Panel Data Unit Root Research Articles

The Causal Relationship between External Debts and Economic Growth in East African Community

Abstract The interest in the causal relationship between external debt and economic growth has increased in the world due to its importance in whether a country is experiencing debt overhang or not. This study attempts to examine the causal relationship between external debt and economic growth in EAC’s founding partner states. The study employs Xtabond2 using a secondary panel data set for the period 1988 to 2020. The study conducted all required econometric tests such as panel data unit root set and overidentification test using Arellano Bond test, Sargan test and Hansen test respectively before proceeding with further regression. The findings reveal the negative impact of external debt on economic growth running from external debt to economic growth with no feedback indicating the debt overhang in these countries. Other moderating variables have shown different impacts on economic growth where the negative impact is revealed from inflation while positive impacts are revealed from gross capital formation, lagged dependent variable, and the square of total external debt. Other variables such as population size have no significant impact which might have been caused by the presence of a square of total external debt while the exchange rate has a short-run impact on economic growth. Therefore it is suggested that the policymaker should ensure that the loan secured is located on the project with a positive net profit to avoid further demand for external assistance on loan repayment. Also, all tax potentials available in these countries should be optimally exhausted to avoid reliance on external financing assistance. Moreover, these countries should ensure that all external debt secured is sustainable and solvency in relation to their economy.

The impact of economic growth, tourism, natural resources, technological innovation on carbon dioxide emission: evidence from BRICS countries.

The main objective of this manuscript was to investigate the relationships among economic development, tourism, the use of natural resources, technical advancement, and carbon dioxide emissions in the BRICS group of nations. Data from the panel was gathered from 1995 to 2018. Modern methodology tools including the CS-ARDL tests, Westerlund cointegration tests, and panel data unit root tests have been used in this study. Results of the models show that all the variables were transformed to the first difference to make it stationary. The Westerlund model test results suggest that dependent and independent variables have robust cointegration. Results of the CS-ARDL models reveal that all the variables signed, and significance are aligned with the economic theory. It indicates that except for tourism, the rest of the variables like technical innovation, natural resources, and economic growth have positive and significant effects on carbon dioxide emissions both in the short and long runs. Additionally, a 1% rise in economic growth, technical innovation, and natural resources over the long term would raise carbon dioxide emissions in the BRICS economies by 1.79%, 0.15%, and 0.10%, respectively. However, a 1% increase in tourism would result in a 0.39% decrease in carbon dioxide emissions among the nations in the panel data set. Therefore, the promotion of sustainable tourism and advancement in technological innovation is highly important in these countries, so the high impact of environmental degradation pressure may reduce to some extent. An in-addition comprehensive set of policies should be made on encouraging low-carbon transportation, promoting sustainable tourism certification, boosting local produce, reducing waste management, and provide education and awareness campaigns to tourists.

Urban growth in the long term: Belgium, 1880–1970

We take advantage of a new data set on Belgian cities to test random growth, that is, Gibrat’s law. This unique data set provides annual population estimates for all Belgian municipalities (2680 cities) from 1880 to 1970. The use of panel data methodology and unit root tests can provide a precise test of Gibrat’s law (a unit root is equivalent to random growth). We run both time series and panel data unit root tests, thus obtaining strong support for random growth in the long term. Results hold when allowing for the presence of one and two structural breaks in the mean, with the timing of the breaks coinciding with some major historical events, such as the World Wars and the economic crisis of 1929–1933.

Convergence of testing and positivity rates for the COVID-19 pandemic: evidence from Germany and Italy

PurposeThis paper aims to analyze the convergence of the testing and positivity rates for the COVID-19 pandemic focusing on Germany and Italy.Design/methodology/approachThe authors employ the two-regime threshold autoregressive (TAR) panel unit root test by using the weekly data in the period of 2020:03-2021:04.FindingsFollowing finding out that the testing and positivity rates are nonlinear, the authors determine that the transition country between the two regimes is Italy for the testing rates and Germany for the positivity rates. Their findings support the partial convergence for the testing rates for Germany and Italy. On the other hand, the authors could not find any convergence for the positivity rates of these two countries.Originality/valueThis paper contributes to academic literature in several ways. Firstly, to the best of their knowledge, this paper is the first study that analyzes the convergence of testing and positivity data. This paper further focuses on two Euro-Area countries which have suffered significantly from the COVID-19 pandemic. In addition, the authors employ the two-regime threshold autoregressive (TAR) panel unit root proposed by Beyaert and Camacho (2008) in their empirical analysis. This recent panel data methodology aims to test real convergence in a nonlinear framework by incorporating the threshold model, panel data unit root test and the calculation of critical values by bootstrap simulation.

Chief Executive Officer Duality, Nationality and Firms’ Growth Opportunities

Corporate governance mechanisms highlight the invaluable role of the Chief Executive Officer (CEO) in the affairs of an organization. The nexus between CEO characteristics and firm performance is not mute in the literature and mixed findings have also been presented. This study deviates from previous studies by focusing on the impact of CEO attributes on growth opportunities of the firm. Accordingly, this paper analysed duality and nationality of CEO on firms’ growth opportunities against the backdrop of national culture and societal values. To this end, secondary data of 76 non-financial firms listed on the Nigerian Exchange Group from 2010- 2019 were analyzed. Various diagnostic tests for panel data unit root were conducted. The result of the robust panel data analysis revealed that both CEO duality and nationality have negative but insignificant relationship with firms’ growth opportunities. Clearly, the CEO attributes examined do not wield significant influence in diminishing growth opportunities.

Testing the Pollution Haven Hypothesis with the Role of Foreign Direct Investments and Total Energy Consumption

The main objective of this study was to examine the nonlinear relationship between environmental deterioration and foreign direct investment for subpanels based on the country’s income level. In this study, the model’s determinants were total consumption of energy and electricity consumption, the share of renewable energy, and economic growth. Due to the observation of cross-sectional dependence, utilization of cointegration tests and panel data unit root were incorporated, which confirmed a mixed integration order. For the compliance of long-run and short-run relationships among the variables, a pooled mean group estimator panel auto-regressive distributed lag approach was incorporated. The results of long-run development support the pollution haven hypothesis; hence, ecological footprint is increased by the activities related to foreign direct investments. The obtained findings depend on the different subpanels based on the income level of countries. For the assurance of economic development sustainability in the energy sector, along with the electrical energy sector, customized policymaking is suggested by this study based on the particulars of each subpanel.

Missing Values in Panel Data Unit Root Tests

Missing data or missing values are a common phenomenon in applied panel data research and of great interest for panel data unit root testing. The standard approach in the literature is to balance the panel by removing units and/or trimming a common time period for all units. However, this approach can be costly in terms of lost information. Instead, existing panel unit root tests could be extended to the case of unbalanced panels, but this is often difficult because the missing observations affect the bias correction which is usually involved. This paper contributes to the literature in two ways; it extends two popular panel unit root tests to allow for missing values, and secondly, it employs asymptotic local power functions to analytically study the impact of various missing-value methods on power. We find that zeroing-out the missing observations is the method that results in the greater test power, and that this result holds for all deterministic component specifications, such as intercepts, trends and structural breaks.

Do foreign direct investments influence environmental degradation? Evidence from a panel autoregressive distributed lag model approach to low-, lower-middle-, upper-middle-, and high-income countries.

The main aim of the present study is to examine the possible nonlinear relations between foreign direct investments and environmental deterioration for subpanels separated according to the income levels of countries by using the classification made by the World Bank. Total energy consumption, economic growth, and renewable energy share are also considered as determinants of environmental deterioration in the model. Cross-sectional dependence is observed; hence, appropriate panel data unit root and cointegration tests are utilized for which results pointed out mixed integration order. Pooled mean group (PMG) estimator panel auto-regressive distributed lag (ARDL, hereafter) approach is adapted to observe short- and long-run relationships between the variables. Long-run results supported the pollution haven hypothesis as foreign direct investments caused an increase in ecological footprint. Findings are sensitive to different income levels of the subpanels of countries. This empirical study suggests tailored policymaking for every income level subpanel to ensure sustainable development.

Trade, Environment, Human Capital and Services Sector in Developing Countries: A Panel Data Analysis

Objective: This study aims to examine the influence of Trade Liberalization, Environmental Quality (CO2 Emission) on Services Sector Growth in some selected developing.
 Methodology: The estimation of the study considers panel data unit root and panel data ARDL approach. The environmental quality is represented by Carbon Dioxide Emission and trade liberalization is by trade openness.
 Findings: The results of Panel Unit Root test summarize that Panel ARDL is the most appropriate method of estimation for having Panel Coefficient values due to mixed order of integration for developing countries.
 Implications: The empirical findings of model, in Developing Countries, Labor Force, Capital Stock, Trade Openness, Money Supply, Government Expenditure and Human Capital exert upward pressure on Services Value Addition while Carbon Dioxide Emission and Price Level is putting download pressure on Services Value Addition.

Examination of impact of COVID-19 on stock market: evidence from American peninsula

PurposeThe rapid spread of COVID-19 has dramatic effects on financial market across the globe. This study analyzes the relationship between the COVID-19 cases, age and stock market indexes in Central America, North America, and South America.Design/methodology/approachThe panel regression analysis on three regions from March 10, 2020 to April 9, 2020 was conducted to test the hypothesized model. The authors used Levin et al.’s (2002) panel data unit root test to check the stationarity, and Hausman (1978) test was applied to determine the random and fixed effects.FindingsThe authors’ panel regression results indicate that the COVID-19 cases have a negative impact on stock indexes, whereas the age has a positive impact on the stock indexes. The region-wise analysis supports the panel finding except for South America, which shows an insignificant association between stock indexes and COVID-19 cases.Originality/valueThe study supplements the literature by examining the impact of pandemics on stock indexes and focus on three multicultural regions, comprising developed, developing and emerging countries, which are hitherto unaddressed.

Tourism demand from Europe to Mexico, 2005-2018: A cointegration analysis

This paper analyzes tourism demand in the countries of Europe for Mexico from 2005 to 2018. Unit root and cointegration tests in panel data are applied. Results indicate that there is presence of unit roots in the variables. A long-term equilibrium relationship was found among tourism demand, real exchange rate, and income, and also there are bidirectional causality relationships between these variables. The positive relationship among the variables implies that a depreciation of the domestic currency and a higher level of income of the releasing countries would generate greater tourism demand in Mexico.

General Diagnostic Tests for Cross Section Dependence in Panels

This paper proposes simple tests of error cross-sectional dependence which are applicable to a variety of panel data models, including stationary and unit root dynamic heterogeneous panels with short T and large N. The proposed tests are based on the average of pair-wise correlation coefficients of the OLS residuals from the individual regressions in the panel and can be used to test for cross-sectional dependence of any fixed order p, as well as the case where no a priori ordering of the cross-sectional units is assumed, referred to as $$\hbox {CD}(p)$$ and $$\hbox {CD}$$ tests, respectively. Asymptotic distribution of these tests is derived and their power function analyzed under different alternatives. It is shown that these tests are correctly centred for fixed N and T and are robust to single or multiple breaks in the slope coefficients and/or error variances. The small sample properties of the tests are investigated and compared to the Lagrange multiplier test of Breusch and Pagan using Monte Carlo experiments. It is shown that the tests have the correct size in very small samples and satisfactory power, and, as predicted by the theory, they are quite robust to the presence of unit roots and structural breaks. The use of the $$\hbox {CD}$$ test is illustrated by applying it to study the degree of dependence in per capita output innovations across countries within a given region and across countries in different regions. The results show significant evidence of cross-dependence in output innovations across many countries and regions in the World.

EXTERNAL SHOCKS, STRUCTURAL BREAKS AND UNEMPLOYMENT HYSTERESIS IN SELECTED ASIAN COUNTRIES

This paper re-examines the hypothesis of unemployment hysteresis using panel data for 11 Asian countries for the period from 1980 to 2008. This study employs a variety of panel data unit root tests recently advanced by Bai and Ng (2004), Pesaran (2007) and Chang and Song (2009). The advantage of these tests is that they are able to exploit the cross-section variations of the series. In addition to these tests, a new powerful panel stationarity test proposed by Carrión-i-Silvestre et al. (2005) is applied which exploits the cross-section variations of the series and also allows for different numbers of endogenous breakpoints in the series. Our findings stress the importance of accounting exogenous shocks in the series and provide stronger evidence against the hypothesis of unemployment hysteresis for the countries analyzed. We also discover critical economic affairs which may cause the unemployment rates to fluctuate significantly. Policy implications are proposed through our observations.

The greenhouse effect of the agriculture-economic growth-renewable energy nexus: Evidence from G20 countries

Agriculture is the world's second largest greenhouse gas (GHG) emitter due to the use of fossil fuel-based fertilizers, agricultural machinery, and the burning of biomass. Meanwhile, the Group of Twenty (G20) countries, which include the world's top three agricultural markets, have a significant impact on world agricultural development. This study is the first attempt to examine the effects of the agriculture-economic growth-renewable energy nexus on carbon dioxide (CO2) emissions within the framework of the environmental Kuznets curve (EKC) in a panel of 19 nations of the G20 countries from 1990 to 2014. To do so, panel data unit root tests, cointegration tests, and the panel fully modified ordinary least squares (FMOLS) estimator are used. The main findings are as follows: (1) Panel data unit root and cointegration tests confirm the existence of a long-run relationship among the selected variables, (2) agriculture significantly increases CO2 emissions in the full sample and the developing economies of the G20, while renewable energy consumption reduces the CO2 emissions in the full sample and the developed economies of the G20, (3) the EKC indeed exists in the full sample and developed economies, while economic growth only exerts a positive impact on CO2 emissions for developing economies, indicating that the peak of CO2 emissions for developing economies has not yet been reached, and (4) varied causality links exist among the variables across the different income levels of the G20 countries. Based on the above findings, to suppress the greenhouse effect, the G20 countries' policymakers should not only promote the development of sustainable agriculture, but also stimulate renewable energy consumption, especially in developing economies.

Panel data unit root test with structural break: A Bayesian approach

The idea about structural break in unit root hypothesis under time series model had received great amount of attention over many last decades. The importance of structural break in the mean had been comprehensively studied by Perron [15], Perron and Vogelsang [17], Zivot and Andrews [25] etc. This had also studied in considering of break in variance by Kim et al. [9], Cook [6], Kumar et al. [11] etc. There is sufficient contribution regarding break in mean and variance individually but both are equally important and this was little explored by Bai [1] for panel data and Meligkotsidou et al. [14] for univariate time series. In present paper, we are extending this on panel dataAR(1) time series model under Bayesian framework. Posterior odds ratio has been derived for various models with and without break in mean, variance and both in consideration of unit root hypothesis. A simulation as well as an empirical analysis is also carried out to get more generalized view on the model under study.

Wagner’s Law, Public Employment, and Spanish Regions

Abstract Following Peacock and Musgrave’s rediscovery of Wagner’s Law, the latter became a standard tool used in research on the relationship between growth of public spending and the factors by which it is influenced. However, conventional empirical tests are based on a specification error related to Wagner’s definition of the public sector, which he considered in its totality, including public companies. The present article attempts to correct this error and obtain an approximation to the size of the public sector by considering public employment as a whole, both in public administrations and services and in public companies. To this end, panel data for the Spanish autonomous regions are used in addition to data for the overall public sector. The empirical test is performed utilizing cointegration techniques and unit roots in panel data. Similarly, the possibility of structural breaks in the data is taken into consideration and they are estimated using fictitious variables. JEL classifications: H11; H50; E62 Keywords: public employment; gross domestic product; unit root; cointegration; panel data

DOES MIDDLE INCOME TRAP EXIST?: EVIDENCE FROM EMERGING ECONOMIES: E7 COUNTRIES FOR 1969-2015

The E7 countries (China, India, Brazil, Russia, Mexico, Indonesia and Turkey)that have been growing fast since 1990s have been under the middle income countrycategory according to the income category classification of the World Bank for a longperiod of time. Researchers have been interested especially in emerging economies thathave not been able to move up from the middle income category to the high incomecategory and this has led to the initiation of what’s called the ‘middle income trap’ (MIT)discussions in literature. The MIT is generally defined as the countries under the middleincome category failing to move up to the high income category. Therefore, the purpose ofthis study is to identify the presence of MIT in E7 countries that hold an importantposition in global economy. The unit root tests were used in the empirical phase of thestudy. This study’s difference from other studies is the fact that both the time series andthe panel data unit root tests were used both in linear and nonlinear forms, thuspreventing the misleading results created by choosing the wrong model specification.The USA was taken as the reference country in the study and the GNI per capitaaccording to the Atlas method (current US$) data of the World Bank was used for the E7countries for the period 1969-2015. To achieve consistency in the analysis results, Russiawas not included in the model as there were no data available for the same period forRussia given the fact that the same timeframe should be taken as the basis for allcountries. The empirical analysis showed that the E7 countries do not fall into the MIT.

Environment and economic growth in the Russian Arctic

The purpose of this study is to examine the hypothesis of the inverted U-shaped interdependence between environmental damage from air pollutant emissions and economic growth measured by gross regional product (GRP) per capita in the Arctic regions of Russia. Using the panel data for the period of 2000-2014, we apply FM-OLS panel long-run estimates proposed by Pedroni to investigate the existence of environmental Kuznets curve. Various panel data unit root and co-integration tests are also applied. We examine the stationary properties of individual series in panel datasets using different panel unit root tests. According to the concept of the environmental Kuznets curve all regions of the Russian Arctic are on the increasing branch of the curve. The economic growth is found to have no beneficial effect on the environment in the Arctic. We actualise the need of concentrated policies and incentives to reduce air pollutant emissions in the Russian Arctic.

Effect of Trading Volume on Market Returns of Equity Securities Market in Kenya

The study aimed to establish the effect of trading volume on market returns of securities traded in Kenyan securities exchange market. The study used secondary data from all the firms listed in NSE during the period 2004 to 2016. The target population of the study consisted of the sixty 62 companies listed in Nairobi securities exchange market that is, both financial and non-financial companies. The study was a census study of all the sixty 2 companies listed in the Nairobi security exchange market for 13 years starting the year 2004 to the year 2016. The study started with descriptive and then diagnostic tests. The measures of central tendency used to test normality were mean, median, maximum and minimum value, standard deviation, skewness, kurtosis and Jarque-Bera (JB) test. The results from these tests showed that the variables were fairly normally distributed. The study further sought to investigate the stationality properties of market returns, trading volume. The study used five panel data unit root tests. Particularly the test were, Levin, Lin and Chu t, Breitung t-stat, Im, Pesaran and Shin W-stat developed, Fisher-type tests using augmented dickey fuller ADF and (Phillip and Peron) PP tests. The all these tests revealed that the variables were stationary on average. The study also sought to test the granger causality among the variable. The results from granger causality showed that some of the variables granger caused one another. The cointegration results showed that there was long-run equilibrium. The regression techniques used was Cross-section fixed (dummy variables) and Period fixed (dummy variables). The regression results revealed that the variables were statistically significant effect on market returns trading volume had a positive effect on the market returns. It is therefore in this light that the future research should consider other variables which would increase the predictive power of the model. The other relevant variables would be variables such as the size of the firm, market value of the firm and the macroeconomic variables such as exchange rate, inflation, money supply among others.

Dynamics of Wood Pulp Production: Evidence from OECD Countries

This paper investigates whether shocks to pulp for paper production for 17 Organisation for Economic Co-Operation and Development (OECD) members over the period 1980–2012 are transitory or permanent. A variety of univariate and panel data unit root tests are employed. The presence of structural breaks is taken into account when performing those tests. Based on the Narayan-Popp univariate unit root test, wood production series for approximately 64.71% of countries is found to follow a non-stationary process. However, univariate unit root tests tend to have low power when the time span is relatively short. Consequently, three generations of panel unit root tests are considered. Cross-sectional dependence is detected. The first generation of unit roots do not effectively control for cross-sectional dependence, while the second and third generations do. The third generation accounts mainly for cross-sectional co-integration. As a confirmatory analysis, both unit root tests that tests for the null of non-stationarity and stationarity are considered. Most of the panel unit root tests point towards a non-stationary process. Hence, while these shocks can be transmitted to other economic sectors, past behaviours of wood production cannot be used for forecasting purposes. Forest conservation policies can have a permanent impact on pulp for paper production.