Developments in the Analysis of Spatial Data

Robinson, Peter

doi:10.14490/jjss.38.87

Cited by 18 publications

(10 citation statements)

References 48 publications

(50 reference statements)

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“…error terms. In case of lack of independence, Robinson (2008Robinson ( , 2011 derives consistency and asymptotic distribution theory for the local constant regression estimator in relation to various kinds of spatial data. Other authors (for example, Xiao et al, 2003;Lin and Carroll, 2000;Ruckstuhl et al, 2000;Wang, 2003) study possible extensions of the nonparametric regression to a non i.i.d.…”

Section: Nonparametric Regression With Dependent Errorsmentioning

confidence: 99%

Distribution Dynamics in the US. A Spatial Perspective

Gerolimetto

Magrini

2016

SSRN Journal

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It is quite common in cross-sectional convergence analyses that data exhibit strong spatial dependence. While the literature adopting the regression approach is now fully aware that neglecting this feature may lead to inaccurate results and has therefore suggested a number of statistical tools for addressing the issue, research is only at a very initial stage within the distribution dynamics approach. In particular, in the continuous state-space framework, a few authors opted for spatial pre-filtering the data in order to guarantee the statistical properties of the estimates. In this paper, we follow an alternative route that starts from the idea that spatial dependence is not just noise but can be a substantive element of the data generating process. In particular, we develop a tool that, building on a mean-bias adjustment procedure established in the literature, explicitly allows for spatial dependence in distribution dynamics analysis thus eliminating the need for pre-filtering. Using this tool, we then reconsider the evidence on convergence across US states. It is quite common in cross-sectional convergence analyses that data exhibit strong spatial dependence. While the literature adopting the regression approach is now fully aware that neglecting this feature may lead to inaccurate results and has therefore suggested a number of statistical tools for addressing the issue, research is only at a very initial stage within the distribution dynamics approach. In particular, in the continuous state-space framework, a few authors opted for spatial pre-filtering the data in order to guarantee the statistical properties of the estimates. In this paper we follow an alternative route that starts from the idea that spatial dependence is not just noise but can be a substantive element of the data generating process. In particular, we develop a tool that, building on a mean-bias adjustment procedure established in the literature, explicitly allows for spatial dependence in distribution dynamics analysis thus eliminating the need for pre-filtering. Using this tool, we then reconsider the evidence on convergence across US states. Keywords

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Section: Nonparametric Regression With Dependent Errorsmentioning

confidence: 99%

Distribution Dynamics in the US. A Spatial Perspective

Gerolimetto

Magrini

2016

SSRN Journal

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“…Geostatistics is the result of introducing spatial dependence into statistical basics. Disregarding spatial dependence can invalidate methods for analyzing cross-sectional and panel data [7]. It was also established along with the already common statistical analysis of the variables, there must be an assessment of how well the models describe the spatial features of the data [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Kriging Infill of Missing Data and Temporal Analysis of Rainfall in North Central Region of Bangladesh

Choudhury¹,

Rahman²

2015

J Climatol Weather Forecasting

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The north-central region of Bangladesh is subjected to rapid land use pattern changes as a result of continuous unplanned urbanization, encroachment of water bodies and agricultural land, filling up of designated wet lands and flood flow zones by real estate companies. There is a growing need to consider rainfall regimes an important factor in assessing drainage network design, flood control work, soil and water conservation planning, watershed management and likes. The historic data of daily rainfall of 7 climate stations in the area comprising 4 districts of Dhaka (Banani, Savar and Dhaka_PBO), Narayanganj (Shimrail), Narsingdi (Narsingdi) and Gazipur (Joydebpur and Maona), 4 of which are for 30 years (1984-2013), 1 is for 21 years (1993-2013) and 2 are for 18 years (1996-2013). An assessment of Kriging infill is conducted and appreciable results confirmed for a missing station from moderate values of nearby observed rainfall. The missing volume of data is identified as 7.95% and infilled by Geostatistical approach Kriging in Arc-GIS 10.1. The Kriging generated data and the observed set of data are both then analyzed station-wise for temporal variation on yearly, monthly and seasonal basis. Kriging yields most significant results for monsoon and pre-monsoon seasons. Localized trends are observed at all stations. Kriging Average Annual Rainfall (AAR) and total rainfall values are higher than observed ones; highest AAR of 2047 mm at Dhaka_PBO and lowest 1322 mm at Joydebpur; the highest monsoon rainfall of 1343 mm is at Maona, the highest pre-monsoon rainfall of 465 mm is at Dhaka_PBO and the highest post-monsoon rainfall of 208 mm is at Shimrail.

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“…We obtain this by normalizing the weights such that each of the rows of the spatial weighting matrix sums up to 1. This normalization offers an interpretation of the weight vector in relative terms and additionally presents the advantage of enhancing dynamic stability (see Robinson 2008).…”

Section: Impulse Response Analysismentioning

confidence: 99%

Spatial propagation of macroeconomic shocks in Europe

2012

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This paper implements a spatial vector autoregressive model that takes into account both the time and the spatial dimensions of economic shocks. We apply this framework to analyze the propagation through space and time of macroeconomic (inflation, output gap and interest rate) shocks in Europe. The empirical analysis identifies an economically and statistically significant spatial component in the transmission of macroeconomic shocks in Europe.

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Developments in the Analysis of Spatial Data

Abstract: Disregarding spatial dependence can invalidate methods for analyzing crosssectional and panel data. We discuss ongoing work on developing methods that allow for, test for, or estimate, spatial dependence. Much of the stress is on nonparametric and semiparametric methods.

Cited by 18 publications

References 48 publications

Distribution Dynamics in the US. A Spatial Perspective

Distribution Dynamics in the US. A Spatial Perspective

Kriging Infill of Missing Data and Temporal Analysis of Rainfall in North Central Region of Bangladesh

Spatial propagation of macroeconomic shocks in Europe

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