Search results

This paper aims to empirically examine the influence of macroeconomic and socioeconomic factors on improving financial inclusion in India, with a specific focus on two distinct indicators of financial inclusion.

This study has used a time-series data set covering the years 1996 to 2022, using a nonlinear autoregressive distributed lag methodology. This approach allows for the examination of both short- and long-run effects of key macroeconomic and socio-economic indicators, including GDP per capita growth, remittance inflows and the income share held by the lowest 20% of the population on the growth of two financial inclusion indicators: the number of commercial bank branches and ATMs per 100,000 adults.

Model-1 investigates how commercial bank branch growth affects financial inclusion. Positive remittance inflow growth and a rise in the income share of the bottom 20% both lead to increased financial inclusion in both the short and long term, with the effects being more pronounced in the long run. Conversely, negative effects of remittance inflow growth and a decline in GDP per capita growth lead to reduced financial inclusion, primarily affecting the long run. Focusing on ATM growth, Model-2 reveals that positive remittance inflow growth has the strongest impact on financial inclusion in the short term. While income share growth for the bottom 20% and GDP growth also positively influence financial inclusion, their effects become significant only in the long run. Conversely, a decline in GDP per capita growth hinders financial inclusion, primarily affecting the short run.

This study fills a gap in research on macroeconomic and socioeconomic factors influencing financial inclusion in India by examining the impact of GDP per capita growth, remittance inflows and the income share held by the lowest 20% of the population, an area relatively unexplored in the Indian context. Second, the study provides comprehensive distinct results for different financial inclusion indicators, offering valuable insights for policymakers. These findings are particularly relevant for policymakers working toward Sustainable Development Goal 8.10.1, as they can use the results to tailor policies that align with SDG objectives. Additionally, policymakers in other developing nations can benefit from this study’s findings to enhance financial inclusion in their respective countries.

House price fluctuations send vital signals to many parts of the economy, and long-term predictions of house prices are of great interest to governments and property developers. Although predictive models based on economic fundamentals are widely used, the common requirement for such studies is that underlying data are stationary. This paper aims to demonstrate the usefulness of alternative filtering methods for forecasting house prices.

We specifically focus on exponential smoothing with trend adjustment and multiplicative decomposition using median house prices for Sydney from Q3 1994 to Q1 2017. The model performance is evaluated using out-of-sample forecasting techniques and a robustness check against secondary data sources.

Multiplicative decomposition outperforms exponential smoothing at forecasting accuracy. The superior decomposition model suggests that seasonal and cyclical components provide important additional information for predicting house prices. The forecasts for 2017–2028 suggest that prices will slowly increase, going past 2016 levels by 2020 in the apartment market and by 2022/2023 in the detached housing market.

We demonstrate that filtering models are simple (univariate models that only require historical house prices), easy to implement (with no condition of stationarity) and widely used in financial trading, sports betting and other fields where producing accurate forecasts is more important than explaining the drivers of change. The paper puts forward a case for the inclusion of filtering models within the forecasting toolkit as a useful reference point for comparing forecasts from alternative models.

To the best of the authors’ knowledge, this paper undertakes the first systematic comparison of two filtering models for the Sydney housing market.

This research aims to examine the macro determinants that significantly affect financial development in the Middle East and North Africa (MENA) region, which could be used furtherly to play a major role in economic sustainability since one of the major driving forces for economic development is the financial development.

The significant determinants of financial development should be efficiently used by the MENA region countries for creating huge financial sector development and innovation, stimulating economic development in turn and leading to the completion of the cycle of development and sustainability. To achieve this study's objective, the researcher employed a quantitative method to develop an econometric model.

This model consisted of two Panel EGLS Cross-Section Random Effects Models (REMs) in which Domestic credit to the private sector as a percentage of GDP (?PCGDP?_it) and stock market capitalization ratio (?SMC?_it) were taken as the dependent variables. In addition, the independent variables included the corruption perception index, financial freedom (FF), political stability (PS) and trade openness (TO). The researcher extracted the data for the analysis from different databases including the World Bank, the Organization for Economic Cooperation and Development and the International Monetary Fund. Throughout the first – Panel EGLS Cross-Section Random Effects Model, it turned out that, while FF, TO and corruption index had a positive relationship with ?PCGDP?_it, PS had an adverse effect on ?PCGDP?_it. The second – Panel EGLS Cross-Section Random Effects Model showed that, while PS and TO had a positive effect on stock market performance, the corruption index and FF had an adverse effect on stock market performance.

Throughout the first – Panel EGLS Cross-Section Random Effects Model, it turned out that, while FF, TO and corruption index had a positive relationship with ?PCGDP?_it, PS had an adverse effect on ?PCGDP?_it. The second – Panel EGLS Cross-Section Random Effects Model showed that, while PS and TO had a positive effect on stock market performance, the corruption index and FF had an adverse effect on stock market performance.

This study aims to elucidate the dynamics of monetary and fiscal policy interactions in Brazil, focusing on the impacts of positive shocks in government consumption and interest rates. By comparing rational and behavioral agent responses, it clarifies how these frameworks influence gross domestic product (GDP), inflation, private and government consumption and nominal interest rates.

The study employs a new Keynesian dynamic stochastic general equilibrium (DSGE) model with Bayesian estimation from 2000Q1 to 2022Q4, capturing rational and behavioral behaviors with adjustments for Brazilian economic idiosyncrasies. Impulse response functions (IRF) assess the dynamic effects of policy shocks, providing a comparative analysis of the two frameworks.

Behavioral agents show greater initial sensitivity to policy shocks, causing more pronounced fluctuations in GDP, inflation and private consumption compared to rational agents. Over time, the behavioral approach leads to a more robust recovery, while the rational approach results in a quicker return to equilibrium but less pronounced long-term recovery. The study also finds fiscal policy can partially offset the negative impacts of monetary tightening, with a more delayed effect in the behavioral model.

This paper provides insights into the interplay between monetary and fiscal policies under different agent expectations, emphasizing the importance of incorporating behavioral elements into macroeconomic models to better capture policy dynamics in emerging markets.

The current study immerses itself in the realm of diversification prospects within a select group of preeminent global stock exchanges. Specifically, the study casts its discerning gaze upon the financial hubs of the United States, Hong Kong, Germany, France, Amsterdam and India. In this expansive vista of international financial markets, the present analytical study aims to unravel the multifaceted opportunities that lie therein for astute portfolio management and strategic investment decisions.

The study encompasses daily time series data spanning from 2019 to 2022. To assess the interconnectedness among these stock indices, advanced statistical techniques, including Johansen cointegration methods and vector autoregressive (VAR) models, have been applied.

The research outcomes reveal both unidirectional and bidirectional relationships between the Indian, Hong Kong and US stock exchanges, encompassing both short-term and long-term time frames. Interestingly, the empirical findings indicate the presence of diversification opportunities between the Indian stock exchange and the stock exchanges of Germany, France and Amsterdam.

These insights hold significant value for both Indian and international investors, including foreign institutional investors (FIIs), domestic institutional investors (DIIs) and retail investors, as they can utilize this knowledge to construct more effective and diversified investment portfolios by understanding the intricate interconnections between these prominent global stock exchanges.

This research undertaking aspires to bring coherence to a landscape rife with divergent interpretations and methodological divergences. We are poised to offer a comprehensive analysis, a beacon of clarity amidst the murkiness, to shed light on the intricate web of interconnections that underpin the world's stock exchanges. In so doing, we seek to contribute a seminal piece of scholarship that transcends the existing ambiguities and thus empowers the field with a deeper understanding of the multifaceted dynamics governing international stock markets.

The study aims to estimate the short- and long-run effects of monetary policy on residential property prices in South Africa. Over the past decades, there has been a monetary policy shift, from targeting money supply and exchange rate to inflation. The shifts have affected residential property market dynamics.

The Johansen cointegration approach was used to estimate the effects of changes in monetary policy proxies on residential property prices using quarterly data from 1980 to 2022.

Mortgage finance and economic growth have a significant positive long-run effect on residential property prices. The consumer price index, the inflation targeting framework, interest rates and exchange rates have a significant negative long-run effect on residential property prices. The Granger causality test has depicted that exchange rate significantly influences residential property prices in the short run, and interest rates, inflation targeting framework, gross domestic product, money supply consumer price index and exchange rate can quickly return to equilibrium when they are in disequilibrium.

There are limited arguments whether the inflation targeting monetary policy framework in South Africa has prevented residential property market boom and bust scenarios. The study has found that the implementation of inflation targeting framework has successfully reduced booms in residential property prices in South Africa.

This study aims to test the unemployment hysteresis hypothesis for Nordic countries by considering age and gender differentials at various frequencies.

First, the authors test the linearity of the unemployment series and apply appropriate unit root tests based on the linearity test results. The authors use these tests for both original and wavelet-decomposed unemployment rates.

The authors' findings indicate that the results obtained from the original and decomposed series differ. While the authors find evidence of unemployment hysteresis in the six unemployment rates in the short run, they observe supportive results for hysteresis in the three unemployment rates in the long run.

The authors take into account different age and gender groups. Furthermore, the authors propose a testing strategy for unemployment hysteresis that considers the nonlinearity and structural breaks in unemployment rates. Finally, the authors determine whether the unemployment hysteresis is valid at various frequencies.

The purpose of this study is to provide new insights into the relationship between fiscal policy and total factor productivity (TFP) while accounting for several economic and econometric issues of the phenomenon like non-stationarity, fiscal feedback effects, persistence in productivity, country heterogeneity and unobserved global shocks and local spillovers affecting heterogeneously the countries in the sample.

The paper is empirical. It builds an Error Correction Model (ECM) specification within a dynamic heterogeneous framework with common correlated effects and models both reverse causality and feedback effects.

The results of this study highlight some new findings relative to the existing related literature. The outcomes suggest some relevant evidence at both the academic and policy levels: (1) the causal effects going from fiscal deficit/surplus to TFP are heterogeneous across countries; (2) the effects depend on the time horizon considered; (3) the long-run dynamics of TFP are positively impacted by improvements in fiscal budget, but only if the austerity measures do not exert slowdowns in aggregate growth.

The main originality of this study is methodological, with possible extensions to related phenomena. Relative to the existing literature, the gains of this study rely on the way econometric techniques, recently proposed in the literature, are adapted to the economic relationship of interest. The endogeneity due to the existence of reverse causality is modelled without implying relevant performance losses of the models. Moreover, this is the first article that questions whether the effects of fiscal budget on productivity depend on the impact of the former on aggregate output growth, thus emphasising the importance of the quality of fiscal adjustments.

This study presents time-series data estimations on the association between the indicators of institutional environment and inward foreign direct investment (FDI) in India utilizing a comprehensive data set from 1996 to 2021.

The study employs the nonlinear autoregressive distributive lag (NARDL) model. The asymmetric ARDL framework evaluates the existence of cointegration among the factors under study and highlights the underlying nonlinear effects that may exist in the long and short run.

The significance of coefficients of negative shock to “control of corruption” and positive shock to “rule of law” is greater when compared to “government effectiveness, regulatory quality, political stability/absence of violence.” The empirical outcomes suggest the positive influence of rule of law, political stability and government effectiveness on FDI inflows. A high “regulatory quality” is observed to deter foreign investment. The “voice and accountability” index and negative shocks to the “rule of law” are exhibited to have no substantial impact on the amount of FDI that the country receives.

This study empirically examines the institutional determinants of FDI in India for a comprehensive period of 1996–2021. The study's findings imply that quality of the institutional environment has a significant bearing on India's inward FDI.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0375

This study investigates the impact of board funds, banking credit, and economic development on food production in the context of South Asian economies (India, Pakistan, Bangladesh, Sri Lanka, and Nepal).

This study used data from the World Development Indicators covering the years 1991–2019. To investigate the relationship between the variables of the study, we employed the panel unit root test, panel cointegration test, cross-sectional dependence test, fully modified least squares (FMOLS), and panel dynamic least squares (DOLS) estimators.

The empirical results indicate that board funding significantly increase food production; however, banking credit had a negative impact. Furthermore, the findings indicate that economic development, Arable land, fertilizer consumption, and agricultural employment play a leading role in enhancing food production. The results of the Dumitrescu-Hurlin causality test also show substantiated the significance of the causal relationship among all variables.

South Asian countries should prioritize board funding, bank credit, and economic development in their long-term strategies. Ensuring financial access for farmers through micro-credit and public bank initiatives can spur agricultural productivity and economic growth.

This study is the first to combine board funding, banking credit, and economic development to better comprehend their potential impact on food production. Instead of using traditional approaches, this study focuses on these financial and developmental aspects as critical determinants for increasing food production, using evidence from South Asia.