Search results

The purpose of this paper is to focus on modeling economy growth with indicators of knowledge-based economy (KBE) introduced by World Bank for a case study in Iran during 1993-2013.

First, for grouping and reducing the number of variables, Tukey method and the principal component analysis are used. Also for modeling, 67 per cent of data is used for training in the two approaches of ARDL bounds testing and gene expression programming (GEP) and 33 per cent of them for testing the models. Then, the result models are compared with fitness function and Akaike information criteria (AIC).

The GEP model with fitness 945.7461 for training data and 954.8403 for testing data from 1000 is better than ARDL bounds testing model with fitness 335.5479 from 1000. In addition, according to model comparison tools (AIC), the GEP model has an extremely larger weight in comparison with ARDL bounds model. Therefore, the GEP model is introduced for future use in academia.

Knowledge and information is one of the most basic sources of wealth in economists’ sight. Thus, using KBE indicators appears essential in economic growth regarding daily progress in knowledge processes and its different theories. It is also extremely important to determine an appropriate model for KBE indicators which play a highly important role in the allocation of the economic resources of the country in an optimal manner.

This paper introduced a novel expression for economy growth using KBE indicators. All the data and the indicators are extracted from Word Bank service between 1993 and 2013.

The study aims to evaluate the long- vs short-run relationships between crops' production (output) and crops' significant inputs such as land use, agricultural water use (AWU) and gross irrigated area in India during the period 1981–2018.

The study applied the autoregressive distributed lag (ARDL) bounds testing approach to estimate the co-integration among the variables. The study uses the error correction model (ECM), which integrates the short-run dynamics with the long-run equilibrium.

The ARDL bounds test of co-integration confirms the strong evidence of the long-run relationship among the variables. Empirical results show the positive and significant relationship of crops' production with land use and gross irrigated area. The statistically significant error correction term (ECT) validates the speed of adjustment of the empirical models in the long-run.

The study suggests that the decision-makers must understand potential trade-offs between human needs and environmental impacts to ensure food for the growing population in India.

For a clear insight into the impact of climate change on crops' production, the current study incorporates the climate variables such as annual rainfall, maximum temperature and minimum temperature. Further, the study considered agro-chemicals, i.e. fertilizers and pesticides, concerning their negative impacts on increased agricultural production and the environment.

This study aims to assess how real income, oil prices and gold prices affect housing prices in China from 2010 to 2021.

This study uses a novel bootstrap autoregressive distributed lag (ARDL) testing to empirically analyze the short and long links among the tested variables.

The ARDL estimations demonstrate a positive impact of oil price shocks and real income on housing market prices in both the phrases of the short and long run. Furthermore, the results reveal that gold price shocks negatively affect housing prices both in the short and long run. The result can be attributed to China’s housing market and advanced infrastructure, resulting in a drop in housing prices as gold prices increase. Additionally, the prediction of housing market prices will provide a base and direction for housing market investors to forecast housing prices and avoid losses.

To the best of the authors’ knowledge, this is the first attempt to analyze the effect of gold price shocks on housing market prices in China.

Financial market holds superior information that can give insights into the future trajectory of economic growth. Further, identifying sectors that hold the key to future economic growth is important for all economies, but particularly relevant to emerging markets. However, unlike existing studies, the paper provides new insights into the forward-oriented nexus between financial markets and economic growth.

This paper takes a forward-looking approach of using financial market information to predict future economic growth. The authors use ARDL modeling approach to predict economic growth using the information from stock market sectoral returns.

The authors find that sectoral stock returns significantly improve economic growth forecasts. However, the forecasting superiority is not uniform across sectors and horizons. Auto, consumers' spending, materials and realty sectors provide the most forecasting gains. In contrast, banking, capital goods and industrial sectors provide superior forecasts, but only at horizons beyond one year. The authors also find that the forecast superiority of sectors at longer horizons is inversely related to volatility.

Research highlights the need for sector-focused policy actions in driving economic growth. Further, the findings of the paper identify sectors that drive short-, medium- and long-term economic growth.

There is a significant heterogeneity among different sectors and across horizons in predicting economic growth. Results suggest that targeted policy actions in sectors like materials, metals, oil and gas, and realty are key in driving economic growth. Further, policies geared toward the grassroots industries are at least as beneficial as the large-scale industries. Evidence also suggests the need for an active fiscal policy to address infrastructural bottlenecks in primary industries like basic materials and energy. Evidence nevertheless does not undermine the role of monetary policy actions.

Unlike any paper till date, the innovation of the paper is that it takes an ARDL modeling approach to measure stock market sectoral returns' ability to forecast economic growth several months ahead in the future. Though the paper considers the Indian case, the innovation and contribution extents to the entire field of economic studies.

This study aims to develop a parsimonious model to estimate US aggregate hotel industry revenue using domestic trips, consumer confidence index, international inbound trips, personal consumption expenditure and number of hotel rooms as predictor variables. Additionally, the study applied the model in six sub-segments of the hotel industry – luxury, upper upscale, upscale, upper midscale, midscale and economy.

Using monthly aggregate data from the past 22 years, the study adopted the auto-regressive distribute lags (ARDL) approach in developing the estimation model. Unit root analysis and cointegration test were further utilized. The model showed significant utility in accurately estimating aggregate hotel industry and sub-segment revenue.

All predictor variables except number of rooms showed significant positive influences on aggregate hotel industry revenue. Substantial variations were noted regarding estimating sub-segment revenue. Consumer confidence index positively affected all sub-segment revenues, except for upper upscale hotels. Inbound trips by international tourists and personal consumption expenditure positively influenced revenue for all sub-segments but economy hotels. Domestic trips by US residents added significant explanatory power to only upper upscale, upscale and economy hotel revenue. Number of hotel rooms only had significant negative effect on luxury and upper upscale hotel sub-segment revenues.

Hotel operators can make marketing and operating decisions regarding pricing, inventory allocation and strategic management based on the revenue estimation models specific to their segments.

It is the first study that adopted the ARDL bound approach and analyzed the predictive capacity of macroeconomic variables on aggregate hotel industry and sub-segment revenue.

The British government takes equity issues formally into account in its appraisal of social projects and policies. However, evidence on which the measured distributional welfare weights are based is neither broad enough nor sufficiently reliable. This paper seeks to address these issues by considering a wider body of evidence.

An important component of the welfare weight measure advocated by HM Treasury is the elasticity of marginal utility of consumption (e). A critical review of existing evidence on e is provided with a view to establishing priority areas for further research. New measures of e are presented based on revealed social values as indicated in specific government policies relating to both foreign aid and proposed income‐related fines for offences. Behavioural evidence based on demand analysis using a co‐integration approach is also presented.

The results for e are sensitive to the estimation approach adopted. While the evidence based on a revealed social values approach including modified tax‐based results suggests that e is close to unity, the measure currently used by HM Treasury, demand analysis suggests an e value close to 1.5. The evidence based on lifetime consumption behaviour is sensitive to model specification and needs updating.

Modified tax‐based findings on e are presented along with new evidence based on alternative revealed social values approaches. The new evidence from demand analysis is based on an Autoregressive Distributed Lag (ARDL) approach to co‐integration. This paper will be of interest to academics specialising in welfare economics and to practitioners involved in social project appraisal.

Personal remittance and ready-made garments (RMG) export incomes have emerged as the largest source of foreign income for Bangladesh's economy. The study investigates their impact on income inequality and gross domestic product (GDP) as a control variable, using time-series yearly data from 1983 to 2018.

It employs the Autoregressive Distributed Lag (ARDL) estimation and the Toda-Yamamoto (T-Y) causality approach. The ARDL estimation outcomes confirm a long-run association among the above variables and validate the autoregressive characteristic of the model.

Personal remittances positively contribute to reducing the income gap among the people of the society and declining income inequality. In contrast, RMG export income and economic growth contribute to further income inequality. The T-Y causality analysis follows the ARDL estimation outcomes and authenticates their robustness. It reveals a feedback relationship between remittance inflow and the Gini coefficient, unidirectional causalities from RMG export income to income inequality and economic growth to income inequality.

The finding has important policy implications to limit the income gaps between low and high-income groups by channeling incremental income to the lower-income group people. The policymakers may facilitate further international migration to attract further remittances and may upgrade the minimum wage of the RMG workers.

The study is original. As far as the authors' knowledge goes, this is a maiden attempt to investigate the impact of personal remittances and RMG export income on income disparity in the case of Bangladesh.

The present study is a novel attempt to measure the impact of population growth, natural resource depletion, non-renewable energy consumption, growth of national income, remittances inflow and industrial output on carbon dioxide emissions in India during the period of 1980–2018.

Autoregressive distributive lag (ARDL) is used to achieve the objective. The application of FMOLS (fully modified ordinary least squares), DOLS (dynamic ordinary least squares) and CCR (canonical cointegrating regression) techniques illustrate statistical robustness.

The long-run ARDL results confirm that increase in population, national income and energy consumption have a positive and significant impact on pollution levels in India. In contradiction to this, long run results further reveal that the increase in natural resource depletion, industrial output and remittances inflow have insignificant and negative impact on pollution levels in India. Further, the empirical findings did not find any evidence for the applicability of the environmental Kuznets curve (EKC) in India during the study period.

The study is confined to only a few important determinants of CO₂ emissions in India. However, there is a large chunk of studies that have incorporated other determinants of CO₂ emissions. Specifying a few determinants of CO₂ emissions in India is itself a lacuna in the present study. Moreover, taking the time period from 1980 to 2018 is also one of the limitations of the study.

Plenty of research has been devoted to the causal relationship between the environment and its various determinants. However, not much attention has been paid to investigating the association between population growth, natural resource depletion, energy consumption, GDP per capita, remittances inflow, industry and carbon dioxide emissions in India. Since, CO₂ emissions are one of the widely accepted and applied emissions in EKC applications, which the present study intends to test. Moreover, the study employs advanced econometric techniques including ARDL framework, FMOLS, DOLS and CRR methodologies to achieve robust results. Such an investigation will potentially allow policymakers to frame efficient environmental and fiscal policies to achieve the desired results.

The continuous increase of CO₂ emissions in India has compelled policy makers to prioritize this issue as soon as possible and formulate national environmental policy for reducing the share of carbon dioxides emissions in climate change. The study could constitute the focus of future research.

The “resource curse phenomenon” has received a lot of attention from researchers; however, there has not been any sound explanation to back this phenomenon since the main reason why natural resource should restrain economic growth instead of boosting economic growth remains unanswered. This paper contributes to literature on “resource curse hypothesis” by examining the role of government effectiveness in influencing the impact of gas resource rent on economic growth.

The study adopted the Cobb-Douglass production and incorporated gas resource rent, institutional quality (government effectiveness), inflation and exchange rate as additional variables that influences total output (gross domestic product). The author estimated the empirical form of the Cobb-Douglass production using autoregressive distributed lag model (ARDL) and Toda and Yamamoto (1995) as the main estimation strategies while other time series approaches were used as a robustness check.

The estimates from the ARDL short-run and the long-run dynamics suggest that the direct impact of gas resource rent on economic growth was positive but not statistically significant. At the same time, the interacting of gas resource rent and government effectiveness showed a positive and statistically significant effect of nearly 0.4123 and 0.8724 on economic growth in the long run and short run, respectively. The results from the Toda and Yamamoto (1995) also indicated that economic growth has a strong influence on gas resource rent while government effectiveness drives economic growth and not vice versa.

The findings from this study imply that government effectiveness plays a crucial role in averting the “resource curse phenomenon”. Hence, improving government effectiveness and efficiency through minimizing corruption among state institutions would be imperative in curbing the “resource curse phenomenon” in developing countries.

The influential role of government effectiveness on the relationship between gas resource rent on economic growth is examined.

The purpose of this paper is to empirically investigate the money demand function for five Southeast Asian countries, viz. Malaysia, Thailand, Singapore, the Philippines, and Indonesia.

The ARDL modeling approach is employed because of its ability to incorporate both I(0) and I(1) regressors.

The results reveal that real M2 aggregate, real expenditure components, exchange rate, and inflation rate are cointegrated for Malaysia, the Philippines, and Singapore. The statistical significance of real income components suggests the bias of using single real income variable in money demand (M2 aggregate) specification of both short‐ and long‐run. The CUSUM and CUSUMSQ tests show that the estimated parameters are stable for the five Southeast Asian economies, except for Indonesia which is based on short‐run specification.

These findings are important for policy makers in formulating monetary policy.

Besides conventional determinants of money demand such as exchange rate and interest rate variables, this study considers the major components of final expenditure (GDP) – final consumption expenditures (private and government sectors), expenditures on investment goods, and exports as scale variables.