Structural vector autoregressive (VAR) models were introduced in 1980 as an alternative to traditional large-scale macroeconometric models when the theoretical and empirical support for these models became increasingly doubtful. Initial applications of the structural VAR methodology often were...

The estimation of dynamic factor models for large sets of variables has attracted considerable attention recently, due to the increased availability of large datasets. In this paper we propose a new parametric methodology for estimating factors from large datasets based on state space models and...

The estimation of structural dynamic factor models (DFMs) for large sets of variables is attracting considerable attention. In this paper we briefly review the underlying theory and then compare the impulse response functions resulting from two alternative estimation methods for the DFM....

This paper explores the effects of the US business cycle on US stock market returns through an analysis of the equity risk premium. We propose a new methodology based on the SDF approach to asset pricing that allows us to uncover the different effects of aggregate demand and supply shocks. We...

Surprisingly there are very few estimates of the equity risk premium period-by-period that satisfy a no-arbitrage condition, despite the vast literature on the subject. This is mainly due to the difficulties of estimation. Using the stochastic discount factor (SDF) model based on observable...

Inflation is a far from homogeneous phenomenon, a fact often neglected in modelling consumer price inflation. This study, the first of its kind for an emerging market country, investigates gains to inflation forecast accuracy by aggregating weighted forecasts of the sub-component price indices,...

This paper analyses the use of factor analysis for instrumental variable estimation when the number of instruments tends to infinity. In particular, we focus on situations where many weak instruments exist and/or the factor structure is weak. Theoretical results, simulation experiments and...

This paper shows consistency of a two step estimator of the parameters of a dynamic approximate factor model when the panel of time series is large (n large). In the first step, the parameters are first estimated from an OLS on principal components. In the second step, the factors are estimated...

An important preliminary step in impulse response analysis is to select the vector autoregressive (VAR) lag order from the data, yet little is known about the implications of alternative lag order selection criteria for the accuracy of the impulse response estimates. In this Paper, we compare...

This paper considers quasi-maximum likelihood estimations of a dynamic approximate factor model when the panel of time series is large. Maximum likelihood is analyzed under different sources of misspecification: omitted serial correlation of the observations and cross-sectional correlation of...