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The present study examines the long-run and short-run effects of monetary factors (money supply, interest rate, inflation and foreign currency exchange rate) on the Indian stock market.

The study used sophisticated econometric tools to analyse monthly observations from January 1993 to December 2019.

The augmented Dickey–Fuller (ADF) test indicates that the variables involved in the present study are either I(0) or I(1). The Bai–Perron test multiple break point test identifies four breakpoint dates in the Indian stock market index series. The breakpoint dates are incorporated as different dummy variables in the autoregressive distributed lag-error correction model (ARDL-ECM) regression. The F-bounds test reveals that the variables in the study are cointegrated within the time period under consideration. This study’s findings show that the interest rate, which is a proxy for monetary policy instrument, and the foreign currency exchange rate have a negative impact on the Indian stock market. Furthermore, the authors find that structural changes significantly affect the performance of Indian stock market.

The study's outcomes indicate that economic factors should be taken into account by investors and portfolio managers when formulating long-term investment strategies. The government, through the Reserve Bank of India, should exercise caution in avoiding discretionary actions that could increase interest rates since the flow of funds to the stock market will be disrupted. To reduce risk, investors should keep a close eye on how interest rates and foreign exchange rates are rising.

The study covers a long period of time, which the majority of previous work did not consider. Furthermore, the study uses different dummy variables in the ARDL model to represent structural breaks (as determined by the Bai–Perron multiple break point test).

Tourism–economy relationship in India has been studied extensively in the past literature using a single equation approach. However, the present paper diverted from this trend and examined the tourism–economy relationship using the specific to general modelling approach over the 1990–2018 time period. The study also accounts for the influence of merchandise trade, capital formation, foreign investment inflows and inflation on economic growth to achieve the robustness of the coefficient estimates.

To achieve the objective, the study utilised a specific to general modelling strategy. First, the regression equation includes only three core variables: gross domestic product (GDP), international tourist receipts and international tourist expenditures. Next, the authors include other control variables in the regression equation one by one, leading us to test five model types for investigating the cointegration among the variables. As for the estimation technique, the authors employed autoregressive distributed lag (ARDL) approach.

The paper's findings highlight that tourism receipts and expenditures exert a positively significant impact on economic growth. Moreover, including the additional independent variables does not substantially change the tourism and economic growth relationship. The existence of one-way causality from tourism expenditures to economic growth supports the tourism-led growth hypothesis. These findings highlight the rationale for intervention by the government and policymakers to promote tourism potential and facilities to accelerate the overall growth performance of the country. While the existence of one-way causal effect from economic growth to tourism revenues supports the growth-led tourism development hypothesis, implying that economic expansion is necessary for tourism development.

This research article tried to present a comprehensive picture of India's tourism–economy relationship. However, the present study is organised as an aggregate economy-level analysis. It assumed that the aggregate tourism sector is homogenous. However, different tourism sectors exert different levels of influence on the economy. The authors expect future research can take the disaggregated analysis of the tourism–economy relationship.

This study provides valuable insights into the tourism-led growth hypothesis in India. The study highlights comprehensive intervention by the government and policymakers for accelerating tourism development to invigorate the overall growth performance of the country over the long run. The principal recommendation emerging from the present research is that the tourism growth potential can be depended upon to stimulate the economic performance of the Indian economy.

The present study diverted from the previous empirical studies by following a specific to general modelling strategy. First, the regression model includes only three core variables such as economic growth, tourism receipts and tourism expenditure. Next, the authors include other control variables in the regression equation one by one, leading us to test five model types for investigating the cointegrating relationship among the variables. GDP growth rate is used as a dependent variable in all five specifications. The idea is to expand the model to capture every feature of the data generating process.

Human capital is a portfolio of rich skills that the labour possesses. Human capital has attracted significant attention from scholars. Nevertheless, empirical findings on the utility of human capital have often been divided. To address the research gap in the literature, the authors attempt to understand how human capital plays a significant role in financial development and economic growth nexus.

The authors rely on secondary data published by the World Bank. The authors use econometric tools such as the autoregressive distributive lag (ARDL) model and related statistical tests to study the relationship between human capital, India's financial growth and gross domestic product (GDP) growth.

Study findings suggest that human capital and financial development contribute significantly to economic growth. Further, the authors found that human capital has a positive and significant moderating effect on the path of joining financial development and economic growth.

The study contributes to the human capital debate. Despite the rich body of literature, the study based on World Bank data confirms the previous findings that investment in human capital is always useful for the financial and economic growth of the nation.

This paper reveals some unique findings regarding effect of financial development and economic growth nexus which opens the window of new dimension to think about their nexus. It also provides a different pathway to foster the economic growth by using human capital and financial development as together, especially in India.

The environmental deterioration has become one of the most economically consequential and charged topics. Numerous scholars have examined the driving factors failing to consider the structural breaks. This study aims to explore sustainability using the per capita ecological footprints (EF) as an indicator of environmental adversities and controlling the resources rent [(natural resources (NR)], labor capital (LC), urbanization (UR) and per capita economic growth [gross domestic product (GDP)] of China.

Through the analysis of the long- and short-run effects with an autoregressive distributed lag model (ARDL), structural break based on BP test and Granger causality test based on vector error correction model (VECM), empirical evidence is provided for the policies formulation of sustainable development.

The long-run equilibrium between the EF and GDP, NR, UR and LC is proved. In the long run, an environmental Kuznets curve (EKC) relationship existed, but China is still in the rising stage of the curve; there is a positive relationship between the EF and NR, indicating a resource curse; the UR is also unsustainable. The LC is the most favorable factor for sustainable development. In the short term, only the lagged GDP has an inhibitory effect on the EF. Besides, all explanatory variables are Granger causes of the EF.

A novel attempt is made to examine the long-term equilibrium and short-term dynamics under the prerequisites that the structural break points with its time and frequencies were examined by BP test and ARDL and VECM framework and the validity of the EKC hypothesis is tested.

Considering the frequency of extreme events, enhancing the global financial system's stability has become crucial. This study aims to investigate the contagion effects of extreme risk events in the international commodity market on China's financial industry. It highlights the significance of comprehending the origins, severity and potential impacts of extreme risks within China's financial market.

This study uses the tail-event driven network risk (TENET) model to construct a tail risk spillover network between China's financial market and the international commodity market. Combining with the characteristics of the network, this study employs an autoregressive distributed lag (ARDL) model to examine the factors influencing systemic risks in China's financial market and to explore the early identification of indicators for systemic risks in China's financial market.

The research reveals a strong tail risk contagion effect between China's financial market and the international commodity market, with a more pronounced impact from the latter to the former. Industrial raw materials, food, metals, oils, livestock and textiles notably influence China's currency market. The systemic risk in China's financial market is driven by systemic risks in the international commodity market and network centrality and can be accurately predicted with the ARDL-error correction model (ECM) model. Based on these, Chinese regulatory authorities can establish a monitoring and early warning mechanism to promptly identify contagion signs, issue timely warnings and adjust regulatory measures.

This study provides new insights into predicting systemic risk in China's financial market by revealing the tail risk spillover network structure between China's financial and international commodity markets.

This study aims to better understand the phenomenon of corruption in Tunisia in relation to its impact on economic development. The period of study is 1995 to 2014. The auto-regressive distributed lag (ARDL) model is adopted to examine the existence of a long-term relationship between the above-mentioned variables and also the direct and indirect consequences of corruption on economic development in Tunisia.

The study uses a modern econometric technique to estimating the long-term relationship (e.g. the co-integration) between corruption and economic development; using this technique also allows us to investigate the impact of corruption on economic growth.

The empirical results show that corruption has a negative effect on per capita gross domestic product (GDP) in Tunisia for the period under review. This effect is described as a direct effect of corruption in the long term; specifically, declines are observed in per capita GDP, over the long run, by almost 1 per cent, following a 1 per cent increase in the level of corruption. The results also show that corruption has indirect effects via transmission channels, such as investment in physical capital, which is positively significant in the presence of corruption. The same observation is made at the level of government expenditure during the previous year, while for those of the current year, the coefficient becomes negative but not significant. With respect to human capital, the impact of corruption on education expenditures is insignificant.

The paper begins with an overview of previous literature in this area. Given the nature of corruption and the differences in the meanings attributed to it, from one country to another and from one culture to another, the paper moves on to study the impact of corruption in Tunisia as a case study for one country with one socio-cultural environment. The authors then propose several methods and possible solutions, which could be implemented to deal with this problem.

The author analyzes households' inflation expectations data for India, collected quarterly by the RBI for more than a decade. The contribution of this paper lies in two folds. First, this study examines the relationship between relatively recent inflation expectations survey of households (IESH) and the actual inflation for India. Secondly, the author employs a structural VAR with the time period 2006 Q2 to 2020 Q2 on inflation expectation survey data of India. A short-term non-recursive restriction is imposed in the model in order to capture the simultaneous co-dependence causal effect of inflation expectation and realized inflation.

This paper studies the dynamic behavior of inflation expectations survey data in two folds. First, the author analyzes the time series property of the survey data. The author begins with testing the stationarity property of the series, followed by the casual relationship between the expected and actual inflation. The author further examines the short-run and long-run behavior of the IESH with actual inflation. Employing autoregressive distributed lag and Johansen co-integration, the author tested if a long-run relationship exists between the variables. In the second approach, the author investigates the determinants of inflation expectations by employing a non-recursive SVAR model.

The preliminary explanatory test reveals that inflation expectation is a policy variable and should be used in monetary policy as an instrument variable. The model identifies the price puzzle for India. The author finds that the response of inflation to a monetary policy shock is neutral. The results also indicate that the expectations of the general public are self-fulfilling.

IESH has only commenced from September 2005, hence is relatively new as compared to other survey in developed countries. Being a new data set so far, the author could not locate any study devoted in analyzing the behavior of the data with other macroeconomic variables.

Following the financial crisis, across the Western world, defense budgets have undergone substantial and far-reaching cuts. But in the emerging markets there has been a significant rise in defense expenditure (DE). Countries such as China, Brazil, and India have doubled or even tripled their defense spending during the past two decades. It has been generally argued that terrorism can potentially affect economic growth adversely in the short run through a number of channels. Terrorist attacks can increase uncertainty which limits the scope for domestic investments and also diverts foreign direct investment. In order to mitigate terrorism, increased government spending on security can crowd out more growth-enhancing public and private investments in social sectors such as health and education, which in turn may affect the long run growth of a nation. Terrorism also hinders growth by raising the cost of doing business in terms of higher wages, larger insurance premiums, and greater security expenditures. These higher costs result in reduced profits and, thus, smaller return on investment. Terrorist attacks can also destroy infrastructure, thereby leading to business disruptions. This study attempts to examine both the short- (SR) and long-run (LR) associations among terrorism, DE, and its impact on GDP taking time series evidence, using autoregressive distributed lag model (ARDL) model from some selected countries of Asia – India, China, Bangladesh, and Pakistan – during 1990 to 2014. The results show that there are LR associations among the three variables for Pakistan only.

The study on how macroeconomic factors affect non-performing loans (NPLs) have not been focussed on property loans, which had been amongst the largest contributor of NPLs in many countries. At the same time, whilst there are many studies that focusses on NPLs during the recession and financial crises, not many studies focus on how macroeconomic factors affect property NPLs in a recovering economic environment. The purpose of this study seeks to fill the gap by analysing the relationships between gross domestic product (GDP), interest rates, income, foreign direct investments (FDI), housing prices and taxes on property NPLs with Malaysia as a case study in which NPLs rose for the first time after declining for almost a decade since the 2008–2009 global financial crisis. This study aims to understand the dynamics and direction of causation in relationships.

The author uses the auto regressive distribution lag analysis between the independent variables of GDP, interest rates, housing prices, service taxes, percapita income and FDI affecting the dependent variable of property NPLs from 2009 to 2017, during a unique period of recovering economic environment where NPLs rose for the first time in almost a decade of decline.

This study found that interest rates, housing prices, income, GDP and service taxes were found to possess long cause effects and long run elasticity with NPLs. At the same time, interest rates were found to implicate property NPLs significantly in longer periods, followed by GDP, housing prices, service taxes and income. FDIs were found to be insignificantly negative in implicating property NPLs in the long run.

This paper allows policymakers to understand the dynamic implications of crucial macroeconomic factors in affecting NPLs so that appropriate strategic monetary policies could be formulated towards addressing them. More focus shall be given to addressing the long term implications of these factors on NPLs.

Appropriate strategic monetary policy making can be channelled towards addressing these factors via understanding the short and long term implications of macroeconomic variables on property NPLs. Policymakers can take note of the long cause effects and long run elasticity of average interest rates, housing prices, income levels, GDP and service taxes with property NPLs so that appropriate long term policies can be addressed to control the rise of property NPLs in the country. At the same time, priority should be given towards strengthening of the GDP of the country due to its strongest impact in long term effects with reduction of NPLs in the country.

The insights from the present study suggest policymakers interested in bringing stability in the real estate finance system need to account for the various macroeconomic variables found in this study.

The paper is novel on at least two dimensions. First, this study involves focussing on a unique period of recovering economic environment where NPLs rose for the first time after a decade of decline since recovering from the 2008–2009 global financial crisis. At the same time, this study focusses on property NPLs, which is unique in nature compared to general NPLs. This study had enabled policymakers to better understand the dynamic implications of several macroeconomic variables affecting property NPLs and assist them in strategic monetary policy making.