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The search for more effective policies, choice of optimal implementation strategies for achieving defined policy targets (e.g., cost-containment, improved access, and quality healthcare outcomes), and selection among the metrics relevant for assessing health system policy change performance simultaneously pose continuing healthcare sector challenges for many countries of the world. Meanwhile, research on the core drivers of healthcare costs across the health systems of the many countries continues to gain increased momentum as these countries learn among themselves. Consequently, cross-country comparison studies largely focus their interests on the relationship among health expenditures (HCE), GDP, aging demographics, and technology. Using more recent 1980–2014 annual data panel on 34 OECD countries and the panel ARDL (Autoregressive Distributed Lag) framework, this study investigates the long- and short-run relationships among aggregate healthcare expenditure, income (GDP per capita or per capita GDP^_HCE), age dependency ratio, and “international co-operation patents” (for capturing the technology effects). Results from the panel ARDL approach and Granger causality tests suggest a long-run relationship among healthcare expenditure and the three major determinants. Findings from the Westerlund test with bootstrapping further corroborate the existence of a long-run relationship among healthcare expenditure and the three core determinants. Interestingly, GDP less health expenditure (GDP^_HCE) is the only short-run driver of HCE. The income elasticity estimates, falling in the 1.16–1.46 range, suggest that the behavior of aggregate healthcare in the 34 OECD countries tends toward those for luxury goods. Finally, through cross-country technology spillover effects, these OECD countries benefit significantly from international investments through technology cooperations resulting in jointly owned patents.

The goal of this study is to look into the connection between Sri Lanka’s fiscal deficit and inflation. Sri Lanka is currently experiencing one of its worst inflation crises in its history, necessitating an investigation into how fiscal deficit affects inflation, as it has been experiencing an ever-increasing fiscal deficit for the last four decades. The quantitative methodology is employed in this study using annual data from 1977 to 2019 following the ARDL technique in the analysis. The findings showed that both in the long run and the near term, Sri Lanka’s fiscal deficit had a positive and significant link with inflation. The policymakers should increase the revenue through the taxes in order to bridge the fiscal deficit. As a developing country, it cannot afford to continue with the ever-increasing fiscal deficit which has become a burden to country. Also, it is the responsibility of each government to think carefully to reduce its massive expenditure which has become a common feature in the country for the last four decades. Cutting down government expenditure can improve the economic growth and well-being of the citizens too. The government should therefore concentrate on short-term investment programmes that will benefit the country while doing the same in the long run.

Introduction: The credit default swap (CDS) represents a country’s credit risk premium. CDS premium changes by being affected by several factors. These changes are followed by international investor for their investment decisions. CDS premium is important for country to determine the country default risk correctly. Purpose: In this study, the authors seek to examine the effects of macroeconomic indicators on the CDS premium, which is used as a measure of sovereign credit risk. Accordingly, in addition to the CDS premium, economic growth, the inflation rate, the interest rate, the real exchange rate, the net foreign debt rate, and the foreign trade deficit rate were employed to represent macroeconomic indicators. Methodology: The relationship between the given variables during the period spanning from 2009:I–2019:II in Turkey was analyzed with the help of the Dolado–Lütkepohl causality test and the autoregressive distributed lag method. Findings: The inflation rate, the real exchange rate, the interest rate, the net foreign debt rate, and the foreign trade deficit rate, which are among the macroeconomic variables (excluding economic growth), have a positive effect on the CDS premium in the short term as well as the long term. The effect of economic growth is negative. Additionally, from an economic standpoint, the coefficients of macroeconomic variables are in the expected direction. These findings verify the effects of macroeconomic indicators on the CDS premium.

The unstable money demand function over the recent decades may explain the unsatisfactory performance of the exchange rate model. Numerous studies have shown that Divisia money serves as a better variable for a stable money demand function. In this study, Divisia money is used as an alternative money supply in MYR/USD exchange rate determination. This study finds that Divisia money differential, real income differential, relative short-term interest rate and real stock prices affect the MYR/USD exchange rate in the long run. The major implication of this study is that policy-makers could monitor the MYR/USD exchange rate via the money supplies following the principle of Divisia monetary aggregate, which assigns higher weightage to more frequently traded monetary assets.

This chapter empirically examines the relationship between defense expending, budget deficits, and income redistribution in India for the period 1970–2009. The analysis is based on an autoregressive distributed lag model (ARDL) popularized by Pesaran and Shin (1999). Empirical estimates reveal that military spending in India is indeed associated with income redistribution. The empirical approach indicates that there exists a long-run relationship between transfer payments as a percentage in GDP (TP), defense expenditures as a percentage in GDP (ME), and budget deficits as a percentage in GDP (DEF) in India. Defense expenditures as a percentage in GDP and the budget deficits as a percentage in GDP have positive and significant impacts on transfer payments in the same fiscal year. But the budget deficit as a percentage in GDP has a negative and significant impact on transfer payments as a percentage in GDP in the next fiscal year.

COVID-19 has been the subject of a number of inquiries recently. All country's capital market practices have been affected by the COVID-19 outbreak. Economic woes, along with the stock market crash, have hit emerging markets and developing economies in a variety of directions.

This study is an attempt to focus on the Indian economy to provide the gist of the situation and recovery mode of an economy with the help of growth indicators of the economy.

This study is based on secondary data. The researchers applied some econometric tools, viz, unit root test Augmented Dickey-Fuller (ADF), Panel Granger Causality, and Panel ARDL Bound Test were applied to examine the relationship of economic indicators and stock market benchmark in two periods: March 2020–June 2021 (during period) and July 2021 to March 2022 (post period).

The findings of this study explored the different causal relationships for the selected variables in both periods. The study discussed the reasons for ARDL (Auto Regressive Distributed Lag) bound for all selected factors. The study revealed the story of crude oil prices and Gold as trusted investment avenues during the crises.

As we know, the capital market's backlash is reflected in movements in stock prices and stock exchange volume, which are concerned with the economic effects of the pandemic and urged the segment to react. Investors can use the information in the event to make investment decisions.

Significant evidence in the literature points to money demand instability and therefore inaccurate forecasting. In view of this issue, this chapter seeks to use a method, innovative for money demand literature, that is, the machine learning model to predict money demand. Specifically, this chapter uses Random Forest Regression to predict money demand using monthly data in the Indian context over the period April-1996 to December-2018 using the variables usually used in literature. The chapter finds that in money demand prediction, the Random Forest Regression performs fairly well. The results are also compared to traditional models and it is found that the Random Forest Regression model has the potential to enhance the prediction of money demand over what traditional models predicts.

Introduction: Financial development has a direct impact on the housing market by facilitating access to credit. The increase in housing loans resulting from the relaxation of the credit constraint causes an increase in housing demand and house prices. Purpose: This study aims to examine the relationship between financial development and house prices in Turkey, using the variables: the domestic credit to the private sector and total housing and consumer credits. Methodology: To determine any long-run relationship between financial development and house prices, the autoregressive distributed lag methods are used, covering the selected variables such as real GDP, inflation, mortgage interest rate, and stock price from 2010Q1 to 2020Q2. Findings: The study’s findings show that both variables representing financial development have a statistically significant and substantial positive effect on house prices. Besides, the selected macroeconomic variables have the theoretically expected impact on house prices.

In the Lao People’s Democratic Republic (Lao PRD), the services sector accounts for more than 41% of GDP and more than 80% of total trade (World Bank, 2021). Empirical studies show that most of the services trade occurs in the travel and tourism sectors, accounting for more than 50% of the total services trade in the Lao PDR. The services sector also plays an essential role in the Lao PDR’s wholesale and retail sectors, which employ the most significant number of people across all services sectors. The services trade balance was in a surplus between 1997 and 2011, though in 2012, it entered a significant deficit that continues to the present. This study investigates the link between services trade and economic growth in the Lao PDR, building on a recent analysis of the services trade in various economic and economic growth. The authors use econometric methods such as the autoregressive distributed lag (ARDL) bound test and the Granger causality test to analyze time-series data for the Lao PDR from 1990 to 2019. The econometric results demonstrate the long-run relationship between economic growth and variables related to the services trade. This indicates the government and policymakers of the Lao PDR should invest in infrastructure, particularly in trade facilitation and the liberalization of the services sector, to facilitate the acceleration of economic growth.