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Classical unit root tests are known to suffer from potentially crippling size distortions, and a range of procedures have been proposed to attenuate this problem, including the use of bootstrap procedures. It is also known that the estimating equation’s functional form can affect the outcome of the test, and various model selection procedures have been proposed to overcome this limitation. In this chapter, the authors adopt a model averaging procedure to deal with model uncertainty at the testing stage. In addition, the authors leverage an automatic model-free dependent bootstrap procedure where the null is imposed by simple differencing (the block length is automatically determined using recent developments for bootstrapping dependent processes). Monte Carlo simulations indicate that this approach exhibits the lowest size distortions among its peers in settings that confound existing approaches, while it has superior power relative to those peers whose size distortions do not preclude their general use. The proposed approach is fully automatic, and there are no nuisance parameters that have to be set by the user, which ought to appeal to practitioners.

This paper aims to study the impact of economic freedom and some key macroeconomic variables on the foreign direct investment (FDI) inflow in Brazil.

An econometric model is developed that includes FDI inflow as the dependent variable and macroeconomic variables such as the output, current account balance, the real exchange rate, openness and economic freedom as explanatory variables. Annual time series data from 1995 to 2022 is used. Before carrying out the estimation, the time series properties of the data are diagnosed using unit root tests and cointegration tests. Since the data series were found to be stationary in the first difference form and the variables in the model were cointegrated, an error correction model is developed and estimated.

The findings demonstrate that the size of the market (gross domestic product), current account balance and the economic freedom index significantly influence FDI inflow to Brazil. Although the signs of openness and the real exchange rate align with theoretical expectations, they do not attain statistical significance.

To the best of the authors’ knowledge, this is the first formal study on the impact of economic freedom on the FDI inflow in Brazil. The finding of this study adds value to the understanding of FDI dynamics in Brazil, highlighting the critical role of economic freedom and market size in attracting foreign investment.

The paper aims to analyse the impact of economic and governance factors on remittance inflows to India from the UK, USA and UAE. India is globally recognised as the largest recipient of remittances.

Using a comprehensive time series data set spanning 1996 to 2022, the authors use an innovative non-linear autoregressive distributed lag model approach to examine the influence of economic growth, corruption control and employer availability in the three source countries on remittance inflows to India.

The results indicate that in the UAE, changes in economic growth and corruption control directly affect remittance outflows. However, the presence of employers in the UAE has minimal impact on remittance outflows to India. Regarding the UK, fluctuations in economic growth primarily drive remittance outflows to India. The effect of corruption control and employment opportunities on remittance outflows is marginal. In the USA, economic growth does not notably impact remittance outflows, whereas corruption control and employment opportunities significantly influence the outflows to India.

These findings have important implications for policymakers. Analysing macroeconomic factors from key remittance-sending nations offers valuable insights for Indian policymakers and their international counterparts to enhance remittance inflows. The study focuses on three countries that collectively contribute to about 50% of India's remittances, providing a unique contribution compared to the usual country-specific or regional focus in existing literature. Finally, leveraging these findings, NITI Aayog, an organisation dedicated to achieving India's sustainable development goals, can effectively monitor macroeconomic indicators related to significant remittance-sending countries.

The purpose of this study is to quantify the impact of electricity power outages on the local housing market in South Africa.

This study uses the autoregressive distributive lag (ARDL) and quantile autoregressive distributive lag (QARDL) models on annual time series data, for the period 1971–2014. The interest rate, real income and inflation were used as control variables to enable a multivariate framework.

The results from the ARDL model show that real income is the only factor influencing housing price over the long run, whereas other variables only have short-run effects. The estimates from the QARDL further reveal hidden cointegration relationship over the long run with higher quantile levels of distribution and transmission losses raising the residential price growth.

Overall, the findings of this study imply that the South African housing market is more vulnerable to property devaluation caused by power outages over the short run and yet remains resilient to loadshedding over the long run. Other macro-economic factors, such as real income and inflation, are more influential factors towards long-run developments in the residential market.

To the best of the authors’ knowledge, this is the first study to examine the empirical relationship between power outages and housing price growth.

The purpose of this study is to assess the impact of the shadow economy on three sustainable development indicators while considering the moderating effect of the governance quality, and to highlight the non-linearity of the considered relationship.

A sample of 82 countries covering the period from 1996 to 2017. The dynamic first-differenced generalized method of moments (FD-GMM) panel threshold model is implemented to control for non-linearity.

The shadow economy hinders sustainable development in countries with low-governance quality, while the opposite result holds in countries with high-governance quality. The critical thresholds triggering the switch from one regime to another vary across the sustainable development indicators. Boosting growth requires enhancing the legal system and the economic dimension of governance, while promoting environmental quality requires the implementation and enforcement of specific environment-friendly regulations.

The study addresses non-linearity and the moderating effect of governance quality. The use of six governance indicators allows to gauge the ability of each governance dimension to curb the negative effects of the shadow economy. Considering the three objectives of sustainable development allows to identify specific policy recommendations for each of them.

This study aims to assess the nexus among oil price (OP), renewable energy consumption (REC) and trade balance (TB) for India using annual time series data for the time period 1985–2019. In particular, the authors examine whether REC improves India's TB in the context of high oil import dependence.

The study uses autoregressive distributed lags (ARDL) bound testing approach that has the advantage of yielding estimates of long-run and short-run parameters simultaneously. Moreover, the small sample properties of this approach are superior to other multivariate cointegration techniques. Fully modified ordinary least square (FMOLS) and dynamic ordinary least squares (DOLS) are also applied to test the robustness of the results. The causality among the series is investigated through block exogeneity test based on vector error correction model.

The findings based on ARDL bounds testing approach indicate that OPs exert a negative impact on TB of India in both long run and short run, whereas REC has a favorable impact on the TB. In particular, 1% increase in OPs decreases TBs by 0.003% and a 1% increase in REC improves TB by 0.011%. The results of FMOLS and DOLS corroborate the findings from ARDL estimates. The results of block exogeneity test suggest unidirectional causation from OPs to TB; OPs to REC and REC to TB.

The study underscore the importance of renewable energy as a potential tool to curtail trade deficits in the context of Indian economy. Our results suggest that the policymakers must pay attention to the hindrances in augmentation of renewable energy usage and try to capitalize on the resulting gains for the TB.

Climate change is a major challenge for developing countries like India. Renewable energy sector is considered an important instrument toward attaining the twin objectives of environmental sustainability and employment generation. This study underscores another role of REC as a tool to achieve a sustainable trade position, which may help India save her valuable forex reserves for broader objectives of economic development.

To the best of the authors’ knowledge, this is the first study that probes the dynamic nexus among OPs, REC and TB in Indian context. From a policy standpoint, the study underscores the importance of renewable energy as a potential tool to curtail trade deficits in context of India. From a theoretical perspective, the study extends the literature on the determinants of TB by identifying the role of REC in shaping TB.

The purpose of this study is to examine two issues, namely the degree of current account deficit (CAD) sustainability and the degree of capital mobility.

The sample for this study comprises 24 Latin American and Caribbean countries, including three regional agreements: Andean Community, MERCOSUR (Mercado Común del Sur), and SICA (Central American Integration System). This study employs the dynamic common correlated effects mean group (DCCEMG) estimator in a panel data set to investigate the long-run relationship between savings and investment along with short-run dynamics.

The findings indicate that CAD is weakly sustainable in the Latin American and Caribbean region, MERCOSUR, and SICA, while CAD is strongly unsustainable in the Andean Community. The sub-period analysis reveals that CAD has been adversely affected by the 2008 crisis. However, in the post-crisis period, CAD has been slowly decreasing in the Latin American and Caribbean region and Andean Community, whereas CAD has continued increasing in MERCOSUR and SICA. Further, the estimates of error-correction terms and short-run coefficients indicate that the Andean Community and MERCOSUR observe a higher degree of long-run and short-run capital mobility than SICA.

The results carry fundamental implications for policy-making processes aimed at maintaining sustainable CADs.

This study gives an alternative interpretation of the “Feldstein-Horioka” coefficient in terms of CAD sustainability and analyses the saving–investment relationship in light of Chudik and Pesaran (2015).

Czechia's economic growth is substantially dependent on foreign trade. An independent monetary policy in a managed floating exchange rate regime gives a unique perspective on the effects of the exchange rate on foreign trade. This chapter evaluates the effects of exchange rate development on different sectors of Czechia's foreign trade. Using disaggregated data based on trading partner and product category, the period from 1999 to 2020 is analyzed. Czechia's 10 major trading partners are included in the estimation. The relationship between exchange rates and foreign trade is assessed through a Johansen cointegration approach and modified vector error correction model. The results of the Johansen cointegration test indicate that the majority of the aggregate bilateral trade balances are in a long-term relationship with Czechia's gross domestic product (GDP), foreign GDP and exchange rate movements. The J-curve is proved only in chemicals and related products traded with France, manufactured goods traded with Italy and Slovakia and mineral fuels and lubricants traded with the Netherlands.

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.

This study aims to investigate the effect of monetary policy on housing prices for US economy. It specifically examines whether nominal or real interest rates are the key drivers behind fluctuations in housing prices in US.

Monthly data from January 1991 to July 2023 and various appropriate analytical tools such as unit root tests, Johansen’s cointegration test, vector error correction model (VECM), impulse response function and Granger causality test were applied for the data analysis.

The Johansen cointegration findings reveal the presence of a long-term relationship among the variables. VECM results indicate a negative correlation between nominal and real interest rates and housing prices in both the short and long terms, suggesting that a strict monetary policy can help in controlling the housing price increase in the USA. However, housing prices are more responsive to changes in nominal interest rates than to real interest rates. Additionally, the study reveals that the COVID-19 pandemic contributed to the upsurge in housing prices in the USA.

This study contributes by examining the role that nominal or real interest rates play in shaping housing prices in the USA. Moreover, given the recent significant upsurge in housing prices, this study presents a unique opportunity to investigate whether these price increases are influenced by the Federal Reserve's monetary policy decisions regarding nominal or real interest rates. Additionally, using monthly data, this study provides a deeper understanding of the fluctuations in housing prices and their connection to monetary policy tools.