for their useful comments and suggestions. We are also grateful to Cédric Tille, editor at the Swiss Journal of Economics and Statistics and two anonymous referees for their careful reading of the paper and their help to improve our paper. Furthermore, we thank the participants at the Swiss National Bank Brown Bag Seminar, at a workshop hosted by the State Secretariat for Economic Affairs in 2013 and at the SSES conference 2012 in Zurich for valuable inputs. The views expressed here are those of the authors and do not necessarily reflect the views of the Swiss National Bank.

Dynamic factor models are becoming increasingly popular in empirical macroeconomics due to their ability to cope with large datasets. Dynamic stochastic general equilibrium (DSGE) models, on the other hand, are suitable for the analysis of policy interventions from a methodical point of view. In this article, we provide a Bayesian method to combine the statistically rich specification of the former with the conceptual advantages of the latter by using information from a DSGE model to form a prior belief about parameters in the dynamic factor model. Because the method establishes a connection between observed data and economic theory and at the same time incorporates information from a large dataset, our setting is useful to study the effects of policy interventions on a large number of observed variables. An application of the method to U.S. data shows that a moderate weight of the DSGE prior is optimal and that the model performs well in terms of forecasting. We then analyze the impact of monetary shocks on both the factors and selected series using a DSGE-based identification of these shocks. Supplementary materials for this article are available online.

New Keynesian models with sticky prices make stark predictions about how the economy responds to shocks under different monetary policy regimes when short‐term interest rates are constrained by an effective lower bound. We use the Swiss case as a laboratory to find evidence in favour of these predictions. We propose a Bayesian VAR to estimate impulse responses to risk shocks for short periods with a binding effective lower bound and with a publicly announced minimum exchange rate. In line with predictions from theory, we find that with a binding effective lower bound, the responses of the exchange rate, prices, and output become more persistent. However, the minimum exchange rate attenuates this adverse impact.

The interaction of macroeconomic variables may change as nominal short-term interest rates approach zero. In this paper, we propose to capture these changing dynamics with a state-switching parameter model which explicitly takes into account that the interest rate might be constrained near the zero lower bound by using a Tobit model. The probability of state transitions is affected by the lagged level of the interest rate. The endogenous specification of the state indicator permits dynamic conditional forecasts of the state and the system variables. We use Bayesian methods to estimate the model and to derive the forecast densities. In an application to Swiss data, we evaluate state-dependent impulse-responses to a risk premium shock identified with sign-restrictions. We provide an estimate of the latent rate, i.e. the rate lower than the constraint on the interest rate level which would be state- and model-consistent. Additionally, we discuss scenario-based forecasts and evaluate the probability of exiting the ZLB region. In terms of log predictive scores and the Bayesian information criterion, the model outperforms a model substituting switching with stochastic volatility and another including intercept switching only combined with stochastic volatility.

We evaluate the forecasting performance of time series models for the production side of gross domestic product (GDP)-that is, for the sectoral real value-added series summing up to aggregate output. We focus on two strategies to model a large number of interdependent time series simultaneously: a Bayesian vector autoregressive model (BVAR) and a factor model structure; and compare them to simple aggregate and disaggregate benchmarks. We evaluate point and density forecasts for aggregate GDP and the cross-sectional distribution of sectoral real value-added growth in the euro area and Switzerland.We find that the factor model structure outperforms the benchmarks in most tests, and in many cases also the BVAR. An analysis of the covariance matrix of the sectoral forecast errors suggests that the superiority can be traced back to the ability to capture sectoral comovement more accurately.