Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large

Yu, Jihai; Jong, Robert de; Lee, Lung‐fei

doi:10.1016/j.jeconom.2008.08.002

Cited by 481 publications

(397 citation statements)

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“…Lee (2004) derives asymptotic properties of the Quasi MLE (QMLE) for n → ∞, and Hillier and Martellosio (2013) investigate its finite sample distribution. Large n and large T asymptotics for QMLE of the spatial model with static dependence parameter are studied in Yu et al (2008). For further textbook treatments of spatial econometric models and their estimation, we refer to Anselin (1988) and LeSage and Pace (2008).…”

Section: Static Spatial Lag Model For Panel Datamentioning

confidence: 99%

Spillover dynamics for systemic risk measurement using spatial financial time series models

Blasques

Koopman

Schaumburg

2016

Journal of Econometrics

108

106

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe introduce a new model for time-varying spatial dependence. The model extends the wellknown static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the static parameters is consistent and asymptotically normal. We also study the information theoretic optimality of the updating steps for the time-varying spatial dependence parameter. We adopt the model to empirically investigate the spatial dependence between eight European sovereign CDS spreads over the period [2009][2010][2011][2012][2013][2014], which includes the European sovereign debt crisis. We construct our spatial weight matrix using cross-border lending data and include country-specific and Europe-wide risk factors as controls. We find a high, time-varying degree of spatial spillovers in the sovereign CDS spread data. There is a downturn in spatial dependence after the first half of 2012, which is consistent with policy measures taken by the European Central Bank. The findings are robust to a wide range of alternative model specifications.

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Section: Static Spatial Lag Model For Panel Datamentioning

confidence: 99%

Spillover dynamics for systemic risk measurement using spatial financial time series models

Blasques

Koopman

Schaumburg

2016

Journal of Econometrics

108

106

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“…The point estimates obtained from using the matrices suggested by Janeba and Osterloh (2013) (2) and include the rst time lag as well as the rst time lag of the spatial lag. Yu, de Jong, and Lee (2008) derive this dynamic estimator with spatial xed e ects. Lee and Yu (2010c) extend this routine to additionally include time xed e ects.…”

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confidence: 99%

“…We estimate (3) using the estimator suggested in Lee and Yu (2010c) and the SFD model using the estimator proposed in Yu, de Jong, and Lee (2008). Due to the fact that T is rather small in our panels, we additionally consider a dynamic model in a GMM framework as proposed by Arellano and Bover (1995) and Blundell and Bond (1998).…”

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Race to the debt trap? — Spatial econometric evidence on debt in German municipalities

Borck

Fossen

Freier

et al. 2015

Regional Science and Urban Economics

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“…There are two predominant approaches to specifying the spatial model: One can either include a spatially weighted dependent variable (the so-called "spatial lag model") or a spatially autocorrelated error ("spatial error model") into the regression model. These approaches were originally focused on crosssectional (Anselin, 1988;Anselin and Bera, 1998;Anselin, 2006) and static panel datasets (Elhorst, 2003) and they have been extended to the case of dynamic panel estimators (Badinger, Müller and Tondl, 2004;Yu, de Jong and Lee, 2008). Recently, further approaches have been introduced, such as including both spatial lag and spatial error simultaneously (Kelejian and Prucha, 1998;Lee, 2003) or including spatially weighted independent variables (the so-called spatial Durban model, see, e.g., Elhorst, Piras and Arbia, 2006;Ertur and Koch, 2007).…”

Section: Main Econometric Issues and Potential Solutionsmentioning

confidence: 99%

Econometric Evaluation of EU Cohesion Policy: A Survey

Hagen

Mohl²

2016

Empirical Evidence on the Macroeconomic Effects of EU Cohesion Policy

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Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW.Download this ZEW Discussion Paper from our ftp server:ftp://ftp.zew.de/pub/zew-docs/dp/dp09052.pdf Non-technical summaryMore than one third of the total EU budget is spent on so-called Cohesion Policy via the structural funds. Its main purpose is to reduce disparities among EU regions and to promote economic growth and convergence. Therefore, the convergence process of EU regions is a question of high political importance. The overall empirical evidence points to a small convergence effect of all or at least some European regions. However, whether or not this potential success results from EU Cohesion Policy remains an open question. The existing empirical evidence has provided mixed, if not contradictory, results. While some authors do find evidence of a positive impact of structural funds on economic growth, others find little to no impact at all. Against this background, this paper provides a fundamental review of the econometric evaluation of EU Cohesion Policy in order to shed light on the reasons for the diverging results. It has been shown that the econometric evaluation of EU Cohesion Policy is hampered by several econometric issues. Based on these issues we discuss potential solutions on how to cope with these problems and discuss the related literature.The most that can be concluded from empirical studies using country-level data is that Cohesion Policy seems to be only conditionally effective. Given a good quality institutional setup or decentralised governmental structures, Cohesion Policy has a positive impact on growth. However, using regional level data might be the preferable alternative because, first, EU Cohesion Policy focuses on the development and convergence of regions and, second, the robustness of the results is increased by the higher number of cross sections. The majority of the studies based on EU regions find at least a weak positive effect.One explanation for the weak results might be the fact that almost all studies are derived from a neoclassical growth model assuming that EU Cohesion Policy increases investments, and ultimately raising the economic growth rate. However, there is some empirical evidence that Cohesion Policy may only have a modest impact on investments. Moreover, we know very little about the labour market impact of EU Cohesion Policy. Hence, one task for future studies will be to investigate more thoroughly the channels through i which EU Cohesion Policy works.Another reason for the inconclusive empirical results m...

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Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large

Cited by 481 publications

References 34 publications

Spillover dynamics for systemic risk measurement using spatial financial time series models

Spillover dynamics for systemic risk measurement using spatial financial time series models

Race to the debt trap? — Spatial econometric evidence on debt in German municipalities

Econometric Evaluation of EU Cohesion Policy: A Survey

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