Cointegration Vector Research Articles

Crude oil price behavior and the adoption of sustainable measures in companies before and after COVID-19

The objectives achieved in the Paris Agreement to reduce greenhouse gas emissions and reduce dependence on fossil fuels have caused, in recent years, a growing importance on sustainability in companies in order to reduce Environmental, social and economic impacts. This study is focused on understanding how the variation in West Texas Intermediate crude oil prices affects the Dow Jones Sustainability Index, and therefore the companies included in it, and vice versa. The research aims to examine the statistical properties of both indices, using fractional integration methods, the fractional cointegration vector autoregressive (FCVAR) approach and the continuous wavelet transform (CWT) technique. The results warn of a change in trend, with the application of extraordinary measures being necessary to return to the original trend, while the analysis of cointegration and wavelet analysis measures reflect that an increase in those adopted based on sustainability by the different companies that make up the index imply a drop in the price of crude oil.

A Novel Approach for Testing Fractional Cointegration in Panel Data Models with Fixed Effects

Fractional cointegration in time series data has been explored by several authors, but panel data applications have been largely neglected. A previous study of ours discovered that the Chen and Hurvich fractional cointegration test for time series was fairly robust to a moderate degree of heterogeneity across sections of the six tests considered. Therefore, this paper advances a customized version of the Chen and Hurvich methodology to detect cointegrating connections in panels with unobserved fixed effects. Specifically, we develop a test statistic that accommodates variation in the long-term cointegrating vectors and fractional cointegration parameters across observational units. The behavior of our proposed test is examined through extensive Monte Carlo experiments under various data-generating processes and circumstances. The findings reveal that our modified test performs quite well comparatively and can successfully identify fractional cointegrating relationships in panels, even in the presence of idiosyncratic disturbances unique to each cross-sectional unit. Furthermore, the proposed modified test procedure established the presence of long-run equilibrium between the exchange rate and labor wage of 36 countries’ agricultural markets.

Displacement residuals reveal landslide regime shifts

AbstractDespite significant progress in the development of advanced technologies for detecting and monitoring unstable slopes, accurately predicting catastrophic landslides remains a challenge. To tackle this challenge, our research integrates advanced prediction models and granular systems theory to provide insights into regime shifts within slow-moving deep-seated landslide dynamics. Our approach is designed to discern exceptional departures from historical landslide dynamics. The approach leverages the “group dynamics,” crucial for identifying precursory failure indicators, according to the generic dynamics of the precursory failure regime in granular systems. We select three different monitored slow-moving landslides as test cases. We employ an error correction cointegration vector autoregression model together with an exogenous regressor to encode historical spatiotemporal landslide dynamics and predict displacement at multiple locations by considering the historical landslide motion and relationship with external triggers. Displacement residuals are obtained by computing the difference between predicted and measured displacement for a given historical calibration time window. Threshold values for the displacement residuals are determined by analyzing the historical distribution of these residuals. Lastly, persistence in time of the threshold exceedance and the number of monitoring points that exceed the threshold at the same time are considered to encode the group dynamics. This approach offers several advantages, including the effective identification of critical regime shifts, adaptability, and transferability, and it introduces regime shift information into local landslide early warning systems. This approach can enhance confidence in the resultant alert, particularly when integrated with conventional alert systems, thereby improving the reliability of landslide warning systems.

Modelling Loans to Non-Financial Corporations in the Eurozone: A Long-Memory Approach

This paper uses fractional integration and cointegration methods to analyze the long-run relationship between loans to non-financial corporations, real gross domestic product, real gross fixed capital formation, the cost of borrowing differential between long- and short-term rates, and a proxy for the cost of debt, securities, and equity issuance. The analysis includes four Eurozone countries, namely Germany, France, Italy, and Spain, and spans the most recent decades. More precisely, fractional integration and cointegration models are estimated to investigate the persistence of the series as well as their long-run relationships and short-run dynamics using both unrestricted and restricted specifications. The univariate results are heterogeneous, the highest degrees of integration being found in the case of loans to non-financial corporations, whilst the multivariate ones provide evidence of a single fractional cointegration vector as well as of a lower adjustment speed to the long-run equilibrium compared to previous studies in all four countries. Moreover, both the short- and long-run response of loans to exogenous shocks to real gross domestic product and the cost of borrowing differentials differs across countries because of country-specific factors.

Threshold inflation and relative price variability – a proposal for inflation targeting in Lebanon

PurposeThe study investigates the impact of inflation on the variability of relative prices in the context of Lebanon.Design/methodology/approachUnlike the traditional method, which relies on the variance of cross-sectional price changes measured at specific points in time to gauge the variability in relative prices, we employ a more appropriate approach. Under this approach, we capture the dispersion in relative prices by estimating how widely (or closely) a set of commodity prices drift apart over a span of time, offering a more comprehensive assessment. Firstly, we employ Johansen’s cointegration test on rolling subsamples to determine the number of statistically significant cointegrating vectors among the prices of 12 major commodity groups. Under this approach, an increase in the number of significant cointegrating vectors indicates a reduction in relative price variability, while a decrease suggests the opposite. Subsequently, we employ ordinary least squares regression to analyze how the fluctuations in inflation affect the variability in relative prices. The sample period ranges from December 2007 to April 2021.FindingsThe empirical results indicate that there exists a certain threshold inflation rate corresponding to which the variability in relative prices is minimized. More importantly, consistent with the theoretical predictions, the results suggest that it is not inflation per se, but the deviation of inflation from the 3% threshold level in either direction that causes higher dispersion in relative prices.Research limitations/implicationsThe empirical findings from this study have crucial implications for the operation of monetary and fiscal policy. In particular, these findings suggest that stabilizing long-term inflation around a certain threshold rate will not only help to anchor inflation expectations effectively but will also minimize the welfare costs associated with inflation.Originality/valueGiven the rising inflationary pressure in the recent past and its welfare costs, the study assumes crucial importance in understating how fluctuations in inflation distort the relative price structure and eventually cause resource misallocations and economic inefficiency.

Revisiting the environmental Kuznets curve hypothesis in the context of renewable and non-renewable energy in China

This research paper attempts to determine the effect of fossil fuel, renewable energy, and economic growth on carbon dioxide emissions and revisits China's Environmental Kuznets curve (EKC) hypothesis. The time series analysis will be performed for the years 1990 to 2020. To begin, Augmented Dickey-Fuller (ADF) and Phillips Perron (PP) tests will be performed to verify the series' stationarity. Second, the cointegration test will be utilized to determine the validity of a cointegration vector before performing the Fully Modified Least Square approach to conduct long- and short-term estimation. The empirical analysis illustrates that renewable energy negatively and significantly affects short- and long-run carbon dioxide emissions. However, fossil fuel consumption only positively and significantly affects carbon dioxide emissions in the short run. The empirical evidence supports the Environment Kuznets Curve's hypothesis in China. Fossil fuel consumption has a considerably higher effect on carbon dioxide emissions than renewable energy. To avoid ecological damage in China, authorities must pay more consideration to encouraging the use of energy from renewable sources. This demonstrates that China's overall environmental performance first deteriorates before gradually improving with economic growth.

Impact of Portfolio Performance on International Portfolio Diversification

Investment opportunity is what we all are looking for every now and then, avenues that can generate maximum return with minimum of risk. International Portfolio diversification may not fulfill aspiration of investors with globalization spreading its wings in each sphere of economic activity. The study has been conducted from the perspective of Indian Investor taking into account 22 foreign markets (11 emerging and 11 developed). The countries have been selected from two points of view; firstly on the basis of market capitalization and secondly on the basis of availability of data. Emerging markets chosen for the study contribute more than 80% in Emerging market Index which is sufficient to generalize the results to the whole. Geographical regions have also been taken into consideration in order to benefit from regional diversification; these are Asia-pacific with 13 markets, 8 markets from Europe, 3 from Latin America and USA. Firstly, portfolios with different investment objective would be chosen on the basis of sampled countries. Then testing co-integration study would help in elimination of those countries generating co-integrating vector. Hence, Indian investor would be benefited by the study to find out whether globally diversified portfolio is better or not as compared to domestic portfolio. International portfolio diversification can be suggested for Indian investor in the wake of its performance during structural breaks.

Panel Data Cointegration Testing with Structural Instabilities

Spurious regression analysis in panel data when the time series are cross-section dependent is analyzed in the article. The set-up includes (possibly unknown) multiple structural breaks that can affect both the deterministic and the common factor components. We show that consistent estimation of the long-run average parameter is possible once cross-section dependence is controlled using cross-section averages in the spirit of Pesaran’s common correlated effects approach. This result is used to design individual and panel cointegration test statistics that accommodate the presence of structural breaks that can induce parameter instabilities in the deterministic component, the cointegration vector and the common factor loadings.

The relationship between money supply and inflation: analysis with PANELVAR approach

The relationship between money supply and inflation: analysis with PANELVAR approach

Johansen’s Cointegration Analysis of Some Factors of Economic Growth and Exports of Products from the Republic of Azerbaijan to Ukraine

In the present paper, a Johansen’s cointegration analysis is carried out considering the volume of exports from Azerbaijan to Ukraine, GDP per capita of Ukraine, the openness of the economy of Ukraine and the economically active population of Azerbaijan for the period 1996-2022, also a comparative analysis of the above indicators is carried out, the characteristics of joint short-term and long-term movements are determined. In the course of research the author used the methodology of modified gravitational modeling, the econometric methodology of time series analysis, including tests for checking stationarity, the extended Dickey-Fuller test, the Granger test for the detection of causality, determining the cointegration dependence using the Johansen’s test, a Vector Error Correction Model (VECM) has been built. First, a basic modified model of gravity has been built, and the statistical adequacy of this model has been checked. A comparative analysis of two regression models was carried out after the inclusion of the trend component in one of them. The dynamic structure of regression residuals was studied and the test for heteroscedasticity and autocorrelation was carried out. It is shown that the most suitable specification for cointegration is the quadratic trend in the initial levels with a linear trend in cointegration relationships, which has led to the emergence of two cointegration vectors. As a result of the completed analysis, two cointegration relationships are obtained. The results of the impulse response functions, decomposition of dispersions and the VECM model in the form of combinations of two cointegrating vectors with the expected signs of the adjustment integers showed that the economic indicators used in the analysis for the specified period maintain cointegration in the long term with the not stable equilibrium joint movements of the factors under study.

Financial assets against inflation: Capturing the hedging properties of gold, housing prices, and equities

<abstract> <p>In this study, we employed a developed Fractional Cointegrating Vector Autoregressive (FCVAR) model to analyze the relationship between three different securities, i.e., housing prices, S&P500 stock prices and gold, and inflation rate, to determine the hedging properties of each type of asset against inflation shocks. Our analyses covered seven decades; ranging from January 1953 to January 2023. Our results suggested that housing prices and S&P500 performed partially against inflation and gold did not have hedging properties when attending to the full sample. Accounting for structural breaks, we discovered that these results changed. We found that housing prices and the S&P500 showed a superior hedging performance against inflation since the second regime. On the other hand, when we studied the behavior of gold, this security showed the inverse results, i.e., it showed no hedging performance in the second regime. Finally, these results were important for an optimal investment strategy, risk diversification, and monetarism.</p> </abstract>

The Causal Relationship between Financial Development, Financial Globalization and National Savings in Pakistan

Financial globalization has a role in transmitting information within financial markets and helps in financial development. Additionally, financial globalization facilitates risk diversification alleviates the negative consequences of economic recessions, enabling nations to engage in international borrowing and lending activities, thus stabilizing income, consumption, investment and savings levels. This research study analyzes the causal nexus between financial development, financial Globalization and national savings in Pakistan. Co-integration test indicates co-integration vector henceforth, there is relationship between variables in the long run. The causal relation is determined through vector error correction mechanism and it is found that there is long run bidirectional causality between financial development, financial globalization and national savings in Pakistan. This study recommends that monetary authorities in Pakistan to encourage financial inclusion as the ratio of account holders in financial sector is very low. This will encourage documented economy and savings will be channelized to productive sectors. Likewise, financial development up gradation with par with international standards will not only encourage domestic savings but also help in financial globalization.

Factors affecting groundnut output in Andhra Pradesh: co-integration and error-correction modeling

Groundnut is an important oilseed crop of India. The area under groundnut in India was maximum (8.0 million ha), however the production was less due to the lowest yield (938 kg/ha) among all Asian countries. Thus it becomes more important to identify the factors affecting groundnut productivity which is directly related to the farmer's income, sustainable supply and the price stabilization. Unless until the factors affecting groundnut productivity are known, the corrective measures can not be initiated. In this paper efforts have been made to examine whether producer's price, area cultivated, fertilizer applied and rainfall have an important effect on groundnut production in Andhra Pradesh using co-integration and error correction modeling. Co-integration and error correction modeling tend to solve spurious regression results obtained from the analysis of macro-economic data and also establish an equilibrium long-run relationship which enables one to carry out a valid inference of the explanatory variables that are responsible for affecting the output of the crop. A stationary test was performed which revealed only rainfall series was stationary at level, while other series become stationary at first differencing applying the unit root test. Johansen co-integration and error correction procedure was adopted which indicates the existence of five co-integrating vectors at 1% level of significance, hence rejecting the null hypothesis of no co-integrating vector. Further, a parsimonious error correction model was applied. The statistical significance of error correction model for groundnut validates the existence of an equilibrium relationship among the variables. The results, therefore, indicate the combined effect of area, fertilizer, rainfall and price jointly affect the output of groundnut in Andhra Pradesh. Therefore, a favorable price policy, assured irrigation and timely supply of fertilizers at remunerative price would help the farmers to allocate more area under groundnut. These measures are necessary to enhance the productivity as well as income of the farmers and also supply of groundnut in the benefit of consumers.

More Accurate Climate Trend Attribution by Using Cointegrating Vector Time Series Models

Adapting to human-induced climate change is becoming an increasingly important aspect of sustainable development. To be able to do this effectively, it is important to know how much human influence has contributed to observed climate trends. Climate detection and attribution (D&A) studies achieve this by estimating scaling factors usually obtained by performing a least squares regression of the observed trending climate variable on the equivalent variable simulated by a climate model. This study proposed instead to estimate scaling factors by using the econometric approach of dynamically modelling the time series as a cointegrating Vector Auto-Regressive (VAR) time series process. It is shown that a 2nd-order cointegrating VAR(2) model is theoretically justified if the observed and simulated variables can be represented as a one-box AR(1) response to a common integrated forcing. The VAR(2) model can be expressed as a Vector Error-Correction Model (VECM) and then fitted to the data to obtain the cointegration relationship, the stationary linear combination of the two variables, from which the scaling factor is then easily obtained. Estimates of the scaling factor from the VAR(2) model are critically compared to those from Ordinary Least Squares (OLS) and Total Least Squares (TLS) for annual Global Mean Surface Temperature (GMST) data simulated by a simple stochastic model of the carbon–climate system and for historical simulations from 16 climate models in the Coupled Model Intercomparison Project 5 (CMIP5) experiment. Results from the toy model simulations show that the slope estimates from OLS are negatively biased, TLS estimates are less biased but have high variance, and the VAR(2) estimates are unbiased and have lower variance and provide the most accurate estimates with smallest mean squared error. Similar behaviour is noted in the CMIP5 data. Hypothesis tests on the VAR(2) fits found strong evidence of a cointegrating relationship with the observations for all the CMIP5 simulations.

Adjustment coefficients and exact rational expectations in cointegrated vector autoregressive models

In this article, we consider the cointegrated vector autoregressive model with adjustment parameters and cointegration vectors . We discuss estimation of the model under the exact linear rational expectations, when we also have linear restrictions on the adjustment parameters . In particular we consider the same restriction on all vectors in and the hypothesis that some vectors in are known.

한국수출입은행의 공적수출금융지원이 한국의 수출에 미치는 영향 연구

Purpose – The purpose of this study is to analyze the impact of official export finance supported by the Export-Import Bank of Korea on Korea's exports. In particular, in order to differentiate from previous studies, the detailed size, as well as the long-term and short-term effects of export financing support on exports, were analyzed. Design/Methodology/Approach – In this analysis, an Export Demand Function Model was introduced. Prior to the empirical analysis, a unit root test and cointegration test were conducted. As a result of the pretest, it was found that the variables included in the analysis were I (1), and the cointegration vector did not exist. Based on these results, a Vector Autoregressive (VAR) model was established. Findings – As a result of estimating the impulse response function, it was found that when an official export finance shock was applied, exports gradually increased from the time of the shock to the third period, and then the increase slowed and converged to zero. As a result of the variance decomposition analysis, the export financing support amount began to show explanatory power in export fluctuations from the second period, and showed about 2.5% explanatory power from the third to the tenth period. Research Implications – It was confirmed that export finance provided to exporting companies plays an important role in helping to promote exports. Therefore, the government and financial institutions should support exporting companies by providing active export financing support, along with export promotion policies.

Price Analysis in the European Food Supply Chain

The paper assesses the degree of price cooperation between the main agricultural supply chain stakeholders in EU-27. The price transmission mechanism between producers and retailers is used for assessing cooperation. Cereals prices of EU-27 are used for the empirical analysis (January 1995 - April 2022). The price mechanism is modeled via the Co-Integrated Vector AutoRegressive (VAR) representation. The empirical analysis follows the usual drill of stationarity and co-integration tests followed by Granger Causality tests, Impulse Responses and Forecasts of Error Decomposition. The results show restricted cooperation between supply chain stakeholders.
 
 JEL Classifications: Q13, Q18
 
 Keywords: Price analysis, European Union - 27, supply chain management

Modeling the Nexus of Renewable and Non-Renewable Energy Consumption and Economic Progress in Southeastern Europe: A Panel Data Analysis

As the fourth industrial revolution drives innovation and economic growth, the energy sector is increasingly recognized for its significant economic contribution. This research aims to investigate the relationship between economic growth and renewable and non-renewable energy consumption in nine southeastern European countries using panel estimation techniques and causal inference. This research employs a unique approach to modeling the energy–growth nexus, incorporating interaction terms to better understand the impact of renewable energy on real GDP growth. The findings are a valuable addendum to the current body of research on the effects of renewable energy consumption on economic growth, and the results contribute to narrowing the empirical research gap in the econometrical field of panel data estimation and endogeneity. This study uses the fully modified OLS (FMOLS) technique for heterogeneous panels to estimate coefficients, while the error correction model (ECM) is used to estimate the cointegration vector between energy variables and GDP. The non-causality test by Dumitrescu and Hurlin (2012) evaluates the causation between energy variables and economic output. Empirical findings indicate that both renewable and non-renewable energy consumption positively affect economic growth. The outcomes of this study hold significant policy ramifications in terms of prioritizing reformation and investment towards specific sectors to foster capital infusion into renewable energy and energy efficiency projects and initiatives, consequently advancing sustainable economic growth.

지역화경제와 도시화경제가 지역 산업의 생산성에 미치는 영향

Purpose: Based on Japan’s regional-industries data from 1994 to 2018, we intend to analyze and present Japanese evidence on the productivity-enhancing effects of localization economies(MAR externality) and urbanization economies(Jacobs externality), and to accumulate research evidence for comparative analysis between Korea and Japan on the effect of agglomeration economies on productivity.
 Research design, data, and methodology: In order to empirically analyze the impact of localization economies and urbanization economies on the labor productivity, RIETI’s R-JIP database, which provides high-quality data on 31 industries in 47 prefectures from 1994 to 2018, was used. We implement a panel co-integration test for non-stationary panel data, and estimate panel co-integration vectors.
 Results: As a result of the panel cointegration test, labor productivity by region in Japan has a long-term equilibrium relationship with the industrial specialization index and industrial diversity index. In the estimation result of the panel cointegration vector, In the case of manufacturing, localization economy improves labor productivity, but urbanization economies does not affect labor productivity. On the other hand, in the case of IT manufacturing, both localization and urbanization economies have a significant impact on improving labor productivity.
 Implications: It is necessary to strengthen manufacturing specialization (localization economies) in order to improve the productivity of regional manufacturing. In addition, in order to improve the productivity of the IT manufacturing, a policy to strengthen the localization economies is important, and in addition, a policy to expand the diversity of related industries.

An Econometric Analysis of Public Expenditure and Agriculture Output: An Evidence from India

In the present era, the agricultural sector played a significant role in the development of the nation by creating jobs for rural people and developing of agricultural sector needed in labour-intensive nations like India. Hence, the study examined the relationship between public expenditure on agriculture and economic growth, public expenditure on agriculture and agricultural output in the context of India. Therefore, the study used the Unit root test, Cointegration test, Var, and ECM for analysis. The results show that there is no cointegration vector between public expenditure and economic growth and found that negative relationship between public expenditure and economic growth. But, the study reveals that there is a cointegration vector between public expenditure and agricultural output. Significant Error Correction term (ECt-1) indicates that 85.2 per cent of total disequilibrium in public expenditure on agriculture and agriculture output is corrected each year in India. The coefficient of the EC2t-1 term is statistically insignificant. shortrun coefficients are also insignificant, which concludes that there is no short-run causality between public Expenditure and agriculture output in any direction. One way causality exists from agriculture output to public expenditure in the long run and is supported the Wagner’s hypotheses instead of Keynesian Hypotheses in India.