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Higher economic output as measured in gross national product (GNP) may not always imply a higher quality of living. It has been the outcome of the long debate between growth and development of a nation. The aims of economic growth should be reconsidered because it has polluted the environment, wasted natural resources, harmed people’s quality of life, and failed to alleviate socioeconomic problems. It is also a common phenomenon to the economies of the South Asian region. The study is thus conducted to show the existence of long-run relationship and short-run interplays between output efficiency of energy use (GEU) and carbon efficiency of energy use (CEU) in the panel of countries in the South Asian region for the period of 1971–2014. The results show that there is a long-run and short-run association between energy efficiency in output and carbon emission as respectively measured in GEU and CEU. This means that in South Asia, energy consumption leads to an increase in both gross domestic product and carbon emissions. When GEU is used as the independent variable in vector error correction model (VECM), the result reveals that any short-run disequilibrium from the long-run stable connection will be adjusted over time, and the long-run stable relationship will be restored.

The purpose of this study is to examine the temporal relationships between inflation and exchange rate changes and their implications for the trade balance in Uganda, which saw persistent trade deficits, rising inflation and disinflation episodes, as well as significant exchange-rate realignments and other liberalization measures over the sample period considered.

The short-run dynamics of the variables in question and the pattern of their long-run relationships are examined applying the bounds testing approach to cointegration on quarterly data.

The estimates suggest that, in the long run, a real depreciation leads to an increase in inflation; and that both real depreciation and inflation exert no significant effect on the trade balance. The estimated short-run dynamics suggest a causal relationship between the trade balance and the real exchange rate and between the real exchange rate and inflation, which is also found responsive to developments in the foreign sector. Taken together, the short-run and long-run multipliers seem to provide a weak support for the J-curve effect, while no evidence is found for the presence of the S-curve effect.

The study sheds light on the relationship among real exchange rate, inflation and the trade balance in the context of a small developing economy; it highlights that an improvement in the trade balance requires more than an appropriate exchange rate policy and underscores the importance of other policies in strengthening the external sector of the economy.

The purpose of this paper is to investigate the interrelationships amongst the sector‐specific indices of the Qatar Exchange (QE) (i.e. Banking and Financial Institutions (BFI), Industrial (IND), Insurance (INS), and Services (SER)). More specifically, three key issues are explored in this study. First, the long‐run relationships amongst the sectors. Second, the short‐run causal relationships amongst them; and third, the relative degree of endogeneity/exogeneity of each sector.

To address the issues of interest, the author employs the econometric analyses of Johansen's multivariate cointegration, Granger's causality, and generalized forecast error variance decomposition. This battery of techniques gives the opportunity to examine the nature of both long‐ and short‐run intersectoral relationships in the QE. To augment the robustness of the empirical analysis, daily as well as weekly closing stock price indices for the four sectors of the Qatar Exchange are used, spanning the period from January 2, 2008 up to April 7, 2011.

Based on daily and weekly data, the results of Johansen's multivariate cointegration analysis suggest that the four sector indices of the QE share a long‐term equilibrium relationship. The Granger's causality analysis based on daily and weekly datasets provides clear evidence that the BFI sector seems to be a significant causal factor in regard to the price predictability of the remaining sectors in the short run, and that the SER sector surprisingly seems to have the least influential role. Finally, the results of the generalized forecast error variance decomposition analysis using daily data show that the IND and BFI appear to be the most exogenous sectors, whereas the SER and INS are the most endogenous ones. The results based on weekly data confirm the relative exogeneity of the BFI sector and the relative endogeneity of the SER sector.

The findings of this study hold practical implications for individual and institutional investors alike. The potential gains derived from cross‐sector diversification could be rather limited, given the significant degree of interrelationships found amongst the sector indices of the QE. Moreover, the composition of domestic portfolios based on sector‐level investments should be revisited, particularly after major events. The findings also bring some important insights for policymakers. Given the influential role played by the BFI sector in the Qatari economy, policymakers should design appropriate strategies that curb the spread of unanticipated shocks originating from this sector to its counterparts. Besides, due to the considerable degree of endogeneity of the SER sector, it is essential for policymakers to set up precautionary regulations, with the aim of minimizing its vulnerability to common shocks in turbulent times.

Building upon the extant research and focusing on a relatively unexplored market, the paper represents a pioneer attempt to provide empirical evidence on the interdependence structure amongst the sector‐specific indices of the Qatar Exchange.

The study aims to analyse the role of finance in the agro-industrialisation nexus in Nigeria using annual data on manufacturing value added, agricultural value added and volume of finance availed to the agricultural sector from 1981 to 2015.

To establish the presence of a long-run relationship, the error correction model and bounds cointegration techniques are employed. Likewise, the model is augmented to test whether the associated relationship between industrial output and agricultural output depends on access to finance by farmers with the inclusion of an interaction term.

Some salient contributions to the literature are as follows: agriculture and finance are strong and positive predictors of industrialisation in the long run; in the short run, past realisations of industrial output and finance have significant asymmetric effects on industrial output; the explanatory power of agriculture decreases with the growth of the financial system; and the long-run results validate the role of finance in the agro-industrialisation nexus.

Given these findings, achieving growth in the agricultural sector that will induce desired industrialisation should be prioritised by the government through agencies such as the central bank, financial intermediaries and other stakeholders with a view to making agricultural financing a major concern for sustainable domestic consumption and industrial growth.

The purpose of this paper is to examine the relationship between financial development indicators and human development in India using annual data from 1980-2012.

The Ng-Perron unit root test is used to check for the order of integration of the variables. The long run relationship and short run dynamics are examined by implementing the ARDL bounds testing approach to co-integration. Granger’s non-causality test and variance decomposition techniques are also used to examine the impact of financial development indicators on human development.

The results confirm a long run relationship among the variables. The results of granger non causality indicate that unidirectional causality runs from financial development indicators to human development index (HDI). The variance decomposition analysis shows that among all the financial indicators, broad money supply (M3) has the largest contribution to changes in human development in India.

The present study recommends for appropriate reforms in financial market to attain sustainable human development in India. The findings will be useful for India’s policy makers, in order to maintain the parallel expansion of financial development and human development.

This paper is first of its kind to empirically examine the casual relationship between financial development indicators and human capital development proxied by HDI in India by using modern econometric techniques.

Examines the behaviour of UK employment in manufacturing over the period 1964 to 1986. The use of cointegration techniques allows the separation of a long‐run equilibrium relationship for employment from its short‐run dynamics. The estimated model demonstrates a high degree of parameter stability both within and outwith the sample period used for estimation. Given the noted sensitivity of other employment equations to system shocks, the model′s performance pre – and post‐1979 is particularly noteworthy.

Analyzes dynamic linkages between stock prices and four macroeconomic variables for the case of Malaysia using standard and well‐accepted methods of cointegration and vector autoregression. Empirical results suggest the presence of a long‐run relationship between these variables and the stock prices and substantial short‐run interactions among them. In particular, documents positive short‐run and long‐run relationships between the stock prices and two macroeconomic variables. The exchange rate, however, is negatively associated with the stock prices. For the money supply, documents immediate positive liquidity effects and negative long‐run effects of money supply expansion on the stock prices. Also notes the predictive role of the stock prices for the macroeconomic variables. However, there seems to be irregularity in the data when observations from the recent crisis are included. Finally, documents the disappearance of the immediate positive liquidity effects of the money supply shocks and unstable interactions between the stock prices and the exchange rate over time.

Housing prices in the UK offer an inspiring, yet a complex and under-explored research area. The purpose of this paper is to investigate the critical factors that affect UK’s housing prices.

The authors utilize the recently developed nonlinear ARDL approach of Shin et al. (2014) over the period 1969–2016.

The authors find that both the long-run and short-run impact of the price-to-rent (PTR) ratio and credit-to-GDP ratio on house prices (HP) is asymmetric whilst ambiguous results are established for mortgage rates, industrial production and equities. Apart from the novel framework of analysis, this study also establishes a positive association between HP and the PTR ratio which suggests a speculative behaviour and could imply the formation of a housing bubble.

It is the first study for the UK housing market that explores the underlying fundamental relationships by looking at nonlinearities hence, allowing HP to be tied by asymmetric relationships in the long as well as in the short run. Modelling the inherent nonlinearities enhances significantly the understanding of UK housing market which can prove useful for policymaking and forecasting purposes.

This paper investigates the empirical relationship between money, real income, interest rates, inflation and expected exchange rate, and examines the constancy of this relationship, especially in the light of financial reform, deregulation of financial markets and financial crises in Turkey. The estimation results show that expected exchange rate is statistically significant in the money demand function, indicating existence of currency substitution in Turkey. The dynamics of money demand is important, the inflation and income effects are much smaller in the short‐run than long‐run. The results also reveal that the demand for money in Turkey is stable, despite the economic reforms and financial crises.

The present study empirically examines the impact of coronavirus disease 2019 (COVID-19) and policy uncertainty on stock prices in India during the COVID-19 pandemic.

To this end, the authors use the daily data by applying the autoregressive distributed lag (ARDL) model, which tests the short- and long-run relationship between stock price and its covariates.

The study finds that increased uncertainty has adverse short- and long-run effects on stock prices, while the vaccine index has favorable effects on stock market recovery.

From investors' perspectives, volatility in the Indian stock market has negative repercussions. Therefore, to protect investors' sentiments, policymakers should be concerned about the uncertainty induced by the COVID-19 pandemic and similar other uncertainty prevailing in the financial markets.

This study used the news-based COVID-19 index and vaccine index to measure recent pandemic-induced uncertainty. The result carries some policy implications for an emerging economy like India.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2023-0244