The identification of causal effects of marketing campaigns (advertisements, discounts, promotions, loyalty programs) require the collection of experimental data. Such data sets frequently suffer from limited sample sizes due to constraints (time, budget) which can result in imprecise estimators...

We empirically investigate the economic impact of natural disasters on food prices and production. We address the key issues of data aggregation and counterfactual biases. Our data set consists of regional information on prices and production for fourteen food products in Peru. This granularity...

We introduce conditional score residuals and provide a general framework for the diagnostic analysis of time series models. A key feature of conditional score residuals is that they account for the shape of the conditional distribution. These residuals offer reliable and powerful diagnostic...

We assess the stability of the unemployment gap parameter using linear dynamic Phillips curve models for the United States. In this study, we allow the unemployment gap parameter to be time-varying such that we can monitor the importance of the Phillips curve over time. We considerdifferent...

We develop a panel data model with stochastic dynamic processes to empirically verify the possible existence of the European crime drop. This time-varying effect can be captured by the stochastic trend and can be interpreted as the "potential" European crime drop. Due to the flexibility of our...

In economics and finance, speculative bubbles take the form of locally explosive dynamics that eventually collapse. We propose a test for the presence of speculative bubbles in the context of mixed causal-noncausal autoregressive processes. The test exploits the fact that bubbles are...

This paper introduces a novel simulation-based filtering method for general state space models. It allows for the computation of time-varying conditional means, quantiles, and modes, but also for the prediction of latent variables in general. The method relies on generating artificial samples of...

We analyze the role of industrial and non-industrial production sectors in the US economy by adopting a novel multilevel factor model. The proposed model is suitable for high-dimensional panels of economic time series and allows for interdependence structures across multiple sectors. The...