Testing for stationarity in heterogeneous panel data

Abstract

This paper proposes a residual-based Lagrange multiplier (LM) test for a null that the individual observed series are stationary around a deterministic level or around a deterministic trend against the alternative of a unit root in panel data. The tests which are asymptotically similar under the null, belong to the locally best invariant (LBI) test statistics. The asymptotic distributions of the statistics are derived under the null and are shown to be normally distributed. Finite sample sizes and powers are considered in a Monte Carlo experiment. The empirical sizes of the tests are close to the true size even in small samples. The testing procedure is easy to apply, including, to panel data models with fixed effects, individual deterministic trends and heterogeneous errors across cross-sections. It is also shown how to apply the tests to the more general case of serially correlated disturbance terms.

Testing the relationships between energy consumption, CO2 emissions, and economic growth in 24 African countries: a panel ARDL approach

“…Particularly, we have used the tests of: Levin, Lin and Chu (2002) We still have to mention the null hypothesis of Hadri's (2000) test is stationarity. In contrast, the null hypotheses of the other tests are the unit root ones.…”

The growth of public health expenditures in OECD countries: Do government ideology and electoral motives matter?

“…We applied the Levin et al (2002), Im et al (2003 and the Fisher tests referring to Maddala and Wu (1999) and Choi (2001). In contrast, the Hadri (2000) test uses the null hypothesis that there is no unit root. Breitung and Pesaran (2005) log(public HCE) = log of public HCE as a share of GDP, log(GDP) = log of GDP per capita, log(unemployment) = log of unemployment rate log(dependency_ratio) = log of the dependency ratio, log(comp_employees) = log of compensation of employees, log(private HCE) = log of private HCE per capita log(population) = log of (total) population, log(IOG) = log of the KOF index of globalization Tables 9 to 11 report the results of different unit root tests and demonstrate that we can always reject the null hypotheses of a unit root in first differences except for the dependency ratio (see below).…”

Energy Consumption and Economic Growth Revisited: Structural Breaks and Cross-Section Dependence

“…Table 1 summarises the thereby existing panel data studies on the energy consumption-growth 7 nexus. 1 Furthermore, there are also some panel data studies on the relationship between growth and specific components of total energy consumption such as coal (Apergis and Payne, 2010a,b), electricity (e.g., Acaravci and Ozturk, 2010;Apergis and Payne, 2011a;Narayan and Smyth, 2009), nuclear energy (Apergis and Payne, 2010d;Lee and Chiu, 2011), and renewable energy (see, e.g., Apergis and Payne, 2010e;Sadorsky, 2009 The first panel data study on the relationship between energy consumption and growth by 1 For a detailed literature overview including time series studies on the causal relationship between energy consumption and economic growth, see the recent surveys by Ozturk (2010) and Payne (2010) 8 Lee (2005) Lee (2005) and Al-Iriani (2006), most panel data analyses have applied the panel unit root tests proposed by Hadri (2000), Levin et al (2002) (LLC) and/or Im et al (2003) (IPS), the Pedroni (1999Pedroni ( , 2004 panel cointegration test and the panel generalised method of moments (GMM) estimator proposed by Arellano and Bond (1991) to test for panel Granger causality.…”