Panel Data Analysis (Stationarity, Cointegration, and Causality)

Lau, Lin‐Sea; Ng, Cheong-Fatt; Cheah, Siew-Pong; Choong, Chee‐Keong

doi:10.1016/b978-0-12-816797-7.00009-6

Cited by 21 publications

(14 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It enables the estimation of the long run relationship in a consistent and efficient fashion in the situation where the series are a mixture of I(0) and I(1). 6 The PMG estimator is also robust to the outliers and lag orders (Lau, et al 2019).…”

Section: Empirical Model Data and Methodologymentioning

confidence: 99%

South–South Trade and Economic Growth: Panel Data Evidence from Africa’s Market Integration

Musila

Yiheyis

2023

Preprint

View full text Add to dashboard Cite

Policymakers in the South have initiated several regional trading schemes in the last four decades aimed at increasing South-South trade, believing that it will help achieve rapid economic growth and development. Although many analysts have examined the effect of regional trade arrangements on South-South trade, little is known about the impact of South-South trade on economic growth of the member states. This paper uses panel data for the period 1995-2020 to estimate the impact of intra-African trade on economic growth of 42 African countries. Using residual trade openness as instruments and PMG-ARDL-VEC estimation approach, we obtain positive and statistically significant long run relationship between growth of real GDP per capita and the proxy for intraAfrican merchandise trade intensity. We also obtain a positive and significant long run relationship between growth and proxiesfor extra-African merchandise trade openness and total trade openness. Although the results suggest that both regional and multilateral trade expansion can help spur Africa’s growth, the impact of extra-African merchandise trade on growth is, however, relatively higher than that of intra-African merchandise trade. Besides trade, we also find our proxies for physical and human capital accumulation to have significant and positive influences on real GDP per capita growth. JEL: C33, F15, O47, O55

show abstract

Section: Empirical Model Data and Methodologymentioning

confidence: 99%

South–South Trade and Economic Growth: Panel Data Evidence from Africa’s Market Integration

Musila

Yiheyis

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The fiscal austerity can be an example in this context. Such a policy decision of one country impacts directly and indirectly provinces and other countries (Atasoy, 2017; Lau et al. , 2019).…”

Section: Methodsmentioning

confidence: 99%

“…Previous studies have defined the main steps in choosing a suitable method to analyze the panel data. Each suitable analysis method has been selected based on the time dimensions (T), cross-section dimension (N) and the existence of the CSD (Menegaki, 2019;Lau et al, 2019;Pala, 2020). Figure 6 shows the main steps and suitable methods to conduct any study that depends on the panel.…”

Section: General Approachmentioning

confidence: 99%

Nexus between construction sector and economic indicators for Turkey and European Union evidenced by panel data analysis

Qabaja

Tenekeci

2022

ECAM

View full text Add to dashboard Cite

PurposeThe research aims to study the regression, cointegration and causality between the construction sector (CS) and the Gross Domestic Product (GDP), considering other variables in the study such as interest rate, taxation, industry sector, investment and Foreign Direct Investment (FDI), which are analyzed through unique panel models. The study was conducted in Turkey and the ten other countries of the European Union (EU) from 1988 to 2019.Design/methodology/approachRegression, cointegration and causality methods were used to investigate the different types of relationships between variables in the models. Data were obtained from official databases and the study contains four main stages, which are explained in detail in the methodology section.FindingsThe study used the analysis methods of regression, cointegration and causality tests and found that the CS and GDP have long-run estimates and the relationship between the two for different countries is negative in a two-way direction. Results are detailed in the analysis section.Research limitations/implicationsNo data were available for the variables before 1988 for most countries, which led to a limited number of observations and issues in statistical analysis methods.Originality/valuePreviously, only input and output tables were used in the analysis. The impact of interest rate, taxation, investment and FDI has not been analyzed. Key variables are very relevant for Turkey, which suffers from chronical inflation and taxation regimes. These show variability with the EU countries for comparative analysis and have not been explored to date, remaining as a major gap for the construction industry. No attempts were made to use regression, cointegration and causality methods with variables. These analysis methods enable an understanding of the differences in variance (heteroscedasticity) and the presence of cross-sectional dependence (CSD), both critical for the reliability of the comparison of data sets and analysis.

show abstract

“…Due to the presence of cross-sectional dependence, the standard first-generation unit root tests and the estimation techniques such as pooled OLS, fixed effects, random effects, and the mean group (MG) estimator are not applicable in this study (Henningsen and Henningsen, 2019). According to Lau et al (2019), the pooled mean group (PMG) estimator developed by Pesaran et al (1999) is more efficient in the presence of cross-sectional dependence compared to the conventional tests and thus is utilized in this study. Eberhardt (2011a).…”

Section: Methodsmentioning

confidence: 99%

Crime and Economic Development in South Africa: A Panel Data Analysis

Jeke¹,

Chitenderu²,

Moyo³

2021

IJEBA

View full text Add to dashboard Cite

Purpose: This study aims to determine the effects and impact of crime in South Africa. As economies seek to develop through eliminating inequalities, alleviating poverty, and ensuring economic growth, crime often stands in the way, discouraging investments, fuelling migration, increasing transaction costs, to mention just a few, thereby creating economic development disparities. Design/Methodology/Approach: To evaluate the economic effects of crime, selected forms of crime are assessed on economic development as measured by economic growth, investments, and property value, among others between the years 1994-2019. To determine the effects and impact of crime, multiple panel regression analysis techniques were employed. Findings: The Pooled Mean Group technique was utilized in this study, and the results obtained from the econometric modelling indicate that crime has an overall detrimental effect on development. Property crime was found to hurt human development and investment, while contact crime only negatively impacts investments. Practical Implications: Economic effects of crime have been established using South African data. It is imperative to understand that safety and security is everyone's responsibility to preserve property values, reduce transaction costs, attract investment and economic opportunities for local economic development. Originality/Value: Limited empirical works exist on evaluating the economic effects of crime. Crime is pertinacious to human nature and society, with variations in the extent and types. Even though education is argued to produce positive externalities through reducing crime, statistics have shown the increasing number of sophisticated crimes that involve welleducated individuals.

show abstract

Panel Data Analysis (Stationarity, Cointegration, and Causality)

Cited by 21 publications

References 72 publications

South–South Trade and Economic Growth: Panel Data Evidence from Africa’s Market Integration

South–South Trade and Economic Growth: Panel Data Evidence from Africa’s Market Integration

Nexus between construction sector and economic indicators for Turkey and European Union evidenced by panel data analysis

Crime and Economic Development in South Africa: A Panel Data Analysis

Contact Info

Product

Resources

About