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This paper investigates the nature of the associations and the potential existence of both short-run and long-run relationships between the stock market indices of Morocco, France, Germany, the United Kingdom, China, and the United States from January 2014 to January 2024. The purpose of analyzing dynamic interconnections and contagion effects is to determine how the stock markets of these countries influence and relate to each other. The study employs a time series Vector Error Correction Model (VECM) approach, incorporating stationarity, cointegration, and Granger causality tests. Additionally, the Impulse Response Function (IRF) is used to analyze the response of variables to shocks. The bivariate Granger causality test reveals significant causal influences: from France, Germany, and the USA to Morocco; from the USA to the DAX and France; and from the UK to Germany. After establishing the Granger causal relationships, long-run and short-run relationships are further examined. Using the Johansen multivariate cointegration approach, the study suggests a long-term equilibrium among the six stock market indices over time. The short-run adjustments are analyzed using the VECM, which reveals that adjustments in the CAC 40, DAX, and MASI tend to correct deviations from equilibrium, indicating a tendency to move towards equilibrium. For the FTSE 100, S&P 500, and SSEC, the VECM captures the speed and direction of adjustments as these indices respond to short-term disruptions and work towards restoring equilibrium. The findings underscore the importance of closely connected global stock markets, which means that international regulators must coordinate their efforts to reduce the risks of contagion. Policymakers should prioritize improving financial stability through integrated frameworks considering short-term disruptions and long-term equilibrium trends.

Foreign direct investment (FDI) is often seen as a key driver of economic growth and development, but its impact on income distribution remains still inconclusive. Thus, this served as a motivation to examine how foreign direct investment (FDI) influences income inequality in Kosovo, a country with a young population and high unemployment that shapes its economic and social patterns. The study focuses on the period from 2009 to 2019, using a Vector Error Correction Model (VECM) to explore the long-term relationship between FDI and income inequality, measured by the Gini coefficient. The findings reveal that FDI inflows have a significant impact on income inequality in Kosovo. This research tries to fill the gap in the literature by analyzing Kosovo's unique socioeconomic, political, and demographic context. The results provide insights for policymakers seeking strategies to attract investment and promote economic growth while addressing income disparities. [ABSTRACT FROM AUTHOR]

This research was conducted to determine the short-term and long-term effects of inflation, exchange rates, interest rates, and the money supply on economic growth in Indonesia from 2009 to 2022 using the Vector Error Correction Model (VECM) method. The VECM method is used to analyze the interaction between these variables over different time horizons, offering valuable insights into their respective roles in influencing economic growth. The results show that in the short run, the exchange rate variable does not have a significant effect on the economic growth, while in the long run the interest rate variable has a positive impact on the economy with a negative coefficient value. The short run variable interest rates do not have significant effects on the growth of the economy, but in the longer run interest rates have an important effect on growth. In conclusion, the effect of exchange rates on the Indonesian economy is still a controversial research topic. The findings enhance the current literature on macroeconomic policy and provide a foundation for policymakers to design more effective economic strategies, especially in addressing the challenges posed by inflation and exchange rate volatility in both the short and long terms. [ABSTRACT FROM AUTHOR]

Purpose This study examined the effect of the risk-free rate of return and other macroeconomic variables on the stock prices of firms listed on the Pakistan Stock Exchange-100 (PSX). Methodology The data from 2013 to 2023 was collected from the Karachi Stock Exchange website, Yahoo Finance, and the State Bank of Pakistan’s website. The main techniques for data analysis were Johansen-Juselius (J.J.) cointegration and the Vector Error Correction Model (VECM). Findings The findings show that a risk-free rate significantly impacts stock returns in the long run. Further, foreign direct investment (FDI) has a significant positive impact on the stock return of listed companies. The cointegration result indicates a long-run association among all the selected variables. Moreover, the results of the VECM show that the error correction term (ECT) in VECM (-1) is negative (-0.150) and significant. The negative coefficient of 0.150 indicates that deviations from the long-run equilibrium will be corrected at a speed of 15% per period, approximately within 7 months. Conclusion The study concludes that stock prices and macroeconomic indicators have long-run associations. Conversely, changes in the risk-free rate of return by the government or the banking sector have the opposite effect on stock prices. This study assists government officials, stock market participants, and policymakers in determining the profitability of the National Savings Scheme and Banks.

The goal of this article is to analyze the impact of household savings and household loans on gross fixed capital formation in Bulgaria. The research is carried out on the basis of quarterly data for the 2010-2023 period. The sources of the data are the National Statistical Institute (NSI) and the Bulgarian National Bank (BNB). The algorithm of the empirical research includes: checking for stationarity by means of the extended Dickey-Fuller test; determining the optimal lag of the model; Johansen cointegration test; specification of vector error correction model (VECM). As a result of the research, it has been established that there is a long-term relationship between the variables gross fixed capital formation, household savings and household loans. At the same time, about 39.2% of the disequilibrium between the studied variables in a certain quarter is compensated in the next one. [ABSTRACT FROM AUTHOR]

This research discusses the impact of climate change on inflation, as well as the limitations of existing econometric models in incorporating multiple climate-related variables in determining inflation. To address this limitation, the study introduces the climate condition index (CCI) for 153 countries covering the 1901–2021 period to investigate the relationship between climate and agricultural inflation. This index represents a weighted average of various climate variables, including cloud coverage, temperature, precipitation, vapor pressure, and wet day frequency. The weights are the long-term cointegration coefficients of the climate variables concerning the Agricultural Producer Price Index (APPI), which were obtained from separate vector error correction models (VECMs) constructed for each country. The study shows that climate change has a significant impact on agricultural prices globally, as evidenced by a median Granger causality p-value of 0.036 across 153 countries (CCI Granger causing APPI in 127 countries with a 90% confidence interval). The results align with panel vector autoregression and country-specific VECMs impulse responses, which indicate that climate conditions negatively impact agriculture in 142 countries, while 11 countries gain from climate change. The study's overall results suggest that climate change substantially affects agriculture globally. In light of the significant economic reliance on agricultural production, it is advisable for governments to consider the issue from the perspective of sustainable growth, while central banks should tackle it with a focus on the inflationary implications through green quantitative easing. [ABSTRACT FROM AUTHOR]

This study evaluates the trend and growth pattern of international tourism and analyzes the impact of tourism on the economic growth of Kerala for the past four decades from 1980 to 2019. The time series analysis employed in this study uses the secondary data on Net State Domestic Product (NSDP) of Kerala at constant prices, foreign tourist arrivals (FTA), and foreign exchange earnings (FEE) at constant prices, collected from various sources of the State and Central Government. The methodology of this study uses the Augmented Dickey Fuller (ADF) test for unit root, followed by the Johansen Cointegration test, the Vector Error Correction Model (VECM), and the Granger Causality test. The results of the analysis reveal the existence of a positive and significant unidirectional long‐run causality running from tourism to the economic growth of Kerala and a bidirectional causal relationship between tourism development and the economic growth in the short run. [ABSTRACT FROM AUTHOR]

Purpose: This study investigates the causal connections and both short-term and long-term associations among corruption, investment, unemployment, and per capita economic growth across twenty-two Asia-Pacific nations spanning from 2012 to 2020. Design/Methodology/Approach: The research employs Granger causality and Vector Error Correction Model methodologies to tackle the research inquiries. Findings: The empirical results unveil bidirectional causality between corruption and per capita economic growth, whereas the unemployment rate and per capita economic growth share a unidirectional relationship. Conversely, no causal linkage is found among the remaining variables. In the short run, corruption has no significant impact on per capita economic growth and unemployment but does significantly and adversely affect the investment rate. On the other hand, in the long run, corruption significantly and negatively influences per capita economic growth. The investment rate and unemployment, in the long term, exhibit a substantial and positive influence on per capita economic growth. [ABSTRACT FROM AUTHOR]

The business model and consequently, the bank's risk exposure significantly depends on the source of capital (Riabichenko et al., 2019). This research uses vector error correction model (VECM) data analysis to investigate the influence of capital adequacy ratio (CAR), non-performing loans (NPL), loan to deposit ratio (LDR) on the level of credit distribution at commercial banks in Indonesia. Using secondary data, research data was processed using the EViews 12 application with the research population being banking companies listed on the Indonesia Stock Exchange in 2019-2021. The research results show the variables CAR, NPL, and LDR have a significant effect on long-term credit distribution. In addition, the NPL variable significantly influences the credit distribution variable in the short term. The Granger causality test result shows that there is no two-directional causality relationship between the independent variables CAR, NPL, and LDR on the credit distribution variable. The results of this research are in accordance with financial intermediation theory, where the theory explains that savings and loans with high leverage can reduce the possibility of default (payment failure). [ABSTRACT FROM AUTHOR]

This study analyzes the effect of Gross Fixed Capital Formation (GFCF), Imports, Exports, and Government Expenditure of selected G20 member countries on Gross Domestic Product (GDP) using historical data from 1981 to 2021. The detailed analysis aims to explore the relationship between short-term and long-term causality that begins with examining and testing the degree of integration, Unit Root Test, Johansen cointegration test, and causality test. The Vector Error Correction Model (VECM) test results with a 95% confidence interval show that Gross Fixed Capital Formation causes Australia’s and South Africa’s long-term GDPs to have reached a balance point. In addition, Government Spending also causes the European Union’s Gross Domestic Product to achieve a balance point. Imports affect the GDP of the United States, China, and South Africa towards a balance point, and exports affect the GDP of Australia, China, and South Africa. The test results using VECM also conclude that GDP, GFCF, exports, and imports affect GDP growth in the short term. However, on the contrary, on the Australian continent, only GDP, GFCF, and imports which in the previous year had an impact on Australia’s GDP in the short term—concluded that differences in government policies in each country in regulating the economy could affect the causal relationship between the independent variable and GDP in the short and long term.

This study explores the link between financial inclusion and food security in Algeria from 2003 to 2022. Using a composite financial inclusion index and the Vector Error Correction Model (VECM), we analyse the data, subjecting it to various diagnostic tests. Surprisingly, our results reveal that financial inclusion (FI) has a significant and positive impact on undernourishment prevalence, indicating a negative effect on food security in both the short and long term. Likewise, food imports (FIM) contribute to higher undernourishment prevalence, implying a weakening of food security in the long-run. Conversely, unemployment rate (UEM) and food production (FOP) show no substantial long-term impact on food security, although UEM has an opposing effect in the short run, meaning it improves food security at the short term; which can be attributed to the informal economy and other State’s policies. Notably, income per capita (INCAPITA) negatively affects undernourishment prevalence, improving food security. These findings offer a nuanced understanding of the complex relationship between financial inclusion and food security in Algeria, emphasizing the need for multifaceted, context-specific policies to address the country's unique challenges.

This study investigated the long-run and short-run relationships between solid waste generation in Nigeria and two key climate variables: rainfall and temperature. Employing a Vector Error Correction Model (VECM) analysis on data from 1982 to 2022, then revealed counterintuitive findings. In the long run, lagged rainfall exhibits a negative association with solid waste (p < 0.05), potentially explained by increased waste decomposition in wetter conditions. Conversely, lagged temperature showed a positive association (p < 0.05), aligning with theories of increased consumption and economic activity in warmer periods. The shortrun analysis unveils a self-correcting mechanism in solid waste generation and a statistically significant negative impact of lagged temperature (p < 0.05), requiring further investigation. Based on these findings, the study proposed policy implications for waste management strategies and data collection, emphasizing the need for sustainable solutions in the context of climate change. [ABSTRACT FROM AUTHOR]

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In this paper, we are interested in modeling the dynamics of cutaneous leishmaniasis (CL) in Errachidia province (Morocco), using epidemiologic data and the most notable climatic factors associated with leishmaniasis, namely humidity, wind speed, rainfall, and temperature. To achieve our objective, we compare the performance of three statistical models, namely the Vector Auto-Regressive (VAR) model, the Vector Error Correction model (VECM), and the Generalized Linear model (GLM), using different metrics. The modeling framework will be compared with the Markov Switching (MSM) approach. [ABSTRACT FROM AUTHOR]

Understanding how international trade, FDI and human capital (FDI-HC and ET-HC) in the ICT industry affect Taiwan's stable economic growth between 2001 and 2020 is the main objective of this study. The empirical analysis method used in this study is mainly divided into two steps: First, it uses variables with reliability and authenticity as keywords for primary, data mining, and semantic network analysis (SNA). Second, it investigates the long- and short-term interactions between the variables using the vector error correction model (VECM). The results of data mining and SNA using FDI and ET as keywords reveal that terms connected to HC have high levels of centrality, clustering, and frequency. This finding implies that the variables FDI-HC and ET-HC are reliable and can be utilized as interaction variables. Moreover, FDI–HC and ET–HC exert positive short- and long-term influences on GDP, and ET–HC exerts strong mid- to long-term impacts on GDP, FDI–HC, and ET. [ABSTRACT FROM AUTHOR]

The goal of this study is to detect linear and nonlinear causal pathways toward climate change as measured by changes in global mean surface temperature and global mean sea level over time using a data-based approach in contrast to the traditional physics-based models. Monthly data on potential climate change causal factors, including greenhouse gas concentrations, sunspot numbers, humidity, ice sheets mass, and sea ice coverage, from January 2003 to December 2021, have been utilized in the analysis. We first applied the vector autoregressive model (VAR) and Granger causality test to gauge the linear Granger causal relationships among climate factors. We then adopted the vector error correction model (VECM) as well as the autoregressive distributed lag model (ARDL) to quantify the linear long-run equilibrium and the linear short-term dynamics. Cointegration analysis has also been adopted to examine the dual directional Granger causalities. Furthermore, in this work, we have presented a novel pipeline based on the artificial neural network (ANN) and the VAR and ARDL models to detect nonlinear causal relationships embedded in the data. The results in this study indicate that the global sea level rise is affected by changes in ice sheet mass (both linearly and nonlinearly), global mean temperature (nonlinearly), and the extent of sea ice coverage (nonlinearly and weakly); whereas the global mean temperature is affected by the global surface mean specific humidity (both linearly and nonlinearly), greenhouse gas concentration as measured by the global warming potential (both linearly and nonlinearly) and the sunspot number (only nonlinearly and weakly). Furthermore, the nonlinear neural network models tend to fit the data closer than the linear models as expected due to the increased parameter dimension of the neural network models. Given that the information criteria are not generally applicable to the comparison of neural network models and statistical time series models, our next step is to examine the robustness and compare the forecast accuracy of these two models using the soon-available 2022 monthly data. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate the human capital on economic growth impact in Romania. Variables have been selected according to an endogenous growth model basing on including the human capital in the Cobb-Douglas production function (Lucas, 1988). As all over usual, here gross domestic product (GDP) will be the endogenous of gross fixed capital formation (GFCF, as physical capital stock), employment (as labour), life expectancy and secondary enrolment rate(as proxies for human capital). We also use expenditure in research and development (R&D) sector (as its percentage in GDP), as control variable. Once our model developed, variables are found as integrated of order one (1) and co-integrated, here allowing a vector error correction model (VECM) for estimation. This will be a system of six equations covering a 25 years (1995-2019) interval for Romania. A long-term relation comes out of our empirical findings, as similarly to Wang (2016), so the GDP growth sees itself determined by: secondary school enrolment, life expectancy(i.e. for human capital), R&D expenditure and labour. Short run causalities have not been found significant for this model

Monetary policy is closely related to activities to achieve economic growth, which eventually gives welfare to the community. This study aims to analyze the description of the transmission flow of financing channels, the effect of monetary policy instruments, and their effectiveness to achieve economic growth. The variables used are Islamic Banking Finance (FIN), return of Sharia Bank Indonesia Certificate (SBIS), return of PUAS, and Industrial Production Index (IPI). This study used Vector Error Correction Model (VECM) to determine short- and long-term relationships using the time series data. First, the result of the study showed that the transmission flow could not be identified clearly, because the flow stopped in FIN, and it could not affect IPI, according to the Granger Causality test. Second, the result of VECM estimation showed that all variables only affected long term period and did not affect the short-term period. Third, monetary policy transmission of Islamic banking financing channel was not effective enough, which was proven with the result of IRF simulation, which showed that the effect of shock on financing channel variable (FIN) towards IPI was subsided and stable in the 10th period later. Meanwhile, the result of the FEVD simulation showed that the financing channel variable (FIN) only gave a contribution of as much as 0.14 percent towards IPI. The contribution and policy implications are also discussed in this study.

The type of relationship that can link energy with economic growth plays a major role in determining the macroeconomic policy of a country. Therefore, several studies have been carried out to derive econometric models to link energy consumption with gross domestic product (GDP). However, in these studies the energy consumption has been used in its global term, while this consumption includes all the economic sectors that use energy (residential, industry, transport, agriculture). Therefore, the objective of this work is to examine this relationship between the gross domestic product (GDP) and energy consumption but disaggregated by sector (residential, transport and industrial). And to validate our model we have taken the case of Morocco during the period 1997-2019, in order to draw the impact of each sector on economic growth. In order to test this causality, a Vector Error Correction Model (VECM) is applied instead of a Vector Autoregressive Model (VAR), using the Johansen cointegration technique. The results obtained showed that in the long run energy consumption by the transportation and residential sectors has a positive impact on GDP, while that of households has a negative impact.

The aim of this study is to estimate the linkages between the actual guaranteed price of wheat and its yield in Tehran province during 2000-2018 by considering the dummy variables of development programs of the Islamic Republic of Iran. This study used the Augmented Dickey-Fuller stationary test, the Johansen Cointegration test, and the Vector Error Correction Model (VECM) to achieve the aim. The results show that the actual guaranteed price of wheat and its yield are of grade I (1), and based on the Johansen test, the long-run relationship between them is confirmed. The results of the VECM model show that the actual guaranteed price of wheat has a positive and significant effect on wheat yield in Tehran province. By a one percent increase in the actual guaranteed price of wheat, the yield will increase 1.07 percent. Also, the coefficient of vector error correction indicates that if in the short-run occurs a sudden shock to the actual guaranteed price of wheat, it will take about 2 periods to adjust the effect of this shock. Considering that the yield is elastic to the actual price of wheat, suggesting to estimate the guaranteed price of wheat, based on actual price (deflated) in the purchasing policy, can encourage the improvement of wheat yield in the country's agricultural lands. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the effect of non-cash payment transactions on economic growth in Indonesia and to see the responses from supporting variables, such as the velocity of money and the price of transactions. This study involves a Vector Error Correction Model (VECM) analysis tool, using monthly time series data during 2009: 1 – 2017: 12. The results show that the payment instrument affects economic growth, especially the Card-Based Payment Instrument (CBPI). In addition, there are changes to the velocity of money and prices caused by the increase in the use of non-cash payment instruments. Keywords: Electronic Payment, Economic Growth, Vector Error Correction Model (VECM) JEL: E4; C51

The weakening of the Rupiah against the US dollar has encouraged Bank Indonesia to issued Bank Indonesia Regulation (Peraturan Bank Indonesia - PBI) No. 17/3/2015. The research aimed to analyze the factors that affected the Rupiah exchange rate, the effect of PBI No. 17/3/2015 on the movement of the Rupiah exchange rate, and the behavior of exchange rate movement to the shocks on the variables that influenced it. The research applied secondary data, namely monthly data from January 2008 to April 2019 taken from reliable sources such as National Development Planning Agency (Bappenas), Bank Indonesia (BI), and Statistics Indonesia (BPS). It was explanatory research with a quantitative approach. The studied data were processed with the Vector Error Correction Model (VECM) method to identify long and short-term effects. The results of the long-term equation show that export-import has a negative effect on the exchange rate. Similarly, inflation has no significant effect on the exchange rate. Then, the money supply has a significantly negative effect on the exchange rate. However, the interest rate of Bank Indonesia positively affects the exchange rate. Next, the implementation of PBI No. 17/3/2015 has a significant and positive impact on the exchange rate. Last, the crisis condition does not affect the changes in exchange rates.

In this paper, we are interested in modeling the dynamics of cutaneous leishmaniasis (CL) in Errachidia province (Morocco), using epidemiologic data and the most notable climatic factors associated with leishmaniasis, namely humidity, wind speed, rainfall, and temperature. To achieve our objective, we compare the performance of three statistical models, namely the Vector Auto-Regressive (VAR) model, the Vector Error Correction model (VECM), and the Generalized Linear model (GLM), using different metrics. The modeling framework will be compared with the Markov Switching (MSM) approach.

We propose a novel framework to examine relationships among drivers of economic growth in Southeast Asia, a region poised to become a significant manufacturing destination. However, unbalanced economic growth among countries poses risks to multinational companies considering offshoring decisions. Our two-stage framework uses multi-way cointegration analysis and a vector error correction model (VECM) to investigate critical drivers of economic growth. We apply a QVAR model to evaluate dynamic connectedness and spillover effects of offshoring decisions. Using World Bank data, our results show that Southeast Asian countries are interconnected through complex relationships featuring multi-way cointegration and dynamic connectedness, informing evidence-based policy. • A conceptual framework to identify spillover effects on drivers of economic growth. • A multi-way co-integration, causality, and dynamic connectedness analysis. • A quantile vector autoregressive (QVAR) model to assess dynamic connectedness. • A subset of Southeast Asian countries have spillover effects on each other. • A sensitivity analysis of spillover effects across a sequel of quantiles. [ABSTRACT FROM AUTHOR]

This paper aims to empirically analyze the relationship between natural disaster, health spending, urban population, gross fixed capital formation, and gross domestic product (GDP) per capita for lower middle-income countries. The data cover the period 2000–2019. The methodological approach used is based on Granger causality and Vector Error Correction Model (VECM) procedures. Empirical result reveals that GDP per capita and health spending are correlated positively with urban population. The results also indicate that there is a one-way relationship running from natural disaster to GDP per capita and from natural disaster to health spending in short and long run, while two-way relationship between health spending and urban population in short term. In long run, there is two-way relationship between GDP per capita and health spending. [ABSTRACT FROM AUTHOR]

This research aims to analyze the effect of electronic payment systems based on credit cards, debit cards, and electronic money, as well as macroeconomic variables namely the money supply (M1), price level, and velocity of money towards real gross domestic product as a proxy for economic growth. The estimation carried out in this journal uses the Vector Error Correction Model (VECM) with period time series data of 2010:1-2018:12. The results of the journal show that doing debit card and electronic money-based transactions has a significant positive effect on economic growth in Indonesia in the long run. Keywords : Electronic payment systems, electronic money, credit cards, debit cards, economic growth, Vector Error Correction Model (VECM) JEL : O470 C320

The dynamic IS equation implies that if the real interest rate is I(1), then so is the output growth rate with possible cointegration, and log output is I(2). This paper extends the Beveridge–Nelson decomposition to such a case, and develops a Bayesian method to obtain error bands. The method is valid whether log output is I(1) or I(2). The paper applies the method to US data to estimate the natural rates (or their permanent components) and gaps of output, inflation, interest, and unemployment jointly, and finds that allowing for cointegration gives much bigger estimates of the gaps for all variables. [ABSTRACT FROM AUTHOR]

The relationship between the housing market, stock market and macroeconomic variables has long been a topic of concern to both academics and practitioners. This paper examines the short-run dynamics and long-run relationships between the residential property price index and the stock market index and four selected macroeconomic variables in Hong Kong. The Johansen (1991) cointegration approach and the vector error correction model (VECM) approach are used to examine the monthly time series during the sample period from 2004 to 2019. Our results show that there is a cointegration relationship between the residential property price index and the stock market index and selected macroeconomic variables. There is evidence that the Hang Seng Index, money supply (M3), total loans and unemployment rate are significantly associated with the residential property price index, while the consumer price index has no significant impact on the residential property price index in the short-run dynamics. Also, only the Hang Seng Index and two macroeconomic variables have a long-run cointegration relationship with the housing market. This is the first attempt to shed light on both short-run and long-run relationships between two capital markets and macroeconomic variables in the context of Hong Kong. Our findings provide important implications for relevant government departments to stabilise the housing market and help practitioners form effective investment strategies. [ABSTRACT FROM AUTHOR]

This study explores the dynamic relation between economic growth and stock market depth in the presence of three more macroeconomic indicators such as exchange rate, inflation and interest rate of Bangladesh. We use Johansen and Juselius (1990) test of co-integration and Vector Error Correction Model (VECM) to detect the possible short-run and long-run causal relation among the selected economic forces. The results of the study evidence that the lagged error-correct term of GDP (i.e., the proxy of economic growth) is found statistically significant in all three models. This manifest that GDP tends to converge to its long-run equilibrium path in response to changes in its regressors. But we find a complex network of causal linkage between the variables in the short-run. The findings of this study are of particular interest and importance to policymakers, financial managers, financial analysts and investors dealing with the Bangladesh economy and the Bangladesh stock market.

The relationship between public expenditure and economic growth is obvious, but the direction of causality is not clear. This paper analyses the relative impact of the different components of public expenditure on economic growth. Specifically, this paper examines whether the level of government expenditure is managed to accelerate economic growth or whether government expenditure is used excessively, which may hurt domestic economy because of increased taxes and/or high government borrowing. The vector error correction method is applied to the annual time series data for India from 1983 to 2020 for testing the long- and short-run causality. The pair-wise Granger causality test indicates one-way causality moving from gross domestic product to total government expenditure, and from gross domestic product to government revenue showing that the growth of the economy leads to an increase in both government revenue and expenditure. The estimated error correction coefficient is significantly negative, indicating that the speed of adjustment between the short-run dynamics and the long-run equilibrium is about 0.03%. The results show a stable long-run relationship between public expenditure and economic growth. [ABSTRACT FROM AUTHOR]

The present article aims to analyse wage-labour productivity causalities in Croatia and Slovenia using cointegration methods based on monthly time series data of variables for labour productivity and real gross wages in tourism industry during the period December 1999–January 2020. The data vector is integrated by chain indices with the constant base January 2000 = 100. A stochastic trend and shocks are covered in the analysis. Shocks are linked to the European Union accession, and economic crisis following with overwhelmed tourist arrivals. The contribution of the research is two-fold. First, the equations for at most normal distributed variables of labour productivity and real wages in tourism are exposed. Three spatial cointegration relations confirm labour productivity integrity of the regional tourism market. Second, pair-wise causalities indicate one cointegrated vector for labour productivity, which drives real gross wages in tourism sub-industries. These results suggest that for a higher non-seasonal assessment of real gross wage, the labour productivity should rise, i.e. less workers, more robotization or more tourist arrivals with better quality solutions. These findings are at most important to be implemented after the COVID-19 infection crisis with expected restructurings and digital transformation in the tourism industry. [ABSTRACT FROM AUTHOR]

The paper examines the relative efficiency of gold Exchange Traded Funds (ETF) against spot gold and gold futures in the Indian scenario, using a series of conventional and threshold cointegration statistics. The results reveal that gold ETFs and spot gold, as well as gold ETFs and gold futures, converge together in the long run. Also, movements in spot prices and futures prices are found to lead those in ETF prices, thus, providing scope for executing profitable trading strategies in ETFs. The study further explores the probable reasons that might account for the relative inefficiency observed in ETF prices.

The introduction of the first tradable stock index futures of VN 30 is a very good signal showing that Vietnam is starting to have a high-level financial market, which brings many expectations about sustainable and safe development of its stock market. However, risk concerns of this type of derivative products have been raising with many claims since then. This article aims to provide empirical evidences to show if futures trading plays important role of price discovery and information transmission for spot market. Using daily data collected about VN 30 Index Futures, VN 30 Index, VN Index from August 10, 2017 to February 28, 2019, which is divided into three sub-periods (increase/decrease/recovery), the research verifies VN 30 Index Futures’ role of price discovery and information transmission by applying Vector Error Correction Model (VECM). Empirical findings show that there is a stable equilibrium relationship between the two series groups (including VN 30 Index Futures, VN 30 Index and VN 30 Index Futures and VN Index) during three sub-periods or spot and futures markets are integrated and synchronized. In particular, VN 30 Index Futures’ price discovery and information transmission are clearly seen when the market falls or does not change a lot.

Purpose – The purpose of this paper is to investigate both long-run and short-run dynamics among the software and services export, investment in information technology (IT) and GDP in India and to investigate the direction of the relationship among the given three macro-economic variables. Design/methodology/approach – The time series data have been taken to investigate the long-run relationship exists among the variables. Annual data were collected from the NASSCOM Annual Reports, Planning Commission of India and Reserve Bank of India during the period 1980–2016. Cointegration and vector error correction model have been used for analyzing the causal relationship among investment in IT, software exports and GDP in India. Findings – Cointegration results confirm that software and services export, investment in IT and GDP are cointegrated, implying that there exists the long-run equilibrium relationship among the given three macro-economic variables. Similarly, vector error correction mechanism Granger causality results hold that there is uni-directional long-run causality running from software and services export and investment in IT to GDP, implying that software and services export is an important determinant of economic growth in India. Research limitations/implications – The limitations of the paper are generalization of the results and proxy variable for IT investments. Practical implications – The paper has implications for the expansion of market concentration, diversification of software and service exports, and investments in R&D for increasing competitiveness of the industry in the global market. Originality/value – This paper focuses on originality in the analysis of the relationship among the given variables software exports, investment in the IT sector and GDP in India. All the work has been done in original by the authors and the work used have been acknowledged properly.

Does the growth in non-agricultural sectors spill over to the agricultural sector of an economy? There is limited evidence available on the issue for the developing world, especially for Pakistan which has undergone large structural changes since its independence. This study examined the impact of sectoral growth linkages on agricultural output of Pakistan for the period of 1960-2016. We have estimated an econometric model which incorporates inter-sectoral linkages of Pakistan economy using a Vector Error Correction Model (VECM). Our analysis revealed that the economy of Pakistan has shifted from an agricultural dominant economy to services-based economy during the past six decades. Results of VECM show that the industrial sector has a negative impact on the performance of agricultural output whereas services sector is influencing the output of agriculture sector positively in the long run. Short run results show that industrial sector is affecting the performance of agricultural output positively whereas services sector is influencing the output of agriculture sector negatively. Negative impacts of industry in the long run and services in the short run imply that agricultural sector should be given its due share in public investment and the role of middle man should be minimised at the time of sale of agricultural production in the markets.

From a financial perspective, decentralization is a transfer of resources from the central government to local governments. Fiscal decentralization policies can lead to more FDI attraction by increasing the share of provincial government funding to local infrastructure.Accordingly, the major purpose of this study is to evaluate the long run and short run impact of fiscal decentralization on FDI in Iran during the period 1992-2014. For this purpose the three indicators of fiscal decentralization of revenue, fiscal decentralization of expenditures, fiscal decentralization of autonomy power and also some control variables including inflation, exchange rate fluctuations, and degree of trade openness have been used.In order to estimate the model, the Johansen-Juselius method and Vector Error Correction Model (VECM) have been applied. Based on the results of the model, all three fiscal decentralization indicators increase FDI in the long run and in the short run. Therefore, providing the necessary conditions for the expansion of fiscal decentralization can help to promote the FDI level in Iran.Also, both in the long run and the short run, inflation and exchange rate fluctuations have a negative effect, and the degree of trade openness has a positive effect on FDI.

This study investigates the relationship between the probability of buying a house, industrial production index, and unemployment rate in Turkey. This paper tests whether there is a long-run relationship among above-stated variables by conducting a cointegration analysis and estimating a vector error correction model with a monthly data over the period between 2010:01 and 2019:12. While the test results suggest a negative and statistically significant relation between the probability of buying a house and the unemployment rate, the results suggest a positive but statistically insignificant relation with economic activity. Moreover, the deviation from the longrun equilibrium is corrected towards equilibrium by 24% within a period. [ABSTRACT FROM AUTHOR]

The direct impact of oil price and oil income fluctuations on government budgets and economy have been the general characteristics of oil exporting countries in recent decades. In Iran, the vast majority of government revenues are oil revenues. One of the main factors behind the volatility of oil revenues and trade balance in recent years has been economic sanctions. Given the importance of achieving the goals of internal and external economy stability in economic planning, in this study using a Vector Auto Regression (VAR) to investigate the effects of long-term fluctuations in oil revenues (The Hodrick-Prescott filter has been used to extract the cyclical component (fluctuating) of oil revenues) on the level of GDP and trade balance (respectively, as internal and external stability index) during the period 1992 to 2019 has been tested. The long-term relationship estimates indicate a negative relationship between oil revenue fluctuations and trade balance. The Vector Error Correction Model (VECM) has also been used to link the short-run volatility of variables to their long-run equilibrium values. The results of the long-term relationship estimation indicated that there is a significant relationship between the three trade balance and GDP and oil revenue fluctuations. Findings of the shock-response function also show that a sudden shock in the endogenous variables of the research increases the level of trade balance and this effect gradually decreases over time. On the other hand, the effect of the sudden shock of GDP changes indicates that this variable in the short run has a inverse relation and in the long run, has a direct with the trade balance. [ABSTRACT FROM AUTHOR]

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This paper examines the drivers of overall inflation and its component, food inflation in India using monthly data from April 1996 to March 2017. The analysis covers both WPI and CPI-IW measures of inflation along with their component food inflation. The study uses cointegration approach to identify the determinants of inflation in India. Empirical estimates of the study show that there is a long-run relationship between inflation and its determinants that include expected inflation, output gap, rate of growth of money supply, exchange rate, interest rate, fiscal deficit, minimum support prices, rainfall international oil and food prices. These determinants also Granger cause both the measures of inflation. The normalised generalised variance decompositions suggest that determinants of inflation, in descending order of importance include expected inflation, exchange rate, rate of growth of money supply and output gap and least variation in them is explained by interest rates. This analysis is found to be similar for both the measures of inflation. Thus demand factors such as exchange rate, rate of growth in money supply and output gap explain significant variation in both the measures. On the supply side global factors like international oil and food prices play a key role in determining both overall and food inflation in the economy. This has important policy implications in the light of new monetary framework where flexible inflation targeting is adopted by the central bank. Inflation management will become a more challenging task as inflation is also significantly determined by the supply side variables. The Reserve Bank of India's report of the Expert Committee to Revise and Strengthen the Monetary Policy Framework (ECRSMPF) reports that monetary policy has little or no effect on food and fuel inflation, which are the supply side factors. The report also finds that high inflation in food and fuel with strong secondary effects gets generalised in the system through inflation expectations. Shocks to food and fuel inflation have a high degree of persistence on household inflation expectations which leads to more prolonged effects on headline inflation. Hence, for better management of inflation dynamics, an analysis of the driving factors can help predict likely changes in inflation more efficiently and accurately. [ABSTRACT FROM AUTHOR]

This study aims to examine the impact of macroprudential policy particlarly the relaxing of Financing to Value (FTV) on property financing of Islamic bank in case of Indonesia. In addition, this paper also analyzes the impact of macroeconomic variables and specific factors on property financing in Islamic Bank. This paper collects data from the Bank Indonesia website, the Financial Services Authority (FSA) and the Central Statistics Agency (CSA) form January 2010 until April 2016. By employing Vector Error Correction Model (VECM), this study found that inflation and Industry Produce Index (IPI) have been positively influence on the property financing, while the BI rate and financing to deposit ratio (FDR) have been negatively influence on the property financing. While the relaxing of FTV policy also negatively influence on the property financing. Therefore, This study conludes that the policy have not effectived yet.