This study examines the dynamics of economic freedom in nine advanced democracies in comparison to China over the 1970-2022 period. Using data from the Fraser Institute and the Manifesto Project Database, we apply a Bayesian time-series methodology to identify three key patterns. First, economic...

This document introduces the R library estimateW to estimate spatial weight matrices for Bayesian spatial econometric panel models. The approach focuses on spatial weights that are binary prior to row-standardization. However, unlike recent literature our approach requires no strong a priori...

Banks often assume that higher credit limits increase customer default risk because greater exposure appears to imply greater vulnerability. This reasoning, however, conflates correlation with causation. Whether increasing a customer's credit limit truly raises the likelihood of default remains...

We assess asymmetries, nonlinearities and state dependencies in dynamic responses of the euro area to monetary policy shocks. The dataset includes macroeconomic, financial, and survey-based variables measuring credit conditions and bank lending transmission channels. These data are observed at...

The output gap is a key guide for economic policymaking and analysis. It is often used to quantify inflationary pressures and thus plays a central role in Danmarks Nationalbank's assessment of the state of the Danish economy. This paper refines Danmarks Nationalbank's existing production...

It is well known that standard frequentist inference breaks down in IV regressions with weak instruments. Bayesian inference with diffuse priors suffers from the same problem. We show that the issue arises because flat priors on the first-stage coefficients overstate instrument strength. In...

We introduce a novel methodology, "parametric tilting," for incorporating external information into econometric model-based density forecasts. Unlike traditional entropic tilting, which can generate unrealistic or unstable distributions under certain conditions, parametric tilting ensures more...

This work presents a practical approach to improve risk quantification for heavy-tailed insurance claims through model averaging and grid map visualization, addressing the drawbacks of traditional single "best" model selection commonly used in actuarial and model-fitting literature. This is a...

This paper estimates Morocco's natural interest rate (NIR) using two approaches: a standard HLW-type framework and an augmented specification that incorporates external factors, namely imported inflation, and movements in the real effective exchange rate. The results point to a downward trend in...

Advice plays a central role in health, personal finance, and energy-efficiency decisions. We study how a benevolent expert should design verifiable advice - such as whether to commission a diagnostic test of different accuracy - when the agent is behaviorally biased, either neglecting...

The output gap is a key guide for economic policymaking and analysis. It is often used to quantify inflationary pressures and thus plays a central role in Danmarks Nationalbank's assessment of the state of the Danish economy. This paper refines Danmarks Nationalbank's existing production...

We study whether firm-level heterogeneity helps explain U.S. macroeconomic fluctuations in response to aggregate shocks. Using quarterly Compustat and CRSP data from 1986 to 2025, we construct two revenue-based statistics inspired by the Melitz (2003) model: the average firm and the marginal...

In the insurance literature, accurately predicting extreme losses has been a persistent and important problem. Recently, under the modelling framework of weighted distributions, several finite-mixture size-biased distributions, including size-biased Weibull and size-biased truncated log-normal...

Conventional VAR-based volatility spillover measures rely on homoskedasticity and single-Gaussian assumptions, limiting their ability to capture structural breaks and heterogeneous shocks during crises. This study develops a flexible framework to analyze volatility transmission in U.S. commodity...

Selecting the relevant covariates in high-dimensional panel data remains a central challenge in applied econometrics. Conventional fixed effects and random effects models are not designed for systematic variable selection under model uncertainty. In addition, many existing models such as LASSO...

This paper investigates the macroeconomic and bank-specific determinants of nonperforming loans in Romania and selected Central and Eastern European countries. Using a combination of econometric approaches, the study employs fixed-effects panel regressions for 18 Romanian banks over the period...

Accurate supplier lead time prediction is critical for maintaining resilience in Engineer-to-Order (EtO) supply chains, characterized by high customization and uncertainty. This study develops a simulation-based predictive framework combining log-normal sensitivity analysis, Internet of Things...

This paper analyses the impact of various macroeconomic shocks - including aggregate demand, supply, monetary policy, real exchange rate, and budget deficit deviation shocks - on Romania's economy using a VAR model with Bayesian inference. The study captures the period following the adoption of...

Several process parameters affect product reliability. Traditional reliability improvement methods primarily focus on maximizing product lifetime, often overlooking the variation in product lifetime. Manufacturers, however, aim to produce products with minimal variations in their performance....

Purpose This study aims to elucidate the dynamics of monetary and fiscal policy interactions in Brazil, focusing on the impacts of positive shocks in government consumption and interest rates. By comparing rational and behavioral agent responses, it clarifies how these frameworks influence gross...

Contrary to claims in studies on financial economics, a sparse database often obscures the identification of parameters in macroeconomic models. These identification problems originate from the poorly defined mapping between a structural model and reduced-form parameters. Hence, researchers rely...

This study investigates the application of Factor-Augmented Vector Autoregression (FAVAR) and Bayesian Vector Autoregression (BVAR) models for inflation forecasting. FAVAR models deal with high-dimensional data by extracting latent factors from extensive macroeconomic indicators, while BVAR...

We study optimal auctions in a symmetric private values setting, where bidders have signaling concerns: they care about winning the object and a receivers inference about their type. Signaling concerns arise in various economic situations such as takeover bidding, charity auctions, procurement...

I use Bayesian VARs with stochastic volatility to forecast global temperatures and sea level and ice cover in the Northerin emisphere until 2010, by exploiting (i) their long-run equilibrium relationship with climate change drivers (CCDs) and (ii) the relationship between world GDP and...

We assess how changes in the scientific consensus around equilibrium climate sensitivity (ECS), as captured by the IPCC’s Fifth (AR5) and Sixth (AR6) Assessment Reports, impact policymakers’ willingness to take climate action. Taking the IPCC’s reports at face value, the ECS estimates in...

Schaeuffele et al. (2024) examined the effect of Transdiagnostic Cognitive Behavioural Psychotherapy (TD-CBT) on emotional disorders through a meta-analysis of 53 studies involving 6,705 participants. Their main findings indicated that TD-CBT has larger treatment effects on depression, g = 0.74,...