Non-linear dynamic modelling for panel data in the social sciences
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Non-linearities and dynamic interactions between state variables are characteristic of complex social systems and processes. In this thesis, we present a new methodology to model these non-linearities and interactions from the large panel datasets available for some of these systems. We build macro-level statistical models that can verify theoretical predictions, and use polynomial basis functions so that each term in the model represents a specific mechanism. This bridges the existing gap between macro-level theories supported by statistical models and micro-level mechanistic models supported by behavioural evidence. We apply this methodology to two important problems in the social sciences, the demographic transition and the transition to democracy.
The demographic transition is an important problem for economists and development scientists. Research has shown that economic growth reduces mortality and fertility rates, which reduction in turn results in faster economic growth. We build a non-linear dynamic model and show how this data-driven model extends existing mechanistic models. We also show policy applications for our models, especially in setting development targets for the Millennium Development Goals or the Sustainable Development Goals.
The transition to democracy is an important problem for political scientists and sociologists. Research has shown that economic growth and overall human development transforms socio-cultural values and drives political institutions towards democracy. We model the interactions between the state variables and find that changes in institutional freedoms precedes changes in socio-cultural values. We show applications of our models in studying development traps.
This thesis comprises the comprehensive summary and seven papers. Papers I and II describe two similar but complementary methodologies to build non-linear dynamic models from panel datasets. Papers III and IV deal with the demographic transition and policy applications. Papers V and VI describe the transition to democracy and applications. Paper VII describes an application to sustainable development.
Place, publisher, year, edition, pages
Uppsala: Department of Mathematics , 2015. , 40 p.
Uppsala Dissertations in Mathematics, ISSN 1401-2049 ; 91
Dynamical systems, stochastic models, Bayesian, panel data, social sciences, development
Research subject Mathematics with specialization in Applied Mathematics
IdentifiersURN: urn:nbn:se:uu:diva-261289ISBN: 978-91-506-2481-6OAI: oai:DiVA.org:uu-261289DiVA: diva2:854732
2015-11-06, Polhemsalen, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 10:00 (English)
Johnson, Oliver, Dr.
Sumpter, David JT, Dr.
List of papers