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A simple panel unit root test in the presence of cross-section dependence

M. Hashem Pesaran
- 01 Mar 2007 - 
- Vol. 22, Iss: 2, pp 265-312
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TLDR
In this paper, a simple alternative where the standard ADF regressions are augmented with the cross section averages of lagged levels and first-differences of the individual series is proposed, and it is shown that the individual CADF statistics are asymptotically similar and do not depend on the factor loadings.
Abstract
A number of panel unit root tests that allow for cross section dependence have been proposed in the literature that use orthogonalization type procedures to asymptotically eliminate the cross dependence of the series before standard panel unit root tests are applied to the transformed series. In this paper we propose a simple alternative where the standard ADF regressions are augmented with the cross section averages of lagged levels and first-differences of the individual series. New asymptotic results are obtained both for the individual CADF statistics, and their simple averages. It is shown that the individual CADF statistics are asymptotically similar and do not depend on the factor loadings. The limit distribution of the average CADF statistic is shown to exist and its critical values are tabulated. Small sample properties of the proposed test are investigated by Monte Carlo experiments. The proposed test is applied to a panel of 17 OECD real exchange rate series as well as to log real earnings of households in the PSID data.

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Journal ArticleDOI

The role of renewable energy technological innovation on climate change: Empirical evidence from China.

TL;DR: It is found that the effect of RETI on curbing CO2 emissions decreases with the rising of coal-dominated energy consumption structure but in contrast, this effect increases with the growing proportion of renewable energy generation.
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The Performance of Panel Unit Root and Stationarity Tests: Results from a Large Scale Simulation Study

TL;DR: In this paper, the authors present results on the size and power of first generation panel unit root and stationarity tests obtained from a large scale simulation study, and assess the performance as a function of the time and the cross-section dimensions.
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Financial development and economic growth: Some theory and more evidence

TL;DR: In this article, the authors developed a Solow-Swan growth model augmented with financial markets in the tradition of Wu, Hou, and Cheng (2010), and showed that debt from credit markets and equity from stock markets are two long run determinants of GDP per capita.
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On vector autoregressive modeling in space and time

TL;DR: A synthetic space-time impulse response function (STIR) is introduced, portraying average effects as a function of displacement in time and space and a standard approach in structural VAR analysis is taken.
References
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Journal ArticleDOI

Testing for a Unit Root in Time Series Regression

TL;DR: In this article, the authors proposed new tests for detecting the presence of a unit root in quite general time series models, which accommodate models with a fitted drift and a time trend so that they may be used to discriminate between unit root nonstationarity and stationarity about a deterministic trend.
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Testing for unit roots in heterogeneous panels

TL;DR: In this article, a unit root test for dynamic heterogeneous panels based on the mean of individual unit root statistics is proposed, which converges in probability to a standard normal variate sequentially with T (the time series dimension) →∞, followed by N (the cross sectional dimension)→∞.
Journal ArticleDOI

Time Series Analysis.

Journal ArticleDOI

Unit root tests in panel data: asymptotic and finite-sample properties

TL;DR: In this article, the authors consider pooling cross-section time series data for testing the unit root hypothesis, and they show that the power of the panel-based unit root test is dramatically higher, compared to performing a separate unit-root test for each individual time series.
Journal ArticleDOI

Time series analysis

James D. Hamilton
- 01 Feb 1997 - 
TL;DR: A ordered sequence of events or observations having a time component is called as a time series, and some good examples are daily opening and closing stock prices, daily humidity, temperature, pressure, annual gross domestic product of a country and so on.
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