Vector Error Correction Model Framework Research Articles

Exploring the influencing factors of environmental deterioration: evidence from China employing ARDL–VECM method with structural breaks

PurposeThe environmental deterioration has become one of the most economically consequential and charged topics. Numerous scholars have examined the driving factors failing to consider the structural breaks. This study aims to explore sustainability using the per capita ecological footprints (EF) as an indicator of environmental adversities and controlling the resources rent [(natural resources (NR)], labor capital (LC), urbanization (UR) and per capita economic growth [gross domestic product (GDP)] of China.Design/methodology/approachThrough the analysis of the long- and short-run effects with an autoregressive distributed lag model (ARDL), structural break based on BP test and Granger causality test based on vector error correction model (VECM), empirical evidence is provided for the policies formulation of sustainable development.FindingsThe long-run equilibrium between the EF and GDP, NR, UR and LC is proved. In the long run, an environmental Kuznets curve (EKC) relationship existed, but China is still in the rising stage of the curve; there is a positive relationship between the EF and NR, indicating a resource curse; the UR is also unsustainable. The LC is the most favorable factor for sustainable development. In the short term, only the lagged GDP has an inhibitory effect on the EF. Besides, all explanatory variables are Granger causes of the EF.Originality/valueA novel attempt is made to examine the long-term equilibrium and short-term dynamics under the prerequisites that the structural break points with its time and frequencies were examined by BP test and ARDL and VECM framework and the validity of the EKC hypothesis is tested.

Pastoral livestock market integration amidst improvements in physical and communication infrastructure: Evidence from northern Kenya

This article analyses the level of integration in pastoral markets in Kenya using high-frequency data generated through a crowdsourcing endeavour. The vector error-correction model framework was used to estimate the causal relationships between the short- and long-run market price. The results indicate that a higher proportion of price variation in larger markets in the region was due to market price shocks, while variation in the smaller markets originated from the larger markets. Weekly adjustments and the convergence of prices on a long-run equilibrium after experiencing shocks were also observed. Price transmission was also evident between markets operating along different trading routes. However, markets located close to production catchments exhibited lower price trends, despite being connected. These results suggest a strong influence of the recent infrastructural investments on price transmission between markets in the region. The findings imply that more investments would enhance the competitive trading environment and reduce unidirectional price transmission.

Price relationships along the Norwegian salmon value chains: A comparative study of the leading consumption market in France and the largest processing industry in Poland

This study investigates price relationships along the Norwegian salmon value chains in France, the EU’s leading consumption market for salmon, and Poland, the EU’s largest “processing hub.” Using a vector error correction model framework, our results indicate that the salmon price determination is a supplier-driven process, with the export prices in Norway influencing prices in France and Poland, but not vice versa. Results also suggest that price linkages are strong between the export market in Norway and the retail market in France and the processing industry in Poland; however, the Polish retail sector is separated from markets at the upstream level, while price discovery at the French wholesale market is dominated by the retail market. Our findings imply that the evolved market structures lead to efficiently functioning retail markets in France and wholesale markets in Poland, but put the French wholesale and Polish retail markets at a disadvantage.

The remittance-food security dynamics in Jamaica

ABSTRACT It is an unsettled question whether the impact of remittance inflows (remittances) on food security is positive, negative, or null. We examine this question for Jamaica, the first for such a high remittance-receiving country. To start, we discuss the issues related to food security and remittances. Next, buttressed by theoretical underpinnings , we measure food security as calorie consumption and empirically evaluate how it is impacted by remittances in Jamaica over the period 1976 to 2019. We employ impulse response function analysis and Granger causality testing, within a robust vector error correction modelling framework. There are two key findings. First, we find a cointegrating relationship wherein the impulse analysis indicates that remittances positively impact calorie consumption in the long run, and vice versa. Second, we find that there is bidirectional Granger causality between remittances and calorie consumption. One implication of our findings is that remittances should form part of any broad developmental poverty alleviation polices, given their impact on promoting food security.

Assessing the impacts of climate change on cereal production in Bangladesh: evidence from ARDL modeling approach

PurposeThis study aims to examine the impacts of climate change (CC), measured average annual rainfall, average annual temperature and carbon dioxide (CO2e) on cereal production (CPD) in Bangladesh by using the annual dataset from 1988–2014, with the incorporation of cereal cropped area (CCA), financial development (FD), energy consumption (EC) and rural labor force as important determinants of CPD.Design/methodology/approachThis study used an auto-regressive distributive lag (ARDL) model and several econometric approaches to validate the long- and short-term cointegration and the causality directions, respectively, of the scrutinized variables.FindingsResults of the bounds testing approach confirmed the stable long-term connections among the underlying variables. The estimates of the ARDL model indicated that rainfall improves CPD in the short-and long-term. However, CO2e has a significantly negative impact on CPD both in the short-and long-term. Results further showed that temperature has an adverse effect on CPD in the short-term. Among other determinants, CCA, FD and EC have significantly positive impacts on CPD in both cases. The outcomes of Granger causality indicated that a significant two-way causal association is running from all variables to CPD except temperature and rainfall. The connection between CPD and temperature is unidirectional, showing that CPD is influenced by temperature. All other variables also have a valid and significant causal link among each other. Additionally, the findings of variance decomposition suggest that results are robust, and all these factors have a significant influence on CPD in Bangladesh.Research limitations/implicationsThese findings have important policy implications for Bangladesh and other developing countries. For instance, introduce improved cereal crop varieties, increase CCA and familiarizes agricultural credits through formal institutions on relaxed conditions and on low-interest rates could reduce the CPD’s vulnerability to climate shocks.Originality/valueTo the best of the authors’ knowledge, this study is the first attempt to examine the short- and long-term impacts of CC on CPD in Bangladesh over 1988–2014. The authors used various econometrics techniques, including the ARDL approach, the Granger causality test based on the vector error correction model framework and the variance decomposition method.

CAUSAL LINK BETWEEN EXPORT, IMPORT, REMITTANCE AND ECONOMIC GROWTH IN BANGLADESH

Given the importance of the economic indicators on the economic growth in Bangladesh, this paper examines the shortrun and long run causality between remittance, imports, exports and GDP growth of Bangladesh using the data over the liberalized period 1976-2016. Applying the Johansen cointegration and elasticity concept, this study finds that variables are cointegrated as well a long run nexus between exports, imports, remittances and GDP growth in Bangladesh. Besides, the Granger Causality test in a VECM (Vector Error Correction Model) framework, this study finds unidirectional causality from import to growth in Bangladesh. The hardly surprising result, remittance does not show any feedback effect/causality in both the short run and long run. The possible interpretation may be that remittances mainly benefit the individual receivers. The finding also explores that exports, imports and remittances endorse each other in the short run

Revisited the Relationship Between Economic Growth and Transport Infrastructure in India: An Empirical Study

This study empirically re-examines the relationship between transport infrastructure and economic growth in India for the period 1990–2017. Multivariate dynamic models are applied to estimate the relationship between economic growth and different modes of transport infrastructure namely road, rail and air transports in the vector error correction model framework. The results reveal that road and air transports have significant positive contribution to economic growth in the long-run while rail transport is insignificant. This study further examines the said issue using unit free index variables and has constructed a composite index of transport infrastructure using principal component analysis to analyse the nexus between aggregate transport infrastructure and economic growth in India in the post globalisation era. The results of the study indicate the bidirectional causality between aggregate transport infrastructure and economic growth. Results of this study suggest incorporating feedback issue in policy formulations. JEL Codes: C22, O18, R4

Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence.

BackgroundCOVID-19 has been one of the most serious global health crises in world history. During the pandemic, health care systems require accurate forecasts for key resources to guide preparation for patient surges. Forecasting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment.ObjectiveThe goal of this study was to explore the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census.MethodsThe study data comprised aggregated daily COVID-19 hospital census data across 11 Atrium Health hospitals plus a virtual hospital in the greater Charlotte metropolitan area of North Carolina, as well as the total daily infection incidence across the same region during the May 15 to December 5, 2020, period. Cross-correlations between hospital census and local infection incidence lagging up to 21 days were computed. A multivariate time-series framework, called the vector error correction model (VECM), was used to simultaneously incorporate both time series and account for their possible long-run relationship. Hypothesis tests and model diagnostics were performed to test for the long-run relationship and examine model goodness of fit. The 7-days-ahead forecast performance was measured by mean absolute percentage error (MAPE), with time-series cross-validation. The forecast performance was also compared with an autoregressive integrated moving average (ARIMA) model in the same cross-validation time frame. Based on different scenarios of the pandemic, the fitted model was leveraged to produce 60-days-ahead forecasts.ResultsThe cross-correlations were uniformly high, falling between 0.7 and 0.8. There was sufficient evidence that the two time series have a stable long-run relationship at the .01 significance level. The model had very good fit to the data. The out-of-sample MAPE had a median of 5.9% and a 95th percentile of 13.4%. In comparison, the MAPE of the ARIMA had a median of 6.6% and a 95th percentile of 14.3%. Scenario-based 60-days-ahead forecasts exhibited concave trajectories with peaks lagging 2 to 3 weeks later than the peak infection incidence. In the worst-case scenario, the COVID-19 hospital census can reach a peak over 3 times greater than the peak observed during the second wave.ConclusionsWhen used in the VECM framework, the local COVID-19 infection incidence can be an effective leading indicator to predict the COVID-19 hospital census. The VECM model had a very good 7-days-ahead forecast performance and outperformed the traditional ARIMA model. Leveraging the relationship between the two time series, the model can produce realistic 60-days-ahead scenario-based projections, which can inform health care systems about the peak timing and volume of the hospital census for long-term planning purposes.

FUEL-MINING EXPORTS AND GROWTH IN A DEVELOPING STATE: THE CASE OF THE UAE

This study examines the causal effects of traditional UAE exports on economic growth over the period 1981-2012, using a neoclassical production function augmented with fuel-mining exports and imports of goods and services. To investigate the existence of a long-run relationship between fuel-mining exports and economic growth, the study applies the Johansen cointegration test, while the direction of the short-run causality is examined by applying the Granger causality test in a vector error correction model framework. In addition, a modified Wald test in an augmented vector autoregressive model, developed by Toda and Yamamoto (1995), is used to investigate the existence of a long-run causality between the variables. The cointegration analysis confirms the existence of a long-run relationship between the variables, while fuel-mining exports are found to have a negative impact on economic growth. Moreover, the study finds that fuel-mining exports do not cause economic growth in the short-run or the long-run.Keywords: Exports, Economic Growth, Causality, UAEJEL Classifications: O47, F43, C32DOI: https://doi.org/10.32479/ijeep.9183

Further Evidence on Export-Led Growth in the United Arab Emirates: Are Non-Oil Exports or Re-Exports the Key to Economic Growth?

Abstract This study investigates the relationships between exports and economic growth in the United Arab Emirates. Understanding these relationships is important for purposes of establishing appropriate growth and development policies and strategies. The study uses an augmented Cobb–Douglas production function to examine the causality between non-oil exports, re-exports and economic growth over the period 1981–2012. To investigate the existence of a long-run relationship between the variables, the study performs the Johansen cointegration test, while the direction of the short-run causality is examined by applying the Granger causality test in a vector error correction model framework. A modified Wald test in an augmented vector autoregressive model is applied in order to find the direction of the long-run causality. This research provides evidence in support of an indirect short-run uni-directional causality from economic growth to re-exports, through physical capital accumulation and imports. As for long-run causality, the results show that a bi-directional causality exists between re-exports and economic growth in the UAE.

The Impact of Financial Development on Energy Demand: Evidence from China

ABSTRACTAn autoregressive distributed lag (ARDL) bounds approach and vector error correction model (VECM) are used here to better understand the role of financial development in energy demand in China. Based on data from 1980 to 2014, the ARDL bounds approach yields empirical evidence that confirms the existence of long-run relationships among energy demand per capita, gross domestic product per capita, urbanization, economic structure, and financial development. The VECM framework shows the direction of Granger causality that combines the short run and the long run between the variables. The results suggest a feedback effect between financial development and energy demand per capita in the long run. However, financial development Granger causes per capita energy demand without a feedback effect in the short run. The results of this research may be of great importance for decision makers as they develop policies on energy and economic growth.

The Nexus Between Carbon Emission, Energy Consumption, Economic Growth And Changing Economic Structure In India: A Multivariate Cointegration Approach

India, one of the fastest growing economies of the world is also one of the largest CO2 emitters in the world. Challenge before the country is to reduce this alarming emission levels without hindering its growth prospects. Against this backdrop, the present paper studies the dynamic causal relationships between India's CO2 emission, energy consumption, GDP growth and changing economic structure. The study uses cointegration and causality analysis for the same. ARDL bound testing approach along with Johansen-Juselius maximum likelihood procedure is applied to examine the existence of long run equilibrium relationship among the variables. Causal linkages between the variables are studied using Granger causality test in Vector Error Correction model framework. For this the study uses data on India for-CO2 emissions, primary energy consumption, GDP per capita and structural variables like, agriculture and service value added, urbanization, production of capital and intermediate goods and employment. Primary energy consumption, per capita GDP and trade openness explain variations in CO2 emissions over long run. Elasticity of CO2 emission with respect to energy consumption is 2 percent in long run and 1.8 per cent in short run. CO2 emissions are less responsive to changes in per capita GDP (0.52) and trade openness (0.10). Both trade openness and GDP per capita growth lower emissions by producing and exporting more labor-intensive environment friendly goods. Causality analysis shows that trade openness Granger causes CO2 emission both in short run and in long run while CO2 emission Granger causes service value added and production of capital and intermediate goods in the short run. Output in these sectors in turn Granger cause employment in the long run. Given the nature of causality, there is no way that India can reduce energy consumption in service sector or in capital and intermediate goods sector. Thus, faced with growing concern over rising emission levels and requirements to meet its growth potentials, India should take policies aiming at greater investment in and usage of cleaner energy, conservation of energy and improving energy efficiency. This way it can strike a balance between reducing its emission levels while maintaining its current growth momentum.

Government digital information discovery and exploration: the case of unraveling tourism-led-growth paradox in China

PurposeThis study aims to examine the relationship between tourism development and economic growth while considering exports simultaneously. Governments in many countries have been developing and deploying strategies to attract tourism receipts as a means for economic growth. However, assessing the potential impact of tourism on economic growth among large economies is still in its infancy.Design/methodology/approachUsing a vector error correction model framework, this study examines the relationship among exports, gross domestic product (GDP) and tourism receipts (including international tourism receipts and domestic tourism receipts in two separate models) with macro data that covers two recent decades (1994-2013) in China.FindingsThe empirical findings confirm the existence of a long-term equilibrium relationship in each of these two tri-variate models. The empirical findings reveal that (1) both tourism-led-growth and export-led-growth hypotheses are supported, (2) the growth rate of tourism receipts exhibit a higher relevance with GDP growth than export growth and (3) the growth rate of international tourism shows a higher relevance with GDP growth than domestic tourism growth.Originality/valueUsing macroeconomic data collected by the Chinese government, the current study employs an advanced econometric methodology to explore the potential benefits of tourism on economic growth in China.

Banking outreach, infrastructure development and regional growth

PurposeThe purpose of this paper is to examine the dynamics between banking penetration, infrastructure development and regional growth within a multivariate framework in 23 Indian states over the period 2000-2012.Design/methodology/approachThe study employs the multivariate panel data framework to analyze the dynamics between banking penetration, infrastructure development and regional growth within the vector error correction model (VECM) framework.FindingsThe findings confirm the long-run equilibrium relationship between banking penetration, infrastructure and income for the panel. Long-run income elasticity of infrastructure, estimated using Panel dynamic ordinary least square, is positive, statistically significant and has a value of 0.1531. Further, results show bidirectional causality between income and aggregate infrastructure and unidirectional causality running from banking penetration to income and aggregate infrastructure in the long run. However, there is unidirectional causality running from income to banking penetration and aggregate infrastructure and from banking penetration to aggregate infrastructure in the short run.Research limitations/implicationsThe study mainly concentrates on the 2000-2012 period and includes transportation (roadways and railways), energy (including electricity) and telecommunication as indicators for infrastructure, as the data for these sectors are easily available at the state level. Second, this study employs the panel data technique as it has a shorter data count.Practical implicationsIn order to minimize the existing regional disparity in a developing India, national infrastructure policies should be aimed toward improving the overall access to as well as the quality of infrastructure (existing as well as newly planned). Further, widening the banking outreach at the bottom level may further help the economy as well as the infrastructure sector in mobilizing long-term finances for productive investments, in order to have a balanced, more inclusive and faster growth in the long run.Originality/valueThe study employs panel unit root, cointegration and Granger causality tests within the panel VECM framework to explore the dynamics among the system variables. Further, the study creates a composite index of infrastructure with principle component analysis.

Financial structure and economic performance in selected African countries: time series evidence

In this paper, the authors investigate the long-debated question of whether or not a country’s financial structure matters for economic performance and, if so, how exactly it matters. The study uses the Johansen cointegration and vector error correction modelling framework within a country-specific setting to examine empirically the existence of a long-run equilibrium relationship between the financial structure of a country and per capita GDP and the causality thereof. The empirical assessment is based on evidence from selected African countries over the period 1971-2013, notably Egypt, Nigeria and South Africa. Firstly, cointegration test results reported in this paper show that there exists a strong relationship between the financial structure of Egypt and South Africa, and per capita GDP in these countries. However, such a relationship is weak in Nigeria, mainly attributable to its low level of financial development and the possibility of the natural resource curse emanating from the oil industry. Secondly, the evidence also strongly suggests that the nature of the relationship between the financial structure of Egypt and South Africa and per capita GDP is positive, albeit based on different measures of financial structure. In Egypt, financial structure is measured by the S-Size ratio, while, in South Africa, it is proxied by the S-Activity ratio. In Nigeria, there is no evidence suggesting that the country’s financial structure influences per capita GDP. Lastly, coefficients of the error correction term for all three countries are low, suggesting inefficiencies in the financial system and possible rigidities within the economies

Does human capital contribute to economic growth in Mauritius?

Purpose Real gross domestic product (GDP) growth for Mauritius has averaged more than 5 per cent since 1970 and GDP per capita has increased more than tenfold between 1970 and 2012, from less than $500 to more than $9,000. It has often been reported that human capital, along with other growth enablers, has played an important role in this development. The purpose of this paper is to study this nexus. Design/methodology/approach A human capital augmented Cobb-Douglas production function is used, where output is also a function of capital and labour. One of the innovations of the present paper is the use of a composite index to proxy human capital. The authors investigate the impact of human capital on economic growth in a dynamic vector error correction modelling (VECM) framework. Findings The general results here show that stock, labour and human capital are all significant growth determinants, with human capital having a long-run output elasticity of 0.36. The VECM results generally validated the long-run output elasticity, although a relatively lower elasticity of 0.1 is obtained. Both sets of results tend to point to the fact that human capital has significantly contributed to economic growth in Mauritius. Research limitations/implications The current paper paves the way for future work, which can build on the composite HCI developed here and aggregate it with relevant variables representing tertiary education and training, to better analyze and further understand the role of human capital on economic growth in Mauritius. Originality/value Here, the authors posit that human capital is an aggregate of health, education and nutrition, and the authors use a composite index along with other contributing factors to study its impact on economic growth, within a VECM framework.

An Empirical Analysis of Supply Response of Rubber in Malaysia

Supply response of rubber to changes in economic incentives is analysed using co-integration approach. Time series data is taken for the period 1990 to 2014 and the vector error correction model framework has been applied. The empirical results confirmed the existence of a unique long-run equilibrium relationship among planted acreage, the relative price of rubber and price of fertilizer. Further, the estimates suggested that rubber supply is significantly influenced by the relative price of rubber and the price of fertilizer. The estimated short- and long-run elasticities of acreage with respect to relative price are respectively 0.04 and 0.77, while the short- and long-run elasticities of acreage with respect to fertilizer price are -0.20 and -0.28 respectively. The study recommends the design of an appropriate economic incentive structure to stimulate output and hence the income of farmers.

An empirical study on the dynamic relationship between oil prices and Indian stock market

Purpose – This study aims to investigate the dynamic relationships between oil price shocks and Indian stock market. Design/methodology/approach – The study used daily data for the period starting from January 2001 to March 2013. In this study, Johansen's cointegration test, vector error correction model (VECM), Granger causality test, impulse response functions (IRFs) and variance decompositions (VDCs) test have been applied to exhibit the long-run and short-run relationship between them. Findings – The cointegration result indicates the existence of long-term relationship. Further, the error correction term of VECM shows a long-run causality moves from Indian stock market to oil price but not the vice versa. The results of the Granger causality test under the VECM framework confirm that no short-run causality between the variables exists. The VDCs analysis revealed that the Indian stock markets and crude oil prices are strongly exogenous. Finally, from the IRFs, analysis revealed that a positive shock in oil price has a small but persistence and growing positive impact on Indian stock markets in short run. Originality/value – The study would enhance the understandings of the interaction between oil price volatilities and emerging stock market performances. Further, the study would enable foreign investors who are interested in Indian stock market helps in understanding the conditional relationship between the variables.

Stock Market Performance and Economic Growth: Evidence from Nigeria Employing Vector Error Correction Model Framework

This study examines the long-run and causal relationship between stock market performance and economic growth in Nigeria for the period from 1987Q1 to 2012Q4 ¬inclusive. The study employs the Augmented-Dickey Fuller test for unit root, the Johansen (1995) Maximum Likelihood cointegration technique and Vector Error Correction Model framework to capture long-run and short-run relationships in the cointegrating vectors of Nigerian stock market and economic growth. The study further employs Granger (1969) Causality, Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) to capture shocks transmission and AR root graph for stability. The optimum lag length was selected based on the Schwartz and Hannan-Quin information criteria. The results of the cointegration test confirm that there exists a long-run relationship between stock market performance and economic growth, while the causality test results suggest that stock market performance causes economic growth with feedback. The Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) suggest that shocks from the stock market do not impede economic growth. Furthermore, the result of the AR root graph indicates that the Nigerian stock exchange market is not stable. Hence, the current reforms and policies going on in the Nigerian stock exchange should be sustained to boost investors’ confidence and participation.

Government debt in Greece: An empirical analysis

Greek government debt has been increasing above the percentage stated in the growth and stability path from 112.9% in 2008 to 175.6% in 2013. This paper investigates the determinants of the general government debt in Greek by means of Vector Error Correction Model framework, Variance Decomposition and Generalized Impulse Response Function Analysis. The analysis showed a significant negative relationship between general government debt and government deficit, general government debt and inflation. Shocks to general government and inflation will cause general government debt to increase. Government deficit should be increased since there is gross capital formation included in its calculation which could be invested in income generating projects. The current account balance should be reduced by improving the net trade balance