Identifying inter-seasonal drought characteristics using binary outcome panel data models
This study mainly focuses on spatiotemporal and inter-seasonal meteorological drought characteristics. Random Effect Logistic Regression Model (RELRM) and Conditional Fixed Effect Logistic Regression Model (CFELRM) are used to identify the spatiotemporal and inter-seasonal characteristics of meteoro...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Geocarto International |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/10106049.2023.2178527 |
| _version_ | 1797681617137827840 |
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| author | Rizwan Niaz Anwar Hussain Mohammed M. A. Almazah Ijaz Hussain Zulfiqar Ali A. Y. Al-Rezami |
| author_facet | Rizwan Niaz Anwar Hussain Mohammed M. A. Almazah Ijaz Hussain Zulfiqar Ali A. Y. Al-Rezami |
| author_sort | Rizwan Niaz |
| collection | DOAJ |
| description | This study mainly focuses on spatiotemporal and inter-seasonal meteorological drought characteristics. Random Effect Logistic Regression Model (RELRM) and Conditional Fixed Effect Logistic Regression Model (CFELRM) are used to identify the spatiotemporal and inter-seasonal characteristics of meteorological drought in selected stations. The log-likelihood Ratio Chi-Square (LRCST) and Wald chi-square tests (WCTs) are used to assess the significance of RELRM and CFELRM. The Hausman test (HT) is applied to select the appropriate model between RELRM and CFELRM. For instance, HT suggests the CFELRM as an appropriate model in spring-to-summer spatiotemporal drought modelling. The significant coefficient from CFELRM indicates that an increment in moisture conditions of the spring season will decrease the probability of drought in the summer. The odds ratio of 0.1942 means that 19.42% chance of being in a higher category. Similarly, in summer-to-autumn using RELRM the computed odds ratio of 0.0673 shows that 6.73% chance of being in a higher category. |
| first_indexed | 2024-03-11T23:47:29Z |
| format | Article |
| id | doaj.art-e611f4748a60448c8d2489e92c0bdcee |
| institution | Directory Open Access Journal |
| issn | 1010-6049 1752-0762 |
| language | English |
| last_indexed | 2024-03-11T23:47:29Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj.art-e611f4748a60448c8d2489e92c0bdcee2023-09-19T09:13:17ZengTaylor & Francis GroupGeocarto International1010-60491752-07622023-12-0138110.1080/10106049.2023.21785272178527Identifying inter-seasonal drought characteristics using binary outcome panel data modelsRizwan Niaz0Anwar Hussain1Mohammed M. A. Almazah2Ijaz Hussain3Zulfiqar Ali4A. Y. Al-Rezami5Department of Statistics, Quaid-I-Azam UniversityDepartment of Statistics, Quaid-I-Azam UniversityDepartment of Mathematics, College of Sciences and Arts (Muhyil), King Khalid UniversityDepartment of Statistics, Quaid-I-Azam UniversityCollege of Statistical Sciences, University of the PunjabMathematics Department, Prince Sattam Bin Abdulaziz UniversityThis study mainly focuses on spatiotemporal and inter-seasonal meteorological drought characteristics. Random Effect Logistic Regression Model (RELRM) and Conditional Fixed Effect Logistic Regression Model (CFELRM) are used to identify the spatiotemporal and inter-seasonal characteristics of meteorological drought in selected stations. The log-likelihood Ratio Chi-Square (LRCST) and Wald chi-square tests (WCTs) are used to assess the significance of RELRM and CFELRM. The Hausman test (HT) is applied to select the appropriate model between RELRM and CFELRM. For instance, HT suggests the CFELRM as an appropriate model in spring-to-summer spatiotemporal drought modelling. The significant coefficient from CFELRM indicates that an increment in moisture conditions of the spring season will decrease the probability of drought in the summer. The odds ratio of 0.1942 means that 19.42% chance of being in a higher category. Similarly, in summer-to-autumn using RELRM the computed odds ratio of 0.0673 shows that 6.73% chance of being in a higher category.http://dx.doi.org/10.1080/10106049.2023.2178527standardized drought indicesmeteorological droughtdrought persistenceconditional fixed effect logistic regression modelrandom effect logistics model |
| spellingShingle | Rizwan Niaz Anwar Hussain Mohammed M. A. Almazah Ijaz Hussain Zulfiqar Ali A. Y. Al-Rezami Identifying inter-seasonal drought characteristics using binary outcome panel data models Geocarto International standardized drought indices meteorological drought drought persistence conditional fixed effect logistic regression model random effect logistics model |
| title | Identifying inter-seasonal drought characteristics using binary outcome panel data models |
| title_full | Identifying inter-seasonal drought characteristics using binary outcome panel data models |
| title_fullStr | Identifying inter-seasonal drought characteristics using binary outcome panel data models |
| title_full_unstemmed | Identifying inter-seasonal drought characteristics using binary outcome panel data models |
| title_short | Identifying inter-seasonal drought characteristics using binary outcome panel data models |
| title_sort | identifying inter seasonal drought characteristics using binary outcome panel data models |
| topic | standardized drought indices meteorological drought drought persistence conditional fixed effect logistic regression model random effect logistics model |
| url | http://dx.doi.org/10.1080/10106049.2023.2178527 |
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