Statistical inference on regression with spatial dependence

Robinson, Peter; Thawornkaiwong, Supachoke

doi:10.1016/j.jeconom.2011.09.033

Cited by 25 publications

(9 citation statements)

References 23 publications

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“…This is a new result in the literature on international R&D spillovers. In our view, this finding is consistent with the stream of literature suggesting that a critical mass of investments in technology and research is necessary to make such research effective (see, for example, Röller and Waverman, ). Moreover, the result complements Eberhardt et al.…”

Section: Resultssupporting

confidence: 91%

Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion

Ertur

Musolesi

2016

J of Applied Econometrics

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Summary This paper provides an econometric examination of technological knowledge spillovers among countries by focusing on the issue of error cross‐sectional dependence, particularly on the different ways—weak and strong—that this dependence may affect model specification and estimation. A preliminary analysis based on estimation of the exponent of cross‐sectional dependence provides a clear result in favor of strong cross‐sectional dependence. This result has relevant implications in terms of econometric modeling and suggests that a factor structure is preferable to a spatial error model. The common correlated effects approach is then used because it remains valid in a variety of situations that are likely to occur, such as the presence of both forms of dependence or the existence of nonstationary factors. According to the estimation results, richer countries benefit more from domestic R&D and geographic spillovers than poorer countries, while smaller countries benefit more from spillovers originating from trade. The results also suggest that when the problem of (possibly many) correlated unobserved factors is addressed the quantity of education no longer has a significant effect. Finally, a comparison of the results with those obtained from a spatial model provides interesting insights into the bias that may arise when we allow only for weak dependence, despite the presence of strong dependence in the data. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Resultssupporting

confidence: 91%

Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion

Ertur

Musolesi

2016

J of Applied Econometrics

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“…Indeed, time series versions may be regarded as very special cases but, as stressed before, the features of spatial dependence must be taken into account in the general formulation. Such a representation was introduced by Robinson (2011) and further examined in other situations by Robinson and Thawornkaiwong (2012) (partially linear regression), Delgado and Robinson (2015) (non-nested correlation testing), Lee and Robinson (2016) (series estimation of nonparametric regression) and Hidalgo and Schafgans (2017) (cross-sectionally dependent panels).…”

Section: Introductionmentioning

confidence: 99%

Consistent specification testing under spatial dependence

Gupta¹,

Qu²

2021

Preprint

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We propose a series-based nonparametric specification test for a regression function when data are spatially dependent, the 'space' being of a general economic or social nature. Dependence can be parametric, parametric with increasing dimension, semiparametric or any combination thereof, thus covering a vast variety of settings.These include spatial error models of varying types and levels of complexity. Under a new smooth spatial dependence condition, our test statistic is asymptotically standard normal. To prove the latter property, we establish a central limit theorem for quadratic forms in linear processes in an increasing dimension setting. Finite sample performance is investigated in a simulation study and empirical examples illustrate the test with real-world data.

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“…Delgado and Robinson (2015) and Robinson (2008) derive tests for spatial dependence in the error term of regressions. Prucha (2007, 2010) and Robinson and Thawornkaiwong (2012) propose HAC asymptotic variance estimators in settings with spatial dependence. There is also a large literature on inference for spatial autoregressive models, see, for example, Robinson (2015, 2018), Kelejian and Prucha (1999), Lee (2002Lee ( , 2003Lee ( , 2004, Lee and Liu (2010), Robinson (2010), Su and Jin (2010) and Xu and Lee (2015).…”

Section: Introductionmentioning

confidence: 99%

Spatial Dependence in Option Observation Errors

Andersen¹,

Fusari

Todorov³

et al. 2020

Econom. Theory

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In this paper, we develop the first formal nonparametric test for whether the observation errors in option panels display spatial dependence. The panel consists of options with different strikes and tenors written on a given underlying asset. The asymptotic design is of the infill type—the mesh of the strike grid for the observed options shrinks asymptotically to zero, while the set of observation times and tenors for the option panel remains fixed. We propose a Portmanteau test for the null hypothesis of no spatial autocorrelation in the observation error. The test makes use of the smoothness of the true (unobserved) option price as a function of its strike and is robust to the presence of heteroskedasticity of unknown form in the observation error. A Monte Carlo study shows good finite-sample properties of the developed testing procedure and an empirical application to S&P 500 index option data reveals mild spatial dependence in the observation error, which has been declining in recent years.

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Statistical inference on regression with spatial dependence

Cited by 25 publications

References 23 publications

Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion

Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion

Consistent specification testing under spatial dependence

Spatial Dependence in Option Observation Errors

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