Alternative representations for cointegrated panels with global stochastic trends

Gengenbach, Christian; Urbain, Jean‐Pierre; Westerlund, Joakim

doi:10.1016/j.econlet.2012.12.028

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…In order to ensure comparability with the DGP based on moving average errors and guarantee stationarity of the resulting autoregressive errors, we set Ψ 11 = 0.3, Ψ 12 = 0.2, Ψ 22 = 0.6; moreover, we choose a value of Ψ 21,i that produces α i,z = γ i = −0.8 as in the simulations above. For this purpose, we make use of the results in Cappuccio and Lubian (1996) and Gengenbach et al (2013) who derive the conditional and marginal ECM representation for a cointegration model as in (14)- (15). In particular, when the idiosyncratic errors are AR(1) it can be shown that the error correction coefficient in the marginal model has the form:…”

Section: Panel a Ofmentioning

confidence: 99%

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Testing weak exogeneity in cointegrated panels

Moral‐Benito

Servén

2015

Applied Economics

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For reasons of empirical tractability, analysis of cointegrated economic time series is often developed in a partial setting, in which a subset of variables is explicitly modeled conditional on the rest. This approach yields valid inference only if the conditioning variables are weakly exogenous for the parameters of interest. This paper proposes a new test of weak exogeneity in panel cointegration models. The test has a limiting Gumbel distribution that is obtained by first letting T → ∞ and then letting N → ∞. We evaluate the accuracy of the asymptotic approximation in finite samples via simulation experiments. Finally, as an empirical illustration, we test weak exogeneity of disposable income and wealth in aggregate consumption. JEL Codes: C23, C32.

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Section: Panel a Ofmentioning

confidence: 99%

“…These results are not reported to save space, but they are available upon request 16. Hence we do not test other aspects of their specification, such as the fact that cross-sectional dependence is not taken into account 17. To avoid notational clutter we present here an ARDL(1,1,1) version of the model.…”

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confidence: 99%

Testing weak exogeneity in cointegrated panels

Moral‐Benito

Servén

2015

Applied Economics

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“…Remark 3.5. As discussed in Gengenbach et al (2013), the representation used in this chapter can actually be derived as an alternative to the triangular systems representation adopted by . The main difference is that the shortrun dynamics are explicitly treated parametrically.…”

Section: Model and Assumptionsmentioning

confidence: 99%

“…The error correction model allows us to model the short run dynamics explicitly. As pointed out in Gengenbach et al (2013), this model representation for cointegrated panels can be used as an alternative to the triangular representation of . It is also used in Gengenbach et al (2014).…”

Section: Introductionmentioning

confidence: 99%

Estimation and inference in cross-sectionally dependent panel data models

Karabiyik

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Likelihood-based panel cointegration test in the presence of a linear time trend and cross-sectional dependence

Arsova

Örsal

2016

Econometric Reviews

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This paper proposes a new likelihood-based panel cointegration rank test which extends the test ofÖrsal and Droge (2012) (henceforth Panel SL test) to allow for crosssectional dependence. The dependence is modelled by unobserved common factors which affect the variables in each cross-section through heterogeneous loadings. The common components are estimated following the panel analysis of nonstationarity in idiosyncratic and common components (PANIC) approach of Bai and Ng (2004) and the estimates are subtracted from the observations. The cointegrating rank of the defactored data is then tested by the Panel SL test. A Monte Carlo study demonstrates that the proposed testing procedure has reasonable size and power properties in finite samples.JEL classification: C12, C15, C33 Keywords: panel cointegration rank test, cross-sectional dependence, common factors, likelihoodratio, time trend * Financial support by the German Research Foundation (DFG) through the project "Likelihood-based panel cointegration methodology and its applications in macroeconomics and financial market analysis" is gratefully acknowledged. †

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Alternative representations for cointegrated panels with global stochastic trends

Cited by 5 publications

References 18 publications

Testing weak exogeneity in cointegrated panels

Testing weak exogeneity in cointegrated panels

Estimation and inference in cross-sectionally dependent panel data models

Likelihood-based panel cointegration test in the presence of a linear time trend and cross-sectional dependence

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