Sveriges riksbank, or simply the Riksbank, is the central bank of Sweden. It is the world's oldest central bank and the fourth oldest bank in operation.
Different proxy variables used in fiscal policy SVARs lead to contradicting conclusions regarding the size of fiscal multipliers. We show that the conflicting results are due to violations of the exogeneity assumptions, i.e. the commonly used proxies are endogenously related to the structural shocks. We propose a novel approach to include proxy variables into a Bayesian non-Gaussian SVAR, tailored to accommodate for potentially endogenous proxy variables. Using our model, we show that increasing government spending is a more effective tool to stimulate the economy than reducing taxes.
This paper examines whether a central bank should stabilize CPI or core inflation (CPI excluding agriculture prices) following an adverse weather shock. We analyse this question in a two-sector small open economy calibrated to reflect key characteristics of the Rwandan economy. We first empirically demonstrate that an adverse weather shock in Rwanda leads to higher agriculture prices and reduced agriculture output, consistent with the mechanisms embedded in the macroeconomic model. We then show that a central bank can minimize its losses-measured by a loss function based on CPI inflation-by stabilizing core inflation rather than headline CPI inflation in response to an adverse weather shock. Additionally, we show that CPI inflation is relatively insensitive to changes in labour mobility between the agriculture and nonagriculture sectors, changes in the elasticity of substitution between agriculture and nonagriculture goods, and changes in land maintenance costs.
In the realm of credit risk assessment, the utilisation of artificial intelligence (AI) and machine learning (ML) classification models has become increasingly prevalent. This paper thoroughly investigates latest advancements in AI/ML classification models for credit risk assessment, which are crucial for assessing the creditworthiness of individuals and businesses. Key findings reveal that modern AI/ML techniques, particularly Random Forest and XGBoost, outperform traditional logistic regression methods. Additionally, interpretability techniques, including Shapley Additive exPlanations (SHAP) and feature importance analysis, improve the understanding and transparency of model predictions. This paper synthesises recent research findings and industry developments to provide practitioners and researchers with insights into model selection, evaluation metrics and explanation techniques, thereby contributing to the ongoing evolution of credit risk management strategies in the financial sector.
This paper examines how banks’ incentives to internalize the spillovers from natural disasters affect their credit lending. Using data on small business loans and damage estimates from natural disasters, I find that banks with a large lending share in a local market provide more credit to small firms during the recovery periods than other banks. This finding implies that banks recognize the benefits of alleviating liquidity constraints for distressed borrowers, which lowers their default risk and preserves future business opportunities. Furthermore, I document that disaster-affected local areas with high-lending-share banks experience a smaller employment contraction than other disaster-affected areas, highlighting the importance of bank lending in disaster recovery and resilience.
