Magali Marx

Generalizing the Taylor Principle: New Comment

In this paper, we provide determinacy conditions, i.e. conditions ensuring the existence and uniqueness of a bounded solution, in a purely forward-looking linear Markov switching rational expectations model. We thus settle the debate between Davig and Leeper (2007) and Farmer et al. (2010). The conditions derived by the former are valid in a subset of bounded solutions only depending on a finite number of past regimes, that we call Markovian. However, in the complete bounded solution space, the new determinacy conditions we derive are tighter. Nevertheless, when unique, the solution coincides with the Markovian solution of Davig and Leeper (2007). We finally illustrate our results in the standard new-Keynesian model studied by Davig and Leeper (2007) and Farmer et al. (2010).

Trends and Cycles: An Historical Review of the Euro Area

We analyze the euro area business cycle in a medium scale DSGE model where we assume two stochastic trends: one on total factor productivity and one on the inflation target of the central bank. To justify our choice of integrated trends, we test alternative specifications for both of them. We do so, estimating trends together with the models structural parameters, to prevent estimation biases. In our estimates, business cycle fluctuations are dominated by investment specific shocks and preference shocks of households. Our results cast doubts on the view that cost push shocks dominate economic fluctuations in DSGE models and show that productivity shocks drive fluctuations on a longer term. As a conclusion, we present our estimations historical reading of the business cycle in the euro area. This estimation gives credible explanations of major economic events since 1985.

The Prisme Model: Can Disaggregation on the Production Side Help to Forecast GDP?

Although a forecasting model has very good statistical properties and the mean of the residuals equals zero, it can produce systematic errors during a short period. In the case of regular publications, forecasters want to prevent such a persistence of errors over several periods. For this reason, a safeguard model can be used to inform the forecaster when there is a risk that the standard model (i.e. the best specified model on average) leads to persistent errors over several months or quarters. This paper explains why and how such a safeguard model has been built in order to improve the forecasts of French GDP at the current quarter horizon (nowcasts), which are officially published by the French central bank. The official benchmark model for GDP nowcasts is an aggregated model that relies exclusively on survey in the manufacturing industry. In the long run, this model still has the best performances. On the contrary, the safeguard model is a disaggregated model which features equations for the valued added of 6 sectors. From this example, we provide general remarks on the advantages of disaggregation as well as how such safeguard models can be used in practice.