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The objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library.

In our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain housing returns.

Our findings show that in some cases the relationship between economic variables, such as inflation and mortgage rates, and house prices is not always consistent with the theory because we should highlight the special conditions of the examined country. The country of our sample, Greece, presents the special case of a country with severe sovereign debt issues, which at the same time has the privilege to have a strong currency and the support and the obligations of being an EU/EMU member.

The results suggest that Google Trends can be a valuable tool for academics and practitioners in order to understand what drives house prices. However, further research should be carried out on this topic, for example, causality relationships, to gain deeper insight into the possibilities and limitations of using such tools in analyzing housing market trends.

This is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market.

Czechia is a small open economy and a member state of the European Union. Several important trends and episodes that have determined economic growth can be identified over the last two decades. This chapter deals with some macroeconomic features like macroeconomic and labour market performance within the business cycle, the Czech National Bank (CNB) exchange rate commitment and interest rate policy, increasing indebtedness and budget deficits, foreign trade and the international investment position. We applied publicly available data from Eurostat, the Organisation for Economic Co-operation and Development and CNB databases. The data show that the Czech economy was significantly converging to the average economic level of the European Union. We also identified key turning points in business cycles. Macroeconomic data on economic development of the economy indicate an atypical course of the business cycle between 2020 and 2022, which can be evaluated as different from the one that followed the global financial crisis.

Inflation expectations are an important part of the transmission mechanism of the inflation targeting regime. As such, central bankers must study the inflation expectations of economic agents to anchor them close to the level of the inflation target. However, economic agents are affected by the past and current macroeconomic situation when they form their expectations concerning future inflation. Using survey data on inflation expectations in Czechia, we investigate the macroeconomic determinants of Czech analysts' and managers' inflation expectations. We find that both actual and past inflation have a substantial impact on inflation expectations of the agents surveyed. We also identify backward-looking behaviour among these agents: persistence in inflation expectations of up to two quarters was detected. Moreover, financial analysts formed inflation expectations more in line with economic theory, while company managers evinced expectations similar to those of consumers.

The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations.

Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opted to use Poisson regression as an alternative approach. Additionally, recognizing the possibility of overdispersion in the count data, we also explored the application of the negative binomial model as a means to address this concern, if present.

This study’s findings offer an insightful perspective on the housing market’s resilience in the face of COVID-19 external shock, aligning with previous research outcomes. Although TOM showed a decrease of around 10 days with standard regression and 27% with Poisson regression during the COVID-19 pandemic, it is noteworthy that this reduction lacked statistical significance in both models. As such, the impact of COVID-19 on TOM, and consequently on the housing market, appears less dramatic than initially anticipated.

This research deepens our understanding of the complex lead–lag relationships between key factors, ultimately facilitating an early indication of housing price movements. It extends the existing literature by scrutinizing the impact of the COVID-19 pandemic on the TOM. From a pragmatic viewpoint, this research carries valuable implications for real estate professionals and policymakers. It equips them with the tools to assess the prevailing conditions of the real estate market and to prepare for potential shifts in market dynamics. Specifically, both investors and policymakers are urged to remain vigilant in monitoring changes in the inventory of houses for sale. This vigilant approach can serve as an early warning system for upcoming market changes, helping stakeholders make well-informed decisions.

The Polish economy has undergone major challenges and changes over the past few decades. The country's trade flows, in particular, have become more firmly tied to the country’s Western neighbors as they have grown in volume. This study examines Poland's trade balances in ten Standard International Trade Classification (SITC) sectors versus the United States of America, first testing for and isolating structural breaks in each time series. These breaks are then included in a set of the cointegration models to examine their macroeconomic determinants.

Linear and nonlinear and nonlinear autoregressive distributed lag models, both with and without dummies corresponding to structural breaks, are estimated.

One key finding is that incorporating these breaks reduces the significance of the real exchange rate in the model, supporting the hypothesis that this variable already incorporates important information. It also results in weaker evidence for cointegration of all variables in certain sectors.

This study looks only at one pair of countries, without any third-country effects.

An important country pair's trade relations is examined; in addition, the real exchange rate is shown to incorporate economic information that results in structural changes in the economy. The paper extends the existing literature by conducting an analysis of Poland's trade balances with the USA, which have not been studied in such a context so far. A strong point is a broad methodology that lets compare the results the authors obtained with different kinds of models, both linear and nonlinear ones, with and without structural breaks.

This study adopts a new macro-perspective to explore the complex and dynamic links between financial instability and the Euro-American green equity market. Its primary focus and novelty is to shed light on the non-linear and asymmetric characteristics of dependence, causality, and contagion within various time and frequency domains. Specifically, the authors scrutinize how financial instability in the U.S. and EU interacts with their respective green stock markets, while also examining the cross-impact on each other's green equity markets. The analysis is carried out over short-, medium- and long-term horizons and under different market conditions, ranging from bearish and normal to bullish.

This study breaks new ground by employing a model-free and non-parametric approach to examine the relationship between the instability of the global financial system and the green equity market performance in the U.S. and EU. This study's methodology offers new insights into the time- and frequency-varying relationship, using wavelet coherence supplemented with quantile causality and quantile-on-quantile regression analyses. This advanced approach unveils non-linear and asymmetric causal links and characterizes their signs, effectively distinguishing between bearish, normal, and bullish market conditions, as well as short-, medium- and long-term horizons.

This study's findings reveal that financial instability has a strong negative impact on the green stock market over the medium to long term, in bullish market conditions and in times of economic and extra-economic turbulence. This implies that green stocks cannot be an effective hedge against systemic financial risk during periods of turbulence and euphoria. Moreover, the authors demonstrate that U.S. financial instability not only affects the U.S. green equity market, but also has significant spillover effects on the EU market and vice versa, indicating the existence of a Euro-American contagion mechanism. Interestingly, this study's results also reveal a positive correlation between financial instability and green equity market performance under normal market conditions, suggesting a possible feedback loop effect.

This study represents pioneering work in exploring the non-linear and asymmetric connections between financial instability and the Euro-American stock markets. Notably, it discerns how these interactions vary over the short, medium, and long term and under different market conditions, including bearish, normal, and bullish states. Understanding these characteristics is instrumental in shaping effective policies to achieve the Sustainable Development Goals (SDGs), including access to clean, affordable energy (SDG 7), and to preserve the stability of the international financial system.

Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.

The purpose of this study is to examine the determinants of the level of indebtedness in the health sector in Portugal, taking into account the effects of the COVID-19 pandemic. At the same time, an attempt is made to understand whether the effect of a pandemic crisis is similar to that of a financial crisis.

To achieve this aim, two subperiods were analyzed: a global period between 2011 and 2020 that includes the pandemic crisis and the period between 2011 and 2014, designated as the financial assistance period by the “Troika” in Portugal. For a sample of 514 companies belonging to the NACE code: 86100 – activities of the health sector with hospitalization, the panel data methodology was applied, specifically, the generalized method of moments system proposed by Arellano and Bover (1995) and Blundell and Bond (1998).

The results of the study are in line with the Pecking-order explanatory theory, demonstrating that companies in this sector follow a financing hierarchy, preferentially resorting to internally generated funds and external debt. Additionally, the results reveal that the capital structure of companies has changed due to the COVID-19 pandemic. As for the period of financial assistance, there are no major differences in evidence when the total debt ratio is considered. The results suggest different impacts when it comes to a bear market period caused by a health crisis or a period of growing economic slowdowns.

As far as we know, this is the first study that analyses the debt levels in the context of the health sector in a country with a financial system based on the bank sector, using short- and long-term debt ratios, taking into account the particularities of two different moments considered to be bear market that may eventually be useful for comparison with other bear market moments in other macroeconomic environments.

El objetivo principal de este estudio es examinar los determinantes del nivel de endeudamiento en el sector de la salud en Portugal, teniendo en cuenta los efectos de la pandemia de COVID-19. Al mismo tiempo, se intenta comprender si el efecto de una crisis pandémica es similar al de una crisis financiera.

Para lograr este objetivo, se analizaron dos subperíodos: un período global entre 2011 y 2020 que incluye la crisis pandémica y el período entre 2011 y 2014, designado como el período de asistencia financiera por la “Troika” en Portugal. Para una muestra de 514 empresas pertenecientes al código NACE: 86100 – actividades del sector de la salud con hospitalización, se aplicó la metodología de datos de panel, específicamente, el método generalizado de momentos (GMM)-sistema propuesto por Arellano y Bover (1995) y Blundell y Bond (1998).

Los resultados del estudio están en línea con la teoría explicativa del “Pecking-order”, demostrando que las empresas en este sector siguen una jerarquía de financiamiento, recurriendo preferentemente a fondos generados internamente y deuda externa. Además, los resultados revelan que la estructura de capital de las empresas ha cambiado debido a la pandemia de COVID-19. En cuanto al período de asistencia financiera, no hay diferencias significativas en la evidencia cuando se considera la proporción total de deuda. Los resultados sugieren impactos diferentes cuando se trata de un período de mercado bajista causado por una crisis de salud o un período de crecimiento económico más lento.

Hasta donde sabemos, este es el primer estudio que analiza los niveles de deuda en el contexto del sector de la salud en un país con un sistema financiero basado en el sector bancario, utilizando ratios de deuda a corto y largo plazo, teniendo en cuenta las particularidades de dos momentos diferentes considerados como momentos de mercado bajista que eventualmente pueden ser útiles para comparar con otros momentos de mercado bajista en otros entornos macroeconómicos.

O principal objetivo deste estudo é examinar os determinantes do nível de endividamento no setor de saúde em Portugal, levando em consideração os efeitos da pandemia de COVID-19. Ao mesmo tempo, tenta-se compreender se o efeito de uma crise pandêmica é semelhante ao de uma crise financeira.

Para atingir esse objetivo, foram analisados dois subperíodos: um período global entre 2011 e 2020, que inclui a crise pandêmica, e o período entre 2011 e 2014, designado como o período de assistência financeira pela “Troika” em Portugal. Para uma amostra de 514 empresas pertencentes ao código NACE: 86100 – atividades do setor de saúde com hospitalização, foi aplicada a metodologia de dados em painel, especificamente o método generalizado de momentos (GMM)-sistema proposto por Arellano e Bover (1995) e Blundell e Bond (1998).

Os resultados do estudo estão de acordo com a teoria explicativa da ordem de preferência (“Pecking-order”), demonstrando que as empresas neste setor seguem uma hierarquia de financiamento, recorrendo preferencialmente a fundos gerados internamente e dívida externa. Além disso, os resultados revelam que a estrutura de capital das empresas mudou devido à pandemia de COVID-19. No que diz respeito ao período de assistência financeira, não há diferenças significativas na evidência quando se considera a proporção total de dívida. Os resultados sugerem impactos diferentes quando se trata de um período de mercado em baixa causado por uma crise de saúde ou um período de desaceleração econômica.

Até onde sabemos, este é o primeiro estudo que analisa os níveis de dívida no contexto do setor de saúde em um país com um sistema financeiro baseado no setor bancário, utilizando índices de dívida de curto e longo prazo, levando em consideração as particularidades de dois momentos diferentes considerados como momentos de mercado em baixa que eventualmente podem ser úteis para comparação com outros momentos de mercado em baixa em outros ambientes macroeconômicos.

The purpose of this study is to explore the relationship between government spending, public debt and the informal economy. In addition, this paper investigates the moderating role of public debt in government spending and the informal economy nexus.

By utilizing a data set spanning from 2000 to 2017 of 32 Asian economies, the study has employed the dynamic ordinary least squares (DOLS) and fully modified ordinary least squares (FMOLS). The study is also extended to consider the marginal effects of government spending on the informal economy at different degrees of public debt.

The results indicate that an increase in government spending and public debt leads to an expansion of the informal economy in the region. Interestingly, the positive effect of government spending on the informal economy will increase with a rise in public debt.

This study stresses the role of government spending and public debt on the informal economy in Asian nations. To the best of the authors' knowledge, this study pioneers to explore the moderating effect of public debt in the public spending-informal economy nexus.

Several studies have examined the impact of market fundamentals on house prices. However, the effect of economic sectors on housing prices is limited despite the existence of two-speed economies in some countries, such as South Africa. Therefore, this study aims to examine the impact of mining activities on house prices. This intends to understand the direction of house price spreads and their duration so policymakers can provide remediation to the housing market disturbance swiftly.

This study investigated the effect of mining activities on house prices in South Africa, using quarterly data from 2000Q1 to 2019Q1 and deploying an auto-regressive distributed lag model.

In the short run, we found that changes in mining activities, as measured by the contribution of this sector to gross domestic product, impact the housing price of mining towns directly after the first quarter and after the second quarter in the non-mining cities. Second, we found that inflationary pressure is instantaneous and impacts house prices in mining towns only in the short run but not in the long run, while increasing housing supply will help cushion house prices in both submarkets. This study extended the analysis by examining a possible spillover in house prices between mining and non-mining towns. This study found evidence of spillover in housing prices from mining towns to non-mining towns without any reciprocity. In the long run, a mortgage lending rate and housing supply are significant, while all the explanatory variables in the non-mining towns are insignificant.

These results reveal that enhanced mining activities will increase housing prices in mining towns after the first quarter, which is expected to spill over to non-mining towns in the next quarter. These findings will inform housing policymakers about stabilising the housing market in mining and non-mining towns. To the best of the authors’ knowledge, this study is the first to measure the contribution of mining to house price spillover.