The Relevance and Utility of Distress Risk and Corporate Failure Forecasts -- Searching for the Holy Grail: Alternative Statistical Modelling Approaches -- The Rise of the Machines -- An Empirical Application of Modern Machine Learning Methods -- Corporate Failure Models for Private Companies,...

We construct an index for economic policy uncertainty in Denmark using articles in Denmark's largest financial newspaper. We adapt a Latent Dirichlet Allocation (LDA) model to sort articles into topics. We combine article-specific topic weights with the occurrence of words describing uncertainty...

Statistical inference can be described as the process of drawing conclusions about a population or process based on sample data. This chapter outlines the logic of “classical” or “frequentist” methods for such inference. Three commonly used concepts for assessing statistical error are...

Purpose: Few studies have applied count data analysis to tourist accommodation data. This study was undertaken to investigate the characteristics and to seek for the most fitting models for population total estimation in relation to tourist accommodation data.Methods: Based on the data of 10,503...

In this paper, we present new evidence from unobserved components time-series models on the cyclical behavior of the demand for education relative to economic cycles. We investigate the cyclical properties of schooling decisions, the time-varying exposure of these decisions to changes in the...

We propose a new no-change benchmark to evaluate forecasts of series that are temporally aggregated. The new benchmark is the last high-frequency observation and reflects the null hypothesis that the underlying series, rather than the aggregated series, is unpredictable. Under the random walk...

I propose an alternative weighting mechanism for equity mandates based on the statistical significance of the factor loading on the benchmark. Specifically, the weight of each security entering the active portfolio is directly proportional to the t-statistic of the factor loading ($\beta$) with...

I show that ambiguity-averse decision functionals matched with the multiple-prior learning model are more robust to model misspecification than the standard expected utility with Bayesian learning. However, these criteria may fail to deliver robust decisions because the multiple-prior learning...

This paper proposes new estimators for the propensity score that aim to maximize the covariate distribution balance among different treatment groups. Heuristically, our proposed procedure attempts to estimate a propensity score model by making the underlying covariate distribution of different...