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The purpose of this paper is to identify the drivers of residential price as well as the degree co-movement of housing among different states in Malaysia.

This study adopted an advanced econometrics technique: the dynamic autoregressive-distributed lag (DARDL) and – the time-frequency domain approach known as the wavelet coherence test. The DARDL model was applied to identify the cointegrating relationships and the CWT was used to analyze the co-movement and lead–lag relationships among four states’ regional housing prices. The extracted data were mainly on annual basis and comprised macroeconomics and financial factors. Information with regard to residential prices and other variables was extracted from the National Property Information Centre (NAPIC) website, the Central Bank of Malaysia Statistics Report, the Department of Statistics, Malaysia, I-Property.com and the World Bank (WB). The data covered in this study were the pool data from four main states in Malaysia and different categories of residential properties.

The empirical results indicate that there were long-run cointegration relationships between the housing price and capital gain and loss, rental per square feet, disposable income, inflation, number of marriages, deposit rate, risk premium and loan-to-value (LTV) ratio. While the wavelet analysis shows that (1) in the long run, Kuala Lumpur housing price having strong co-movement with Selangor, Penang and Melaka housing prices except for Johor and (2) the lead–lag relationship also postulates Kuala Lumpur housing price having in-phase category with Selangor, Penang and Melaka housing prices except for Johor.

This study offers relevant practical implications. First, the study proposes an active collaboration between the private sector and government support which may help to smooth the pricing issue of residential properties. More low-cost residential projects are needed for focus groups including middle- and low-income earners. Furthermore, the results are expected to provide real estate investor in Malaysia, an improved understanding of the regional housing market price dynamics.

The findings of this study were obtained from various reliable sources; therefore, the results reflected the analysis of price drivers and co-movements. Furthermore, findings from this study lend some support to the argument on the rise of residential prices and offer several policy implications from a practical point of view with regard to the residential market.

This research examines the impact of lockdown stringency measures and COVID-19 cases on food and healthcare prices in six Brazil, Russia, India, China, South Africa and Turkey (BRICST) countries. This research is conducted in these countries since previous studies failed to examine the effect of COVID-19 reported cases on food and healthcare prices.

To achieve the objectives of this study, food and healthcare services were regressed against CVC and lockdown stringency measures using the dynamic autoregressive distributed lag (DARDL) model. For this purpose, we used daily data for BRICST countries such as Brazil, Russia, India, China, South Africa and Turkey.

The empirical evidence indicates that, in the long run, COVID-19 cases significantly and positively affect both food and healthcare prices in India, South Africa and China. In contrast, in the short run, COVID-19 positively affects food and healthcare prices in all countries except Russia and Turkey. Similarly, in the long run, the government stringency index (GSI) and Containment and Health Index (CHI) significantly affect health prices in India and South Africa. In contrast, GSI and CHI significantly affect healthcare prices in South Africa only in the short run. Finally, GSI and CHI significantly affect the food prices in the long run in India, South Africa and China and in the short run in South Africa only.

The widespread impact of the new Coronavirus (COVID-19) has made the world panic. COVID-19 affected all spheres of life, including food supplies and healthcare services. However, most of the empirical research failed to examine the impact of COVID-19 cases on food and healthcare prices which is the main focus of this study. Moreover, in the given context, the authors use a recently developed model that the previous studies failed to use.

Introduction: Financial failure is a concept that may arise from many internal and external factors such as operational, financial, and economic items and may incur serious losses. Over-indebtedness arising from managerial misjudgments may cause high financial distress, insufficiency, and bankruptcy. In this regard, determination of effects of capital structure decisions on financial failure risk is crucial.

Aim: The main purpose of this study is to explore the relationship between capital structure decisions and financial failure risk. For this purpose, data from Borsa İstanbul (BIST) for listed food and beverage companies for the period from 2004 to 2019 is used. Another purpose of this study is to compare the financial failure models considering capital structure theories.

Method: In the study, capital structure decisions are associated with five different financial ratios; while the financial failure risk is proxied by financial failure scores of Altman (1968), Springate (1978), Ohlson (1980), Taffler (1983), and Zmijewski (1984). Therefore, five different panel data models are used for testing these hypotheses.

Findings: The results of panel data analysis reveal that capital structure decisions have statistically significant effects on financial failure risk for all models; however, those effects vary from one financial failure model to another. Also, the results show that in the models in which financial failure risk is proxied by the Altman (1968) and Taffler (1983) scores, the aggressive financial policies increase the financial failure risk. However, regarding the models in which financial failure risk is proxied by the Springate (1978), Ohlson (1980), and Zmijewski (1984) scores, aggressive financial policies decrease the financial failure risk.

Originality of the Study: To the best of our knowledge, this chapter is original and important in terms of revealing the effects of capital structure decisions on the financial failure risk and comparing the financial failure models.

Implications: The results revealed that the risk of financial failure models represented by Altman (1968) and Taffler (1983) scores are found to be statistically stronger and more successful in meeting theoretical expectations compared to other models. Therefore, it would be more appropriate to refer Altman’s (1968) and Taffler’s (1983) financial failure models in financial failure risk measurements.

This research explores the effect of bioenergy use on carbon dioxide releases in 28 European Union (EU-28) affiliated members starting from 1990 to 2018.

Applying panels' fixed effect (FE) estimator and random effect (RE) estimator, the regressed findings are highly validated as they were robust by panel least square dummy variable corrected (LSDVC) and pooled ordinary least square (Pooled OLS) estimators.

The findings claimed that carbon dioxide releases decrease with an incline in bioenergy use and trade openness. On the other hand, fossil-fuel and economic growth indicators mounting carbon dioxide releases. The result implies that carbon dioxide releases in EU-28 region members can be mitigated significantly by mounting the quantity bioenergy use in generation channel. This will mostly participate in combating environmental pollution.

The study suggests for EU28 region members to enhance the portion of bioenergy in their fuel access to decrease emitted carbon dioxide. Governors in EU28 members should mainly encourage bioenergy expansion to raise its security and availability. The politicians of the EU28 members must assert on efficacy and productivity of bioenergy production to achieve energy accessibility and decrease dependency on conventional energy.

This research applies the recently improved model, the panel data analysis approach, which considered for the first-class impacts of estimators on the dependent variable and deals with the several problems of the common Pooled OLS estimator's manner and performance. Finally, this research contributes to the previous studies on ecological sustainability by examining the presence correlation among carbon dioxide emissions, bioenergy sustainability, trade openness, fossil fuel and gross domestic product in the EU28 region. Hence, it proves our research novelty, originality and contribution to the body of knowledge.