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Hashem M Pesaran - One of the best experts on this subject based on the ideXlab platform.
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a bias corrected cd test for error cross sectional Dependence in panel data models with latent factors
Social Science Research Network, 2021Co-Authors: Hashem M Pesaran, Yimeng XieAbstract:In a recent paper Juodis and Reese (2021) (JR) show that the application of the CD test proposed by Pesaran (2004) to residuals from panels with latent factors results in over-rejection and propose a randomized test statistic to correct for over-rejection, and add a screening component to achieve power. This paper considers the same problem but from a different perspective and shows that the standard CD test remains valid if the latent factors are weak, and proposes a simple bias-corrected CD test, labelled CD*, which is shown to be asymptotically normal, irrespective of whether the latent factors are weak or strong. This result is shown to hold for pure latent factor models as well as for panel regressions with latent factors. Small sample properties of the CD* test are investigated by Monte Carlo experiments and are shown to have the correct size and satisfactory power for both Gaussian and non-Gaussian errors. In contrast, it is found that JR's test tends to over-reject in the case of panels with non-Gaussian errors, and have low power against spatial network alternatives. The use of the CD* test is illustrated with two empirical applications from the literature.
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exponent of cross sectional Dependence for residuals
Research Papers in Economics, 2018Co-Authors: Natalia Bailey, George Kapetanios, Hashem M PesaranAbstract:In this paper we focus on estimating the degree of Cross-Sectional Dependence in the error terms of a classical panel data regression model. For this purpose we propose an estimator of the exponent of Cross-Sectional Dependence denoted by α; which is based on the number of non-zero pair-wise cross correlations of these errors. We prove that our estimator, ᾶ; is consistent and derive the rate at which ᾶ approaches its true value. We evaluate the finite sample properties of the proposed estimator by use of a Monte Carlo simulation study. The numerical results are encouraging and supportive of the theoretical findings. Finally, we undertake an empirical investigation of α for the errors of the CAPM model and its Fama-French extensions using 10-year rolling samples from S&P 500 securities over the period Sept 1989 - May 2018.
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exponent of cross sectional Dependence estimation and inference
Journal of Applied Econometrics, 2016Co-Authors: Natalia Bailey, Hashem M Pesaran, George KapetaniosAbstract:Summary This paper provides a characterisation of the degree of Cross-Sectional Dependence in a two dimensional array, {xit,i = 1,2,...N;t = 1,2,...,T} in terms of the rate at which the variance of the Cross-Sectional average of the observed data varies with N. Under certain conditions this is equivalent to the rate at which the largest eigenvalue of the covariance matrix of xt=(x1t,x2t,...,xNt)′ rises with N. We represent the degree of Cross-Sectional Dependence by α, which we refer to as the ‘exponent of Cross-Sectional Dependence’, and define it by the standard deviation, Std(xt)=ONα−1, where xt is a simple Cross-Sectional average of xit. We propose bias corrected estimators, derive their asymptotic properties for α > 1/2 and consider a number of extensions. We include a detailed Monte Carlo simulation study supporting the theoretical results. We also provide a number of empirical applications investigating the degree of inter-linkages of real and financial variables in the global economy. Copyright © 2015 John Wiley & Sons, Ltd.
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a two stage approach to spatio temporal analysis with strong and weak cross sectional Dependence
Journal of Applied Econometrics, 2016Co-Authors: Natalia Bailey, Hashem M Pesaran, Sean HollyAbstract:Summary An understanding of the spatial dimension of economic and social activity requires methods that can separate out the relationship between spatial units that is due to the effect of common factors from that which is purely spatial even in an abstract sense. The same applies to the empirical analysis of networks in general. We use cross-unit averages to extract common factors (viewed as a source of strong Cross-Sectional Dependence) and compare the results with the principal components approach widely used in the literature. We then apply multiple testing procedures to the de-factored observations in order to determine significant bilateral correlations (signifying connections) between spatial units and compare this to an approach that just uses distance to determine units that are neighbours. We apply these methods to real house price changes at the level of Metropolitan Statistical Areas in the USA, and estimate a heterogeneous spatio-temporal model for the de-factored real house price changes and obtain significant evidence of spatial connections, both positive and negative. Copyright © 2015 John Wiley & Sons, Ltd.
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exponent of cross sectional Dependence estimation and inference
Social Science Research Network, 2012Co-Authors: Natalia Bailey, Hashem M Pesaran, George KapetaniosAbstract:An important issue in the analysis of Cross-Sectional Dependence which has received renewed interest in the past few years is the need for a better understanding of the extent and nature of such cross dependencies. In this paper we focus on measures of Cross-Sectional Dependence and how such measures are related to the behaviour of the aggregates defined as Cross-Sectional averages. We endeavour to determine the rate at which the Cross-Sectional weighted average of a set of variables appropriately demeaned, tends to zero. One parameterisation sets this to be O(N^2α-2), for 1/2
Eyup Dogan - One of the best experts on this subject based on the ideXlab platform.
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analyzing the environmental kuznets curve for the eu countries the role of ecological footprint
Environmental Science and Pollution Research, 2018Co-Authors: Mehmet Akif Destek, Recep Ulucak, Eyup DoganAbstract:A great majority of the environmental Kuznets curve (EKC) literature use CO2 emissions to proxy for environmental degradation. However, this is an important shortage in application of the EKC concept because environmental degradation cannot be captured by CO2 emissions only. By using a broader proxy, ecological footprint, this study aims to investigate the presence of environmental Kuznets curve hypothesis for the EU countries. The annual data from 1980 to 2013 is examined with second generation panel data methodologies which take into account the Cross-Sectional Dependence among countries. The results show that there is U-shaped relationship between the real income and ecological footprint. In addition, non-renewable energy increases the environmental degradation while renewable energy and trade openness decrease the environmental degradation in the EU countries. Policy implications are further discussed.
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investigating the impacts of energy consumption real gdp tourism and trade on co2 emissions by accounting for cross sectional Dependence a panel study of oecd countries
Current Issues in Tourism, 2017Co-Authors: Eyup Dogan, Fahri Seker, Serap BulbulAbstract:The objective of this study is to analyse the long-run dynamic relationship of carbon dioxide emissions, real gross domestic product (GDP), the square of real GDP, energy consumption, trade and tourism under an Environmental Kuznets Curve (EKC) model for the Organization for Economic Co-operation and Development (OECD) member countries. Since we find the presence of Cross-Sectional Dependence within the panel time-series data, we apply second-generation unit root tests, cointegration test and causality test which can deal with Cross-Sectional Dependence problems. The Cross-Sectionally augmented Dickey-Fuller (CADF) and the Cross-Sectionally augmented Im-Pesaran-Shin (CIPS) unit root tests indicate that the analysed variables become stationary at their first differences. The Lagrange multiplier bootstrap panel cointegration test shows the existence of a long-run relationship between the analysed variables. The dynamic ordinary least squares (DOLS) estimation technique indicates that energy consumption and ...
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exploring the relationship among co2 emissions real gdp energy consumption and tourism in the eu and candidate countries evidence from panel models robust to heterogeneity and cross sectional Dependence
Renewable & Sustainable Energy Reviews, 2017Co-Authors: Eyup Dogan, Alper AslanAbstract:A major criticism to the existing energy-growth-environment literature, we notice, is the selection of methodology. Panel estimation techniques that fail to consider both heterogeneity and Cross-Sectional Dependence across countries may cause forecasting errors. The other concern related to the literature is that only a small number of studies analyze the influence of tourism on CO2 emissions even though tourism sector has potential for affecting the environment. To fulfill the mentioned gaps in the literature, this study analyzes the relationship among carbon emissions, real income, energy consumption and tourism for a panel of the EU and candidate countries over the period 1995–2011 by using heterogeneous panel estimation techniques with Cross-Sectional Dependence. Results from the CADF and the CIPS panel unit root tests show that the analyzed variables become stationary at their first-differences. The LM bootstrap panel cointegration test indicates the presence of a long run relationship among the analyzed variables. Results from the OLS with fixed effects, the FMOLS, the DOLS and the group-mean estimator reveal that energy consumption contributes to the level of emissions while real income and tourism mitigate CO2 emissions. The Emirmahmutoglu-Kose panel Granger causality test suggests that there is one-way causality running from tourism to carbon emissions, and two-way causality between CO2 emissions and energy consumption, and between real income and CO2 emissions. Policy implications are further discussed.
George Kapetanios - One of the best experts on this subject based on the ideXlab platform.
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common correlated effect cross sectional Dependence corrections for nonlinear conditional mean panel models
Journal of Applied Econometrics, 2021Co-Authors: Sinem Hacioglu Hoke, George KapetaniosAbstract:This paper provides an approach to estimation and inference for nonlinear conditional mean panel data models, in the presence of cross‐sectional Dependence. We modify Pesaran's (Econometrica, 2006, 74(4), 967–1012) common correlated effects correction to filter out the interactive unobserved multifactor structure. The estimation can be carried out using nonlinear least squares, by augmenting the set of explanatory variables with cross‐sectional averages of both linear and nonlinear terms. We propose pooled and mean group estimators, derive their asymptotic distributions, and show the consistency and asymptotic normality of the coefficients of the model. The features of the proposed estimators are investigated through extensive Monte Carlo experiments. We also present two empirical exercises. The first explores the nonlinear relationship between banks' capital ratios and riskiness. The second estimates the nonlinear effect of national savings on national investment in OECD countries depending on countries' openness.
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Exponent of Cross-Sectional Dependence for Residuals
Sankhya B, 2019Co-Authors: Natalia Bailey, George Kapetanios, M. Hashem PesaranAbstract:In this paper, we focus on estimating the degree of Cross-Sectional Dependence in the error terms of a classical panel data regression model. For this purpose we propose an estimator of the exponent of Cross-Sectional Dependence denoted by α , which is based on the number of non-zero pair-wise cross correlations of these errors. We prove that our estimator , α ~ $, \tilde {\alpha }$ , is consistent and derive the rate at which it approaches its true value. We also propose a resampling procedure for the construction of confidence bounds around the estimator of α . We evaluate the finite sample properties of the proposed estimator by use of a Monte Carlo simulation study. The numerical results are encouraging and supportive of the theoretical findings. Finally, we undertake an empirical investigation of α for the errors of the CAPM model and its Fama-French extensions using 10-year rolling samples from S&P 500 securities over the period Sept 1989 - May 2018.
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exponent of cross sectional Dependence for residuals
Research Papers in Economics, 2018Co-Authors: Natalia Bailey, George Kapetanios, Hashem M PesaranAbstract:In this paper we focus on estimating the degree of Cross-Sectional Dependence in the error terms of a classical panel data regression model. For this purpose we propose an estimator of the exponent of Cross-Sectional Dependence denoted by α; which is based on the number of non-zero pair-wise cross correlations of these errors. We prove that our estimator, ᾶ; is consistent and derive the rate at which ᾶ approaches its true value. We evaluate the finite sample properties of the proposed estimator by use of a Monte Carlo simulation study. The numerical results are encouraging and supportive of the theoretical findings. Finally, we undertake an empirical investigation of α for the errors of the CAPM model and its Fama-French extensions using 10-year rolling samples from S&P 500 securities over the period Sept 1989 - May 2018.
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exponent of cross sectional Dependence estimation and inference
Journal of Applied Econometrics, 2016Co-Authors: Natalia Bailey, Hashem M Pesaran, George KapetaniosAbstract:Summary This paper provides a characterisation of the degree of Cross-Sectional Dependence in a two dimensional array, {xit,i = 1,2,...N;t = 1,2,...,T} in terms of the rate at which the variance of the Cross-Sectional average of the observed data varies with N. Under certain conditions this is equivalent to the rate at which the largest eigenvalue of the covariance matrix of xt=(x1t,x2t,...,xNt)′ rises with N. We represent the degree of Cross-Sectional Dependence by α, which we refer to as the ‘exponent of Cross-Sectional Dependence’, and define it by the standard deviation, Std(xt)=ONα−1, where xt is a simple Cross-Sectional average of xit. We propose bias corrected estimators, derive their asymptotic properties for α > 1/2 and consider a number of extensions. We include a detailed Monte Carlo simulation study supporting the theoretical results. We also provide a number of empirical applications investigating the degree of inter-linkages of real and financial variables in the global economy. Copyright © 2015 John Wiley & Sons, Ltd.
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a nonlinear panel data model of cross sectional Dependence
Journal of Econometrics, 2014Co-Authors: George Kapetanios, James B Mitchell, Yongcheol ShinAbstract:This paper proposes a new panel model of Cross-Sectional Dependence. The model has a number of potential structural interpretations that relate to economic phenomena such as herding in nancial markets. On an econometric level, it provides a exible approach of modelling interactions across panel units and can generate endogenous Cross-Sectional Dependence that can resemble the Dependence that arises in a variety of existing models, such as factor or spatial models. We discuss the theoretical properties of the model and ways in which inference can be carried out. We supplement this analysis with a detailed Monte Carlo study and two empirical illustrations.
Diego Romeroavila - One of the best experts on this subject based on the ideXlab platform.
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questioning the empirical basis of the environmental kuznets curve for co2 new evidence from a panel stationarity test robust to multiple breaks and cross Dependence
Ecological Economics, 2008Co-Authors: Diego RomeroavilaAbstract:Abstract This paper investigates the time series properties of per capita CO2 emissions and per capita GDP levels for a sample of 86 countries over the period 1960–2000. For that purpose, we employ a state-of-the-art panel stationarity test which incorporates multiple shifts in level and slope, thereby controlling for Cross-Sectional Dependence through bootstrap methods. Our analysis renders clear-cut evidence that per capita GDP levels are nonstationary for the world as a whole while per capita CO2 is found to be regime-wise trend stationary. The analysis of country-groups shows that for Africa and Asia, per capita CO2 is best described as nonstationary, while per capita GDP appears stationary around a broken trend. In addition, we find evidence of regime-wise trend stationarity in both variables for the country-groups consisting of America, Europe and Oceania. The results of our analysis carry important implications for the statistical modelling of the Environmental Kuznets curve for CO2, since the differing order of integration in both variables for the world as a whole and for Africa and Asia calls into question the validity of panel cointegration techniques which assume that both variables are nonstationary and cointegrated with one another. Cointegration techniques would not be appropriate either for the case of America, Europe and Oceania which are characterised by per capita GDP and CO2 emissions being stationary around a broken trend. Similar conclusions are reached when we analyse country-groups based on levels of development. Failure to properly characterise the time series properties of the data by not controlling for an unknown number of structural breaks and for Cross-Sectional Dependence could be responsible for the fragility and lack of robustness surrounding the estimation of environmental Kuznets curves.
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unit roots and persistence in the nominal interest rate a confirmatory analysis applied to the oecd
Canadian Journal of Economics, 2007Co-Authors: Diego RomeroavilaAbstract:This paper investigates the stochastic properties of long-term and short-term nominal interest rates for the OECD over the post-war era. For that purpose, we employ univariate unit root tests as well as panel unit root and stationarity tests that explicitly allow for Cross-Sectional Dependence. Overall, we find overwhelming evidence that the nominal interest rate contains a unit root, which may be driven by a stochastic common factor. The computation of half-lives through impulse-response functions also points to a high degree of persistence. This has important implications for the cointegration analysis of the Fisher equation, the uncovered interest parity, and the term structure.
Brendan Mccabe - One of the best experts on this subject based on the ideXlab platform.
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panel stationarity tests for purchasing power parity with cross sectional Dependence
Social Science Research Network, 2006Co-Authors: David Harris, Stephen J Leybourne, Brendan MccabeAbstract:We investigate the purchasing power parity hypothesis for a group of 17 countries using a new panel based test of stationarity that allows for arbitrary Cross-Sectional Dependence. We treat the short run time series dynamics non-parametrically and thus avoid the need to fit separate, and potentially misspecified, models for the individual series. The statistic is simple to compute and uses standard Normal critical values, even in the presence of a wide range of deterministic components. We also show how the test can be applied using an approximate factor model for cross sectional Dependence. Taken together, these features provide a generally applicable solution to the problem of testing for stationarity versus unit roots in macro-panel based data. The tests find significant evidence against the purchasing power parity hypothesis being true.
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panel stationarity tests for purchasing power parity with cross sectional Dependence
Journal of Business & Economic Statistics, 2005Co-Authors: David Harris, Stephen J Leybourne, Brendan MccabeAbstract:We investigate the purchasing power parity (PPP) hypothesis for a group of 17 countries using a new panel-based test of stationarity that allows for arbitrary Cross-Sectional Dependence. We treat the short-run time series dynamics nonparametrically and thus avoid the need to fit separate, and potentially misspecified, models for the individual series. The statistic is simple to compute and uses standard normal critical values, even in the presence of a wide range of different deterministic components. We also evaluate the behavior of the test using a factor model to approximate Cross-Sectional Dependence and find that it generally improves finite-sample performance. Taken together, these features provide a widely applicable solution to the problem of testing for stationarity versus unit roots in macro-panel data. The test finds significant evidence against the PPP null hypothesis being true, even when allowance is made for structural breaks.