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M. Hashem Pesaran - One of the best experts on this subject based on the ideXlab platform.
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Panel Unit Root tests in the presence of a multifactor error structure
Journal of Econometrics, 2013Co-Authors: M. Hashem Pesaran, L. Vanessa Smith, Takashi YamagataAbstract:This paper extends the cross sectionally augmented Panel Unit Root test proposed by Pesaran (2007) to the case of a multifactor error structure. The basic idea is to exploit information regarding the unobserved factors that are shared by other time series in addition to the variable under consideration. Importantly, our test procedure only requires speci…cation of the maximum number of factors, in contrast to other Panel Unit Root tests based on principal components that require in addition the estimation of the number of factors as well as the factors themselves. Small sample properties of the proposed test are investigated by Monte Carlo experiments, which suggest that it controls well for size in almost all cases, especially in the presence of serial correlation in the error term, contrary to alternative test statistics. Empirical applications to Fisher’s in‡ation parity and real equity prices across
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On the interpretation of Panel Unit Root tests
Economics Letters, 2012Co-Authors: M. Hashem PesaranAbstract:Abstract Applications of Panel Unit Root tests have become commonplace in empirical economics, yet there are ambiguities as how best to interpret the test results. This note clarifies that rejection of the Panel Unit Root hypothesis should be interpreted as evidence that a statistically significant proportion of the Units are stationary. Accordingly, in the event of a rejection, and in applications where the time dimension of the Panel is relatively large, it recommends the test outcome to be augmented with an estimate of the proportion of the cross-section Units for which the individual Unit Root tests are rejected. The economic importance of the rejection can be measured by the magnitude of this proportion.
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A simple Panel Unit Root test in the presence of cross-section dependence
Journal of Applied Econometrics, 2007Co-Authors: M. Hashem PesaranAbstract: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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On The Panel Unit Root Tests Using Nonlinear Instrumental Variables
2004Co-Authors: M. Hashem PesaranAbstract:This paper re-examines the Panel Unit Root tests proposed by Chang (2002). She establishes asymptotic independence of the t-statistics when integrable functions of lagged dependent variable are used as instruments even if the original series are cross sectionally dependent. From this rather remarkable result she claims that her non-linear instrumental variable (NIV) Panel Unit Root test is valid under general error cross correlations for any N (the cross section dimension) as T (the time dimension of the Panel) tends to infinity. We show that her claim is valid only if NlnT/square Root of T to 0, as N and T to infinity, and this condition is unlikely to hold in practice, unless N is very small. The favourable simulation results reported by Chang are largely due to her particular choice of the error correlation matrix, which results in weak cross section dependence. Also, the asymptotic independence property of the t-statistics disappears when Chang's modified instruments are used. Using a common factor model with a sizeable degree of cross section correlations, we are able to show that Chang's NIV Panel Unit Root test suffers from gross size distortions, even when N is small relative to T (for example N=5, T=100).
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On The Panel Unit Root Tests Using Nonlinear Instrumental Variables
2003Co-Authors: M. Hashem PesaranAbstract:This paper re-examines the Panel Unit Root tests proposed by Chang (2002). She establishes asymptotic independence of the t-statistics when integrable functions of lagged dependent variable are used as instruments even if the original series are cross sectionally dependent. She claims that her non-linear instrumental variable (NIV) Panel Unit Root test is valid under general error cross correlations for any N (the cross section dimension) as T (the time dimension of the Panel) tends to infinity. These results are largely due to her particular choice of the error correlation matrix which results in weak cross section dependence. Also, the asymptotic independence property of the t- statistics disappears when Chang's modified instruments are used. Using a common factor model with a sizeable degree of cross section correlations, we show that Chang's NIV Panel Unit Root test suffers from gross size distortions, even when N is small relative to T.
Christoph Hanck - One of the best experts on this subject based on the ideXlab platform.
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Nonstationary-volatility robust Panel Unit Root tests and the great moderation
AStA Advances in Statistical Analysis, 2015Co-Authors: Christoph Hanck, Robert CzudajAbstract:This paper argues that typical applications of Panel Unit Root tests should take possible nonstationarity in the volatility process of the innovations of the Panel time series into account. Nonstationary volatility arises, for instance, when there are structural breaks in the innovation variances. A prominent example is the reduction in GDP growth variances enjoyed by many industrialized countries, known as the ‘Great moderation.’ It also proposes a new testing approach for Panel Unit Roots that is, unlike many existing tests, robust to such volatility processes. The Panel test is based on Simes’ (Biometrika 73:751–754, 1986 ) classical multiple test, which combines evidence from time series Unit Root tests of the series in the Panel. As time series tests, we employ the recent proposals of Cavaliere and Taylor (J Time Ser Anal 29:300–330, 2008b ). The Panel test is robust to general patterns of cross-sectional dependence and yet is straightforward to implement, only requiring valid $$p$$ p values of time series tests, and no resampling. Simulations show other Panel Unit Root tests to suffer from sometimes severe size distortions under nonstationary volatility and that this can be remedied using the test proposed here. We use the methods to test for Unit Roots in OECD Panels of gross domestic products and inflation rates, yielding inference robust to the ‘Great moderation’. We find little evidence of trend stationarity and mixed evidence regarding inflation stationarity.
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Nonstationary-Volatility Robust Panel Unit Root Tests and the Great Moderation
SSRN Electronic Journal, 2013Co-Authors: Christoph Hanck, Robert CzudajAbstract:This paper argues that typical applications of Panel Unit Root tests should take possible nonstationarity in the volatility process of the innovations of the Panel time series into account. Nonstationarity volatility arises for instance when there are structural breaks in the innovation variances. A prominent example is the reduction in GDP growth variances enjoyed by many industrialized countries, known as the “Great Moderation”. It also proposes a new testing approach for Panel Unit Roots that is, unlike many previously suggested tests, robust to such volatility processes. The Panel test is based on Simes' (1986) classical multiple test, which combines evidence from time series Unit Root tests of the series in the Panel. As time series Unit Root tests, we employ recently proposed tests of Cavaliere and Taylor (2008b). The Panel test is robust to general patterns of cross-sectional dependence and yet is straightforward to implement, only requiring valid p-values of time series Unit Root tests, and no resampling. Monte Carlo experiments show that other Panel Unit Root tests suff er from sometimes severe size distortions in the presence of nonstationary volatility, and that this defect can be remedied using the test proposed here. We use the methods developed here to test for Unit Roots in OECD Panels of gross domestic products and inflation rates, yielding inference robust to the “Great Moderation”. We find little evidence of trend stationarity, and mixed evidence regarding inflation stationarity.
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A simple nonstationary-volatility robust Panel Unit Root test
Economics Letters, 2012Co-Authors: Matei Demetrescu, Christoph HanckAbstract:We propose an IV Panel Unit Root test robust to nonstationary error volatility. Its finite-sample performance is convincing even for many Units and strong cross-correlation. An application to GDP prices illustrates the inferential impact of nonstationary volatility.
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A Simes-type Panel Unit Root Test
2008Co-Authors: Christoph HanckAbstract:This paper proposes a new Panel Unit Root test based on Simes’ [Biometrika 1986, “An Improved Bonferroni Procedure for Multiple Tests of Significance”] classical intersection test. The test is robust to general patterns of cross-sectional dependence and yet straightforward to implement, only requiring p-values of time series Unit Root tests of the series in the Panel, and no resampling. Monte Carlo experiments show good size and power properties relative to existing tests. Unlike previously suggested Panel Unit Root tests, the new test additionally allows to identify the Units in the Panel for which the alternative of stationarity can be said to hold. We provide two empirical applications to Panels of real gross domestic product (GDP) and real exchange rate data.
Jack Strauss - One of the best experts on this subject based on the ideXlab platform.
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Shortfalls of Panel Unit Root testing
Economics Letters, 2003Co-Authors: Jack Strauss, Taner M. YigitAbstract:Abstract This paper shows that (i) magnitude and variation of contemporaneous correlation are important in Panel Unit Root tests, and (ii) demeaning across the Panel usually does not eliminate these problems.
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Panel Unit-Root Tests of OECD Stochastic Convergence
Review of International Economics, 2001Co-Authors: Adrian R. Fleissig, Jack StraussAbstract:This paper uses three Panel Unit-Root tests and finds that real per capita GDP for OECD countries and a European subsample converge stochastically for the period 1948–87 but not for the entire sample of 1900–87. For the postwar period, the differential in income gaps or speed of adjustment is eliminated at an annual rate of 4–8% for OECD economies, and 6–9% for European economies.
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Panel Unit Root tests of purchasing power parity for price indices
Journal of International Money and Finance, 2000Co-Authors: Adrian R. Fleissig, Jack StraussAbstract:Abstract This paper adopts four Panel Unit Root tests to evaluate PPP over the floating period for six different price indices. Results generally support PPP, albeit the speeds of adjustment differ considerably between price indices and test procedures. The degree of contemporaneous and serial correlation as well as heterogeneity of the series in the Panel affect stationarity and the speed of mean reversion.
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Is OECD real per capita GDP trend or difference stationary? Evidence from Panel Unit Root tests
Journal of Macroeconomics, 1999Co-Authors: Adrian R. Fleissig, Jack StraussAbstract:Panel Unit Root tests are used to evaluate if real per capita GDP for OECD economies are trend or difference stationary. The Panel approaches require that the series in the Panel are independent, but evidence from the correlation matrix of the residuals indicates dependence. The Panel Unit Root procedures are thus adjusted to allow for correlation in the data using different approaches. There is overwhelming evidence that the OECD data are trend stationary using bootstrap methods that accommodate more general forms of serial and cross correlation in the data compared to the standard approach of subtracting cross sectional means.
Helmut Herwartz - One of the best experts on this subject based on the ideXlab platform.
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Panel Unit-Root tests for heteroskedastic Panels
The Stata Journal: Promoting communications on statistics and Stata, 2018Co-Authors: Helmut Herwartz, Simone Maxand, Fabian H.c. Raters, Yabibal M. WalleAbstract:In this article, we describe the command xtpurt, which implements the heteroskedasticity-robust Panel Unit-Root tests suggested in Herwartz and Siedenburg (2008, Computational Statistics and Data Analysis 53: 137–150), Demetrescu and Hanck (2012a, Economics Letters 117: 10–13), and, recently, Herwartz, Maxand, and Walle (2017, Center for European, Governance and Economic Development Research Discussion Papers 314). While the former two tests are robust to time-varying volatility when the data contain only an intercept, the latter test is unique because it is asymptotically pivotal for trending heteroskedastic Panels. Moreover, xtpurt incorporates lag-order selection, prewhitening, and detrending procedures to account for serial correlation and trending data. Buy article (PDF): $14.00 View cart
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Heteroskedasticity Robust Panel Unit Root Testing Under Variance Breaks in Pooled Regressions
Econometric Reviews, 2014Co-Authors: Helmut Herwartz, Florian Siedenburg, Yabibal M. WalleAbstract:Noting that many economic variables display occasional shifts in their second order moments, we investigate the performance of homogenous Panel Unit Root tests in the presence of permanent volatility shifts. It is shown that in this case the test statistic proposed by Herwartz and Siedenburg (2008) is asymptotically standard Gaussian. By means of a simulation study we illustrate the performance of first and second generation Panel Unit Root tests and undertake a more detailed comparison of the test in Herwartz and Siedenburg (2008) and its heteroskedasticity consistent Cauchy counterpart introduced in Demetrescu and Hanck (2012a). As an empirical illustration, we reassess evidence on the Fisher hypothesis with data from nine countries over the period 1961Q2–2011Q2. Empirical evidence supports Panel stationarity of the real interest rate for the entire subperiod. With regard to the most recent two decades, the test results cast doubts on market integration, since the real interest rate is diagnosed nonstat...
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The effects of variance breaks on homogenous Panel Unit Root tests
2009Co-Authors: Helmut Herwartz, Florian SiedenburgAbstract:Noting that many economic variables display occasional shifts in their second order moments, we investigate the performance of homogenous Panel Unit Root tests in the presence of permanent volatility shifts. It is shown that in this case, Panel Unit Root tests derived under time invariant innovation variances lose control over actual significance levels while the test proposed by Herwartz and Siedenburg (2008) retains size control. A simulation study of the finite sample properties confirms the theoretical results in finite samples. As an empirical illustration, we reassess evidence on the Fisher hypothesis.
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homogenous Panel Unit Root tests under cross sectional dependence finite sample modifications and the wild bootstrap
Computational Statistics & Data Analysis, 2008Co-Authors: Helmut Herwartz, F SiedenburgAbstract:First generation Panel Unit Root tests are known to be invalid under cross sectional dependence. Focussing on the subclass of homogenous tests, three extensions of existing approaches are proposed. First, a test based on a generalized variance estimator is suggested for Panels with small time and relatively large cross sectional dimension. Second, the application of refined residuals in variance estimators is proposed to reduce finite sample biases. Third, the wild bootstrap is proved to be an asymptotically valid method of resampling homogenous Panel Unit Root test statistics. A Monte Carlo study shows that the wild bootstrap yields unbiased inference, even in cases where existing procedures are biased. Most accurate results under the null hypothesis are obtained by resampling robust statistics while there is no, or minor, evidence of power loss invoked by the wild bootstrap. An empirical illustration underpins that the current account to GDP ratio is likely Panel stationary.
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Homogenous Panel Unit Root tests under nonstationary volatility
2008Co-Authors: Helmut Herwartz, Florian SiedenburgAbstract:Noting that many economic variables display occasional shifts in their second order moments, we investigate the performance of homogenous Panel Unit Root tests in the presence of permanent volatility shifts. It is shown that in this case, Panel Unit Root tests derived under time invariant variances lose control over actual signiflcance levels while the test proposed by Herwartz and Siedenburg (2008) retains size control. The wild bootstrap is suggested as a general means to overcome the di-culties associated with both, cross sectional dependence and time varying volatility. As an empirical illustration, we reassess evidence on the Fisher hypothesis.
Martin Wagner - One of the best experts on this subject based on the ideXlab platform.
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the performance of Panel Unit Root and stationarity tests results from a large scale simulation study
Econometric Reviews, 2006Co-Authors: Jaroslava Hlouskova, Martin WagnerAbstract:This paper presents results on the size and power of first generation Panel Unit Root and stationarity tests obtained from a large scale simulation study. The tests developed in the following papers are included: Levin et al. (2002), Harris and Tzavalis (1999), Breitung (2000), Im et al. (1997 2003), Maddala and Wu (1999), Hadri (2000), and Hadri and Larsson (2005). Our simulation set-up is designed to address inter alia the following issues. First, we assess the performance as a function of the time and the cross-section dimensions. Second, we analyze the impact of serial correlation introduced by positive MA Roots, known to have detrimental impact on time series Unit Root tests, on the performance. Third, we investigate the power of the Panel Unit Root tests (and the size of the stationarity tests) for a variety of first order autoregressive coefficients. Fourth, we consider both of the two usual specifications of deterministic variables in the Unit Root literature.
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the performance of Panel Unit Root and stationarity tests results from a large scale simulation study
2005Co-Authors: Jaroslava Hlouskova, Martin WagnerAbstract:This paper presents results concerning the size and power of first generation Panel Unit Root and stationarity tests obtained from a large scale simulation study, with in total about 290 million test statistics computed. The tests developed in the following papers are included: Levin, Lin and Chu (2002), Harris and Tzavalis (1999), Breitung (2000), Im, Pesaran and Shin (1997 and 2003), Maddala and Wu (1999), Hadri (2000), and Hadri and Larsson (2005). Our simulation set-up is designed to address i.a. the following issues. First, we assess the performance as a function of the time and the cross-section dimension. Second, we analyze the impact of positive MA Roots on the test performance. Third, we investigate the power of the Panel Unit Root tests (and the size of the stationarity tests) for a variety of first order autoregressive coefficients. Fourth, we consider both of the two usual specifications of deterministic variables in the Unit Root literature.
