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383
A Simple Panel Unit Root Test in the Presence of Cross Section Dependence
 JOURNAL OF APPLIED ECONOMETRICS
, 2006
"... 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 thi ..."
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Cited by 372 (16 self)
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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 firstdifferences of the individual series. New asymptotic results are obtained both for the individual cross sectionally augmented ADF (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.
Panel Data Models with Interactive Fixed Effects
, 2005
"... This paper considers large N and large T panel data models with unobservable multiple interactive effects. These models are useful for both micro and macro econometric modelings. In earnings studies, for example, workers ’ motivation, persistence, and diligence combined to influence the earnings in ..."
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Cited by 125 (6 self)
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This paper considers large N and large T panel data models with unobservable multiple interactive effects. These models are useful for both micro and macro econometric modelings. In earnings studies, for example, workers ’ motivation, persistence, and diligence combined to influence the earnings in addition to the usual argument of innate ability. In macroeconomics, the interactive effects represent unobservable common shocks and their heterogeneous responses over cross sections. Since the interactive effects are allowed to be correlated with the regressors, they are treated as fixed effects parameters to be estimated along with the common slope coefficients. The model is estimated by the least squares method, which provides the interactiveeffects counterpart of the within estimator. We first consider model identification, and then derive the rate of convergence and the limiting distribution of the interactiveeffects estimator of the common slope coefficients. The estimator is shown to be √ NT consistent. This rate is valid even in the presence of correlations and heteroskedasticities in both dimensions, a striking contrast with fixed T framework in which serial correlation and heteroskedasticity imply unidentification. The asymptotic distribution is not necessarily centered at zero. Biased corrected estimators are derived. We also derive the constrained estimator and its limiting distribution, imposing additivity coupled with interactive effects. The problem of testing additive versus interactive effects is also studied. We also derive identification conditions for models with grand mean, timeinvariant regressors, and common regressors. It is shown that there exists a set of necessary and sufficient identification conditions for those models. Given identification, the rate of convergence and limiting results continue to hold. Key words and phrases: incidental parameters, additive effects, interactive effects, factor
Panels with Nonstationary Multifactor Error Structures ∗
, 2008
"... The presence of crosssectionally correlated error terms invalidates much inferential theory of panel data models. Recently work by Pesaran (2006) has suggested a method which makes use of crosssectional averages to provide valid inference in the case of stationary panel regressions with a multifac ..."
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Cited by 74 (10 self)
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The presence of crosssectionally correlated error terms invalidates much inferential theory of panel data models. Recently work by Pesaran (2006) has suggested a method which makes use of crosssectional averages to provide valid inference in the case of stationary panel regressions with a multifactor error structure. This paper extends this work and examines the important case where the unobservable common factors follow unit root processes and could be cointegrated. The extension of the results of Pesaran (2006) to the I(1) is remarkable on two counts. Firstly, it is of great interest to note that while intermediate results needed for deriving the asymptotic distribution of the panel estimators differ between the I(1) and I(0) cases, the final results are surprisingly similar. This is in direct contrast to the standard distributional results for I(1) processes that radically differ from those for I(0) processes. Secondly, it is worth noting the significant extra technical demands required to prove the new results. The theoretical findings are further supported for small samples via an extensive Monte Carlo study. In particular, the results of the Monte Carlo study suggest that the crosssectional average based method is robust to a wide variety of data generation processes and has lower biases than the alternative estimation methods considered in the paper.
Unit Roots and Cointegration in Panels
, 2007
"... This paper provides a review of the literature on unit roots and cointegration in panels where the time dimension (T), and the cross section dimension (N) are relatively large. It distinguishes between the first generation tests developed on the assumption of the cross section independence, and the ..."
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Cited by 54 (3 self)
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This paper provides a review of the literature on unit roots and cointegration in panels where the time dimension (T), and the cross section dimension (N) are relatively large. It distinguishes between the first generation tests developed on the assumption of the cross section independence, and the second generation tests that allow, in a variety of forms and degrees, the dependence that might prevail across the different units in the panel. In the analysis of cointegration the hypothesis testing and estimation problems are further complicated by the possibility of cross section cointegration which could arise if the unit roots in the different cross section units are due to common random walk components.
Large panels with common factors and spatial correlations
 IZA DISCUSSION PAPER
, 2007
"... This paper considers the statistical analysis of large panel data sets where even after conditioning on common observed effects the cross section units might remain dependently distributed. This could arise when the cross section units are subject to unobserved common effects and/or if there are spi ..."
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Cited by 52 (5 self)
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This paper considers the statistical analysis of large panel data sets where even after conditioning on common observed effects the cross section units might remain dependently distributed. This could arise when the cross section units are subject to unobserved common effects and/or if there are spill over effects due to spatial or other forms of local dependencies. The paper provides an overview of the literature on cross section dependence, introduces the concepts of timespecific weak and strong cross section dependence and shows that the commonly used spatial models are examples of weak cross section dependence. It is then established that the Common Correlated Effects (CCE) estimator of panel data model with a multifactor error structure, recently advanced by Pesaran (2006), continues to provide consistent estimates of the slope coefficient, even in the presence of spatial error processes. Small sample properties of the CCE estimator under various patterns of cross section dependence, including spatial forms, are investigated by Monte Carlo experiments. Results show that the CCE approach works well in the presence of weak and/or strong cross sectionally correlated errors. We also explore the role of certain characteristics of spatial processes in determining the performance of CCE estimators, such as the form and intensity of spatial dependence, and the sparseness of the spatial weight matrix.
Lumpy price adjustments: a microeconometric analysis", by
, 2006
"... This paper presents a simple model of statedependent pricing that allows identi cation of the relative importance of the degree of price rigidity that is inherent to the price setting mechanism (intrinsic) and that which is due to the prices driving variables (extrinsic). Using two data sets consi ..."
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Cited by 49 (4 self)
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This paper presents a simple model of statedependent pricing that allows identi cation of the relative importance of the degree of price rigidity that is inherent to the price setting mechanism (intrinsic) and that which is due to the prices driving variables (extrinsic). Using two data sets consisting of a large fraction of the price quotes used to compute the Belgian and French CPI, we are able to assess the role of intrinsic and extrinsic price stickiness in explaining the occurrence and magnitude of price changes at the outlet level. We
nd that infrequent price changes are not necessarily associated with large adjustment costs. Indeed, extrinsic rigidity appears to be signi
cant in many cases. We also
nd that asymmetry in the price adjustment could be due to trends in marginal costs and/or desired markups rather than asymmetric cost of adjustment bands. JEL Classi
cations: C51, C81, D21.
Capital Accumulation and Growth: A New Look at the Empirical Evidence,” unpublished
, 2004
"... We present evidence that an increase in investment as a share of GDP predicts a higher growth rate of output per worker, not only temporarily, but also in the steady state. These results are found using pooled annual data for a large panel of countries, using pooled data for nonoverlapping fiveyea ..."
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Cited by 48 (0 self)
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We present evidence that an increase in investment as a share of GDP predicts a higher growth rate of output per worker, not only temporarily, but also in the steady state. These results are found using pooled annual data for a large panel of countries, using pooled data for nonoverlapping fiveyear periods, or allowing for heterogeneity across countries in regression coefficients. They are robust to model specifications and estimation methods. The evidence that investment has a longrun effect on growth rates is consistent with the main implication of certain endogenous growth models, such as the AK model.
Estimation and inference in short panel vector autoregressions with unit roots and cointegration
, 2003
"... This paper considers estimation and inference in panel vector autoregressions (PVARs) where (i) the individual effects are either random or fixed, (ii) the timeseries properties of the model variables are unknown a priori and may feature unit roots and cointegrating relations, and (iii) the time di ..."
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Cited by 47 (1 self)
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This paper considers estimation and inference in panel vector autoregressions (PVARs) where (i) the individual effects are either random or fixed, (ii) the timeseries properties of the model variables are unknown a priori and may feature unit roots and cointegrating relations, and (iii) the time dimension of the panel is short and its crosssectional dimension is large. Generalized Method of Moments (GMM) and Quasi Maximum Likelihood (QML) estimators are obtained andthencomparedintermsoftheirasymptoticandfinite sample properties. It is shown that GMM estimators based only on standard orthogonality conditions break down if the underlying time series contain unit roots. Extended GMM estimators making use of further moment conditions are not subject to this problem. However, their finite sample performance is shown to deteriorate as a ratio of crosssection to timeseries variation is increased, while the performance of the fixed effects QML estimator is invariant to this ratio. The QML estimators also tend to outperform the various GMM estimators in finite sample. Overall, our findings favor the use of the fixed effects QML estimator, given that it does not impose any restrictions on the distribution generating the individual effects. The paper also shows how the fixed effects QML