Using Web-Data to Estimate Spatial Regression Models
Macro econometrics has been recently affected by the so-called ‘Google Econometrics’. Comparatively less attention has been paid to the subject by the regional and spatial sciences where the Big Data revolution is challenging the conventional econometric techniques with the availability of a variety...
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| Vydáno v: | International regional science review Ročník 47; číslo 2; s. 204 - 226 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Los Angeles, CA
SAGE Publications
01.03.2024
Sage Publications Ltd |
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| ISSN: | 0160-0176, 1552-6925 |
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| Abstract | Macro econometrics has been recently affected by the so-called ‘Google Econometrics’. Comparatively less attention has been paid to the subject by the regional and spatial sciences where the Big Data revolution is challenging the conventional econometric techniques with the availability of a variety of non- traditionally collected data (such as, e. g., crowdsourcing, web scraping, etc) which are almost invariably geo-coded. However, these unconventionally collected data represent only what in statistics is known as a “convenience sample” that does not allow any sound probabilistic inference. This paper aims at making aware researchers of the consequence of the unwise use of such data in the applied work and to propose a technique to minimize such the negative effects in the estimation of spatial regression. The method consists of manipulating the data prior their use in an inferential context. |
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| AbstractList | Macro econometrics has been recently affected by the so-called ‘Google Econometrics’. Comparatively less attention has been paid to the subject by the regional and spatial sciences where the Big Data revolution is challenging the conventional econometric techniques with the availability of a variety of non- traditionally collected data (such as, e. g., crowdsourcing, web scraping, etc) which are almost invariably geo-coded. However, these unconventionally collected data represent only what in statistics is known as a “convenience sample” that does not allow any sound probabilistic inference. This paper aims at making aware researchers of the consequence of the unwise use of such data in the applied work and to propose a technique to minimize such the negative effects in the estimation of spatial regression. The method consists of manipulating the data prior their use in an inferential context. |
| Author | Arbia, Giuseppe Nardelli, Vincenzo |
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| Cites_doi | 10.1080/01621459.2019.1677241 10.1080/13658816.2017.1346255 10.1080/01621459.1992.10475217 10.1111/insr.12434 10.1007/978-94-009-2395-9 10.1016/j.econmod.2012.07.017 10.2307/1403632 10.1177/0160017616650488 10.1257/jep.30.2.151 10.1007/s10708-007-9111-y 10.1080/01621459.1993.10476368 10.1007/978-3-030-73030-7 10.1177/01600176211059981 10.2196/jmir.9330 10.1111/j.2517-6161.1983.tb01224.x 10.1057/9780230244405_26 10.1080/17421771003730703 10.1016/j.spasta.2021.100568 10.1002/env.2194 10.1111/insr.12290 10.1201/9781420064254 10.1007/978-981-10-2762-8 10.1016/j.econmod.2016.12.002 10.1111/rssb.12354 10.4324/9781315735276 10.1177/0739456X16664789 10.1177/152582202237725 10.1007/s12076-011-0065-9 10.1007/978-94-015-7799-1 10.1016/0166-0462(92)90039-4 10.17713/ajs.v44i2.79 10.2139/ssrn.1438286 10.1057/9781137317940 10.1080/17421770600661337 10.1111/j.1468-0262.2004.00558.x 10.1214/16-STS598 10.1016/j.econmod.2019.04.003 10.1177/0160017618821428 |
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| Keywords | spatial microeconometrics spatial regression webscraping crowdsourcing big data |
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| Title | Using Web-Data to Estimate Spatial Regression Models |
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