In the first chapter of this thesis, I estimate Markov-switching models with time-varying
transition probabilities to predict the US business cycle regimes. In particular, I evaluate
the predictive power of real and financial indicators and find that the slope of the yield curve
turns out to be the most reliable indicator for regime predictions. This first chapter paves
the way for the next two chapters of this thesis that also use models with Markov-switching
for analysing the business cycle.
The second chapter (a joint work with Massimiliano Marcellino) combines the Markovswitching
model with the MIxed DAta Sampling (MIDAS) model. This new model uses
information from variables sampled at different frequencies. We first show in a Monte-Carlo
experiment that our estimation method yields accurate estimates. We then apply this new
model to the prediction of both the business cycle regimes and GDP growth for the US
and the UK. We find that the use of high frequency information and parameter switching
performs better than using each of these two features separately.
In the third chapter (a joint work with Laurent Maurin and Matthias Mohr), we estimate
nine different models of the output gap (univariate, multivariate, linear and non-linear) and
compute model-averaged estimates of the output gap. We find some evidence for changes in
the slope of the trend of the Euro area output for few periods in 1974 and 2009. Moreover,
our model-averages measures of the output gap reduce the uncertainty associated with the
output gap estimates and soften the impact of data revisions. We then evaluate the forecasting
performance of our output gap estimates for inflation and find that the output gap
estimates improve on the forecasting performance of standard AR benchmarks for inflation
although the inflation forecasts based on the output gap estimates exhibit a poor forecasting
performance since 2008.
The last chapter of this thesis (a joint work with Eric Ghysels and Massimiliano Marcellino)
is an empirical evaluation of the risk-return relation. We use a MIDAS estimator
of the conditional variance and model regime changes in the parameter entering before the
conditional variance. We find evidence for a reversed risk-return relation in periods of high
volatility, while we uncover the traditional positive risk-return relation in periods of low
volatility. In particular, the high volatility regime is interpreted as a 
flight-to-quality regime.
This finding is robust to a large range of specifications.