Tapsoba, Augustin (2022) Conflict prediction using kernel density estimation. TSE Working Paper, n. 22-1295, Toulouse.

[thumbnail of wp_tse_1295.pdf]
Download (18MB) | Preview


Being able to assess conflict risk at local level is crucial for preventing political violence or mitigating its consequences. This paper develops a new approach for predicting the timing and location of conflict events from violence history data. It adapts the methodology developed in Tapsoba (2018) for measuring violence risk across space and time to conflict prediction. Violence is modeled as a stochastic process with an unknown underlying distribution. Each conflict event observed on the ground is interpreted as a random realization of this process and its underlying distribution is estimated using kernel density estimation methods in a three-dimensional space. The optimal smoothing parameters are estimated to maximize the likelihood of future conflict events. An illustration of the practical gains (in terms of out-of-sample forecasting performance) of this new methodology compared to standard space-time autoregressive models is shown using data from Côte d’Ivoire.

Item Type: Monograph (Working Paper)
Language: English
Date: January 2022
Place of Publication: Toulouse.
Uncontrolled Keywords: Conflict, Insecurity, Kernel Density Estimation
JEL Classification: C1 - Econometric and Statistical Methods - General
O12 - Microeconomic Analyses of Economic Development
O13 - Agriculture; Natural Resources; Energy; Environment; Other Primary Products
Divisions: TSE-R (Toulouse)
Institution: Université Toulouse 1 Capitole.
Site: UT1
Date Deposited: 27 Jan 2022 08:24
Last Modified: 14 Feb 2022 14:16
OAI Identifier: oai:tse-fr.eu:126538
URI: https://publications.ut-capitole.fr/id/eprint/44275
View Item


Downloads per month over past year