Probit models are employed to evaluate leading indicators for Germany's recessions. The predictive power of leading indicators is found to be lower than assumed in previous studies. Although, monetary variables provide the best predictive power for recessions, survey data and order inflows show...

This paper uses bilateral data on 420 merchandise trade flows between 21 industrial countries are used to estimate standard trade equations. The data set of over 11,000 observations allows the underlying elasticities to be estimated with considerable precision. Remarkably, a single specification...

The paper discusses methodological topics of bankruptcy prediction modelling—unbalanced sampling, sample bias, and unbiased predictions of bankruptcy. Bankruptcy models are typically estimated with the use of non-random samples, which creates sample choice biases. We consider two types of...

We introduce a long memory autoregressive conditional Poisson (LMACP) model to model highly persistent time series of counts. The model is applied to forecast quoted bid-ask spreads, a key parameter in stock trading operations. It is shown that the LMACP nicely captures salient features of...

I propose a method for estimating treatment effect heterogeneity in event studies with staggered adoption. This method serves as an alternative to two-way fixed effects regressions, which have been shown to be near-term biased in these settings. I build on insights from the machine learning (ML)...

This paper uses bilateral data on 420 merchandise trade flows between 21 industrial countries are used to estimate standard trade equations. The data set of over 11,000 observations allows the underlying elasticities to be estimated with considerable precision. Remarkably, a single specification...

Public programs often use statistical profiling to assess the risk that applicants will become long-term dependent on the program. The literature uses linear probability models and (Cox) proportional hazard models to predict duration outcomes. These either focus on one threshold duration or...

Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...