CO2 Emissions, GDP and trade : a panel cointegration approach

This paper examines the relationships among per capita CO2 emissions, per capita GDP and international trade based on panel data sets spanning the period 1960-2008: one for 150 countries and the others for sub-samples comprising OECD and Non-OECD economies. We apply panel unit root and cointegration tests, and estimate a panel error correction model. The results from the error correction model suggest that there are long-term relationships between the variables for the whole sample and for Non-OECD countries. Finally, Granger causality tests show that there is bi-directional short-term causality between per capita GDP and international trade for the whole sample and between per capita GDP and CO2 emissions for OECD countries

Mean reversion in stock prices: new evidence from panel unit root tests

Purpose – There are several studies that investigate evidence for mean reversion in stock prices. However, there is no consensus as to whether stock prices are mean reverting or random walk (unit root) processes. The goal of this paper is to re-examine mean reversion in stock prices.
Design/methodology/approach – The authors use five different panel unit root tests, namely the Im, Pesaran and Shin t-bar test statistic, the Levin and Lin test, the Im, Lee, and Tieslau Lagrangian multiplier test statistic, the seemingly unrelated regression test, and the multivariate augmented Dickey Fuller test advocated by Taylor and Sarno.
Findings – The main finding is that there is no mean reversion of stock prices, consistent with the efficient market hypothesis.
Research limitations/implications – One issue not considered by this study is the role of structural breaks. It may be the case that the efficient market hypothesis is contingent on structural breaks in stock prices. Future studies should model structural breaks.
Practical implications – The findings have implications for econometric modelling, in particular forecasting.
Originality/value – This paper adds to the scarce literature on the mean reverting property of stock prices based on panel data; thus, it should be useful for researchers.

Evidence of panel stationarity from Chinese provincial and regional income

The aim of this paper is to examine whether Chinese provincial and regional real GDP and per capita real GDP are panel stationary for the period 1952–2003. We allow for multiple structural breaks based on a technique developed by Carrion-i-Silvestre et al. [Carrion-i-Silvestre, J. L., Barrio-Castro, T, D., & Lopez-Bazo, E. (2005). Breaking the panels: An application to the GDP per capita. Econometrics Journal, 8, 159–175]. Allowing for at most five structural breaks, we find that for 67% of the provinces, per capita real GDP is stationary; while we only find stationarity of real GDP for 17% of the provinces. However, when we extend the analysis to panel data models, we find statistically strong evidence of panel stationarity of Chinese provincial and regional income.

A modified LLC panel unit root test of the PPP hypothesis

In a recent study, Westerlund (Empir Econ 37:517–531, 2009) shows that the performance of the popular LLC (Levin et al., J Econ 108:1–24, 2002) panel unit root test depends critically on the choice of lag truncation used when correcting for serial correlation, and that it is only when this parameter is set as a function of time that the power raises above size. The purpose of the current paper is to propose a modified test that does not suffer from this drawback. The new test is not only simpler to compute but also superior in terms of small-sample performance, which is illustrated using an example purchasing power parity for less developed countries.

Panel versus GARCH information in unit root testing with an application to financial markets

In a search for more powerful unit root tests, some researchers have recently proposed accounting for the information contained in the GARCH of the innovations. However, while promising, tests with GARCH are difficult to implement, which has made them quite uncommon in the empirical literature. A computationally attractive alternative is to account not for GARCH but the information contained in a panel of multiple time series. The purpose of the current note is to compare the relative power achievable from these two information sources. © 2014 Elsevier B.V.

Does electricity consumption panel Granger cause GDP? A new global evidence

The goal of this paper is to undertake a panel data investigation of long-run Granger causality between electricity consumption and real GDP for seven panels, which together consist of 93 countries. We use a new panel causality test and find that in the long-run both electricity consumption and real GDP have a bidirectional Granger causality relationship except for the Middle East where causality runs only from GDP to electricity consumption. Finally, for the G6 panel the estimates reveal a negative sign effect, implying that increasing electricity consumption in the six most industrialised nations will reduce GDP. © 2010 Elsevier Ltd. All rights reserved.

On the determination of the number of factors using information criteria with data-driven penalty

As is well known, when using an information criterion to select the number of common factors in factor models the appropriate penalty is generally indetermine in the sense that it can be scaled by an arbitrary constant, c say, without affecting consistency. In an influential paper, Hallin and Liška (J Am Stat Assoc102:603–617, 2007) proposes a data-driven procedure for selecting the appropriate value of c. However, by removing one source of indeterminacy, the new procedure simultaneously creates several new ones, which make for rather complicated implementation, a problem that has been largely overlooked in the literature. By providing an extensive analysis using both simulated and real data, the current paper fills this gap.

Testing for stock return predictability in a large Chinese panel

This paper proposes a simple panel data test for stock return predictability that is flexible enough to accommodate three key salient features of the data, namely, predictor persistency and endogeneity, and cross-sectional dependence. Using a large panel of Chinese stock market data comprising more than one million observations, we show that most financial and macroeconomic predictors are in fact able to predict returns. We also show how the extent of the predictability varies across industries and firm sizes.

On the use of panel cointegration tests in energy economics

There is a burgeoning literature based on using panel cointegration techniques to study the relationship between energy consumption and GDP. Most panel cointegration tests employed take no cointegration as the null hypothesis. The current paper illustrates how a rejection by such a test cannot be taken as evidence of cointegration for the panel as a whole, a fact that seems to have gone largely unnoticed in the literature. Hence, even if the no cointegration null is rejected, this evidence is not enough to ensure that the relationship can be meaningfully estimated, as most (if not all) estimators in the literature require that the panel is cointegrated as a whole.

Panel cointegration tests of the sustainability hypothesis in rich OECD countries

This study reexamines the sustainability hypothesis by testing whether government revenues and expenditures for eight rich OECD countries between 1977Q1 and 2005Q4 are cointegrated. For this purpose, a nonstationary panel data approach is adopted, which is general enough to permit for cross-country dependence as well as structural breaks representing major shifts in fiscal policy. In contrast to many earlier studies, the results reported in this study suggest that the sustainability hypothesis cannot be rejected. © 2010 Taylor & Francis.

A note on the use of the LLC panel unit root test

One of the single most cited studies within the field of nonstationary panel data analysis is that of LLC (Levin et al. in J Econom 98:1 - 24, 2002), in which the authors propose a test for a common unit root in the panel. Using both theoretical arguments and simulation evidence, we show that this test can be misleading unless it is based on the same bandwidth selection rule used by LLC. © Springer-Verlag 2008.

Do order imbalances predict Chinese stock returns? New evidence from intraday data

In this paper we examine whether order imbalances can predict the Chinese stock market returns. We use intraday data, a panel data predictive regression model that accounts for persistent and endogenous order imbalances and cross-sectional dependence in returns, and show that order imbalances predict stock returns from 1-minute trading to 90-minute trading. On the basis of this predictability evidence using multiple trading strategies we show that profits persist during the day. These results imply that a source of Chinese market inefficiency is order imbalances.

New tools for understanding the local asymptotic power of panel unit root tests

Abstract Motivated by the previously documented discrepancy between actual and predicted power, the present paper provides new tools for analyzing the local asymptotic power of panel unit root tests. These tools are appropriate in general when considering panel data with a dominant autoregressive root of the form ^ρi=1+^ciN-^κT-^τ, where i=1,...,N indexes the cross-sectional units, T is the number of time periods and ^ci is a random local-to-unity parameter. A limit theory for the sample moments of such panel data is developed and is shown to involve infinite-order series expansions in the moments of ^ci, in which existing theories can be seen as mere first-order approximations. The new theory is applied to study the asymptotic local power functions of some known test statistics for a unit root. These functions can be expressed in terms of the expansions in the moments of ^ci, and include existing local power functions as special cases. Monte Carlo evidence is provided to suggest that the new results go a long way toward bridging the gap between actual and predicted power.

Panel bootstrap tests of slope homogeneity

This paper proposes two bootstrap-based tests that can be used to infer whether the individual slopes in a panel regression model are homogenous. The first test is suitable when wanting to infer the null of homogeneity versus the general alternative, while the second is suitable when wanting to infer the units of the panel that can be pooled. Both approaches are shown to be asymptotically valid, a property that is verified in small samples using Monte Carlo simulation.

Rethinking the univariate approach to panel unit root testing: using covariates to resolve the incidental trend problem

In an influential article, Hansen showed that covariate augmentation can lead to substantial power gains when compared to univariate tests. In this article, we ask if this result extends also to the panel data context? The answer turns out to be yes, which is maybe not that surprising. What is surprising, however, is the extent of the power gain, which is shown to more than outweigh the well-known power loss in the presence of incidental trends. That is, the covariates have an order effect on the neighborhood around unity for which local asymptotic power is negligible.