This dissertation develops and applies empirical methods to find policy-relevant answers regarding financial stability and network effects, predicting food insecurity risks, and understanding the financial relevance of natural capital. Chapter 2 proposes a dynamic network effect (DNE) model to study network effects, which refer to entities affecting their neighbors due to the proximity to each other. The smooth marginalized particle filter (SMPF) is shown to be a well-suited estimator in Monte Carlo simulations. Chapter 3 applies the DNE model to explain contagion among the largest Eurozone banks. Supervisory asset holding data allow the construction of a bank business model similarity network. The associated time-varying network effects help resolve the credit spread puzzle, especially during turbulent times. Chapter 4 proposes a stochastic framework to forecast food insecurity risks using LASSO variable selection, a panel vector-autoregression and Bayesian priors to incorporate expert opinions. The model is stochastic and can inform vulnerability and risk assessments. Chapter 5 asks how 1% growth in natural capital affects a country’s government bond yields. Comparisons across countries lead to problematic insights, due to the ingrained income bias. Instead, within-country comparisons over the recent past, estimated using interactive fixed-effects, are unaffected by the bias and show that renewable natural capital tend to lower borrowing costs.
|Award date||3 May 2021|
|Publication status||Published - 3 May 2021|