This paper analyzes the effects of macroeconomic shocks on prices and output at different levels of aggregation using a bottom up approach. We show how to generate firm level impulse responses by incorporating experimental settings into surveys and by exposing firm executives to treatment...

This paper studies the effects of a change in the Swiss franc/euro exchange rate floor, as introduced by the Swiss National Bank in September 2011 using a survey based impulse responses analysis. Survey based impulse responses incorporate experimental settings into representative firm surveys,...

In this work we present a new method for Quickest Change Detection (QCD) and Quickest Hub Discovery (QHD) in correlation structures that geometrically represents solutions from asymptotic Random Matrix theory (RMT) consistently, and allows to incorporate new distance metrics for both tests. We...

Bivariate time series data often show strong relationships between the two components, while both individual variables can be approximated by random walks in the short run and are obviously bounded in the long run. Three model classes are considered for a time-series model selection problem:...

The occurrence of decision problems with changing roles of null and alternative hypotheses has increased interest in extending the classical hypothesis testing setup. Particularly, confirmation analysis has been in the focus of some recent contributions in econometrics. We emphasize that...

This paper picks up on a model developed by Philipov and Glickman (2006) for modeling multivariate stochastic volatility via Wishart processes. MCMC simulation from the posterior distribution is employed to fit the model. However, erroneous mathematical transformations in the full conditionals...

The present value model of the current account has been very popular, as it provides an optimal benchmark to which actual current account series have often been compared. We show why persistence in observed current account data makes the estimated optimal series very sensitive to small-sample...

This paper considers Bayesian regression with normal and doubleexponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range...

Empirical evidence suggests a sharp volatility decline of the growth in U.S. gross domestic product (GDP) in the mid-1980s. Using Bayesian methods, we analyze whether a volatility reduction can also be detected for the German GDP. Since statistical inference for volatility processes critically...

The present value model of the current account has been very popular, as it provides an optimal benchmark to which actual current account series have often been compared. We show why persistence in observed current account data makes the estimated optimal series very sensitive to small-sample...