. Ex ante forecasting during the COVID-19 pandemic has broadened existing tourism forecasting research. The future forecasting research focuses on the rational use of big data, improving the accuracy of models and enhancing the credibility of forecasting results.

This paper uses CiteSpace to analyze tourism forecasting articles to obtain future research trends, which supplements existing research and provides directions for future research.

本文旨在分析旅游预测领域的研究重点、演化过程和未来的研究方向。

本研究使用 CiteSpace 软件对 1213 篇旅游预测文章进行了文 献计量学分析。

结果表明, 旅游预测研究经历三个阶段。机构合作包含国际机构合作和 国内机构合作, 需要持续加强国家之间的合作, 作者之间的合作多以校内合作为 主。高引用文章不仅发表在旅游领域的核心期刊还发表在其他专业的核心期刊上。 旅游预测研究的主要内容为旅游需求、收入管理、酒店需求和游客量。新冠疫情 期间的事前预测拓宽了现有的旅游预测研究。未来预测的研究重点在于合理利用 大数据, 提高模型的准确定以及提高预测结果的可信度。

本文使用 CiteSpace 分析旅游预测文章得到未来研究趋势, 既是对 现有研究的补充, 又为今后的研究提供方向。

Este artículo pretende analizar los aspectos más destacados de la investigación, el proceso evolutivo y las futuras orientaciones de la investigación en el campo de la previsión turística.

Este estudio utilizó CiteSpace para realizar un análisis bibliométrico de 1213 artículos sobre previsión turística.

Los resultados muestran que la investigación sobre previsión turística ha experimentado tres etapas. La colaboración institucional incluye la colaboración transnacional y la colaboración institucional nacional. La colaboración entre países aún debe reforzarse. La colaboración entre autores se basa principalmente en la colaboración dentro del campus. Los artículos con una alta cocitación se publican principalmente en las principales revistas de turismo y en otras publicaciones relevantes. El contenido de la investigación se refiere principalmente a la demanda turística, el revenue management, la demanda hotelera y los volúmenes turísticos. La previsión previa y durante la pandemia de la COVID-19 ha ampliado la investigación existente sobre previsión turística. La futura investigación sobre previsiones se centra en el uso racional de los big data, la mejora de la precisión de los modelos y el aumento de la credibilidad de los resultados de las previsiones.

Este artículo utiliza CiteSpace para analizar artículos de previsión turística con el fin de obtener futuras tendencias de investigación, lo que complementa la investigación existente y proporciona orientaciones para futuras investigaciones.