Localized autocorrelation diagnostic statistic (LADS) for spatial models: Conceptualization, utilization, and computation
This paper proposes a regression diagnostic, the localized autocorrelation diagnostic statistic (LADS), that differs from traditional autocorrelation diagnostics in that: (1) it is concerned with localized occurrences of spatial autocorrelation; (2) each localized occurrence is assumed to result fro...
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| Veröffentlicht in: | Regional science and urban economics 1992-09, Vol.22 (3), p.333-346 |
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| creator | Nass, Clifford Garfinkle, David |
| description | This paper proposes a regression diagnostic, the localized autocorrelation diagnostic statistic (LADS), that differs from traditional autocorrelation diagnostics in that: (1) it is concerned with localized occurrences of spatial autocorrelation; (2) each localized occurrence is assumed to result from a potentially distinct problem in model specification; and (3) problems are deduced post hoc from the particular geographic units implicated by the statistic. LADS (
N ×
N,
E,
C) is the probability that in a regression model with
N spatial units, a contiguous block of
C or more residuals among the
E most extreme, same-signed residuals occurred by chance. LADS can help identify omitted independent variables, distinct regimes, and error heteroskedasticity. Two algorithms for the computation of LADS and various LADS(
N,
E,
C)s for state-level models of the United States are provided. |
| doi_str_mv | 10.1016/0166-0462(92)90033-W |
| format | Article |
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N ×
N,
E,
C) is the probability that in a regression model with
N spatial units, a contiguous block of
C or more residuals among the
E most extreme, same-signed residuals occurred by chance. LADS can help identify omitted independent variables, distinct regimes, and error heteroskedasticity. Two algorithms for the computation of LADS and various LADS(
N,
E,
C)s for state-level models of the United States are provided.</description><identifier>ISSN: 0166-0462</identifier><identifier>EISSN: 1879-2308</identifier><identifier>DOI: 10.1016/0166-0462(92)90033-W</identifier><identifier>CODEN: RGUEA3</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Algorithms ; Economic models ; Economic Theory ; Regression analysis ; Spatial models ; Statistics ; Time series ; U.S.A</subject><ispartof>Regional science and urban economics, 1992-09, Vol.22 (3), p.333-346</ispartof><rights>1992</rights><rights>Copyright Elsevier Sequoia S.A. Sep 1992</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/0166-0462(92)90033-W$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,778,782,3539,3996,27858,27913,27914,45984</link.rule.ids><backlink>$$Uhttp://econpapers.repec.org/article/eeeregeco/v_3a22_3ay_3a1992_3ai_3a3_3ap_3a333-346.htm$$DView record in RePEc$$Hfree_for_read</backlink></links><search><creatorcontrib>Nass, Clifford</creatorcontrib><creatorcontrib>Garfinkle, David</creatorcontrib><title>Localized autocorrelation diagnostic statistic (LADS) for spatial models: Conceptualization, utilization, and computation</title><title>Regional science and urban economics</title><description>This paper proposes a regression diagnostic, the localized autocorrelation diagnostic statistic (LADS), that differs from traditional autocorrelation diagnostics in that: (1) it is concerned with localized occurrences of spatial autocorrelation; (2) each localized occurrence is assumed to result from a potentially distinct problem in model specification; and (3) problems are deduced post hoc from the particular geographic units implicated by the statistic. LADS (
N ×
N,
E,
C) is the probability that in a regression model with
N spatial units, a contiguous block of
C or more residuals among the
E most extreme, same-signed residuals occurred by chance. LADS can help identify omitted independent variables, distinct regimes, and error heteroskedasticity. Two algorithms for the computation of LADS and various LADS(
N,
E,
C)s for state-level models of the United States are provided.</description><subject>Algorithms</subject><subject>Economic models</subject><subject>Economic Theory</subject><subject>Regression analysis</subject><subject>Spatial models</subject><subject>Statistics</subject><subject>Time series</subject><subject>U.S.A</subject><issn>0166-0462</issn><issn>1879-2308</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1992</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><sourceid>K30</sourceid><sourceid>7UB</sourceid><recordid>eNqFkk-LFDEQxYMoOK5-Aw9BQXZhWyuV2D3xICzj-o8BDyp7DOl0zZqlu9Mm6YXx05ueEQVBPLxU5fHjQVLF2GMBzwWI-kVRXYGq8VTjmQaQsrq6w1Zi3egKJazvstVv5D57kNINQDFQrth-G5zt_Q_quJ1zcCFG6m32YeSdt9djSNk7nnKxDt3p9uLN5zO-C5GnqZi250PoqE-v-CaMjqY8L3GHhHM-Z__nYseOuzBMcz4YD9m9ne0TPfpVT9jXt5dfNu-r7ad3HzYX24rUS5UrtGvnWsSmJek07LAWSiitCKBVLVggEA3aWreyRYHtWkmJAJ2Sxep2KE_Ys2PuFMP3mVI2g0-O-t6OFOZkatDQCP1_UDZKgGpEAZ_8Bd6EOY7lEQaX71YIS9rTf0ECNQpVqKZQH49UpImcmaIfbNwbIop0TS6YWyMtYjn2RULrpfVFsmhaqiydqs23PJSw18ewMg669RRNcp7KUDofyWXTBW8EmGVlzLIPZtkHUyIPK2Ou5E90A7Pb</recordid><startdate>19920901</startdate><enddate>19920901</enddate><creator>Nass, Clifford</creator><creator>Garfinkle, David</creator><general>Elsevier B.V</general><general>Elsevier</general><general>North-Holland</general><general>Elsevier Sequoia S.A</general><scope>DKI</scope><scope>X2L</scope><scope>JQCIK</scope><scope>K30</scope><scope>PAAUG</scope><scope>PAWHS</scope><scope>PAWZZ</scope><scope>PAXOH</scope><scope>PBHAV</scope><scope>PBQSW</scope><scope>PBYQZ</scope><scope>PCIWU</scope><scope>PCMID</scope><scope>PCZJX</scope><scope>PDGRG</scope><scope>PDWWI</scope><scope>PETMR</scope><scope>PFVGT</scope><scope>PGXDX</scope><scope>PIHIL</scope><scope>PISVA</scope><scope>PJCTQ</scope><scope>PJTMS</scope><scope>PLCHJ</scope><scope>PMHAD</scope><scope>PNQDJ</scope><scope>POUND</scope><scope>PPLAD</scope><scope>PQAPC</scope><scope>PQCAN</scope><scope>PQCMW</scope><scope>PQEME</scope><scope>PQHKH</scope><scope>PQMID</scope><scope>PQNCT</scope><scope>PQNET</scope><scope>PQSCT</scope><scope>PQSET</scope><scope>PSVJG</scope><scope>PVMQY</scope><scope>PZGFC</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope><scope>7UB</scope></search><sort><creationdate>19920901</creationdate><title>Localized autocorrelation diagnostic statistic (LADS) for spatial models: Conceptualization, utilization, and computation</title><author>Nass, Clifford ; 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(2) each localized occurrence is assumed to result from a potentially distinct problem in model specification; and (3) problems are deduced post hoc from the particular geographic units implicated by the statistic. LADS (
N ×
N,
E,
C) is the probability that in a regression model with
N spatial units, a contiguous block of
C or more residuals among the
E most extreme, same-signed residuals occurred by chance. LADS can help identify omitted independent variables, distinct regimes, and error heteroskedasticity. Two algorithms for the computation of LADS and various LADS(
N,
E,
C)s for state-level models of the United States are provided.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/0166-0462(92)90033-W</doi><tpages>14</tpages></addata></record> |
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| ispartof | Regional science and urban economics, 1992-09, Vol.22 (3), p.333-346 |
| issn | 0166-0462 1879-2308 |
| language | eng |
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| source | RePEc; Worldwide Political Science Abstracts; Periodicals Index Online; ScienceDirect Journals (5 years ago - present) |
| subjects | Algorithms Economic models Economic Theory Regression analysis Spatial models Statistics Time series U.S.A |
| title | Localized autocorrelation diagnostic statistic (LADS) for spatial models: Conceptualization, utilization, and computation |
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