A Reduced Rank Regression Approach to Coincident and Leading Indexes Building.

This paper proposes a reduced rank regression framework for constructing coincident and leading indexes. Based on a formal definition that requires that the first differences of the leading index are the best linear predictor of the first differences of the coincident index, it is shown that the notion of polynomial serial correlation common features can be used to build these composite variables. Concepts and methods are illustrated by an empirical investigation of the US business cycle indicators.Coincident and Leading Indexes, Polynomial Serial Correlation Common Feature, Reduced Rank Regression.

Complex Reduced Rank Models for Seasonally Cointegrated Time Series

This paper introduces a new representation for seasonally cointegrated variables, namely the complex error correction model, which allows statistical inference to be performed by reduced rank regression. The suggested estimators and tests statistics are asymptotically equivalent to their maximum likelihood counterparts. Tables are provided for both asymptotic and finite sample critical values, and an empirical example is presented to illustrate the concepts and methods.

Common Shocks, Common Dynamics, and the International Business Cycle

This paper develops an econometric framework to understand whether co-movements observed in the international business cycle are the consequences of common shocks or common transmission mechanisms. Then we propose a new statistical measure of the importance of domestic and foreign shocks over the national business cycle. We show how to decompose the business cycle effects of permanent-transitory shocks into those due to their domestic and foreign components. We apply our analysis to G7 outputs.Common Cycles, Cointegration, Domestic-Foreign Shocks, International Business Cycles, Permanent-Transitory Decomposition.

Small Sample Improvements in the Statistical Analysis of Seasonally Cointegrated Systems

This paper proposes new iterative reduced-rank regression procedures for seasonal cointegration analysis. The suggested methods are motivated by the idea that modelling the cointegration restrictions jointly at different frequencies may increase efficiency in finite samples. Monte Carlo simulations indicate that the new tests and estimators perform well with respect to already existing statistical procedures.Seasonal Cointegration, Reduced Rank Regression.

Macro-panels and Reality

This note argues that large VAR models with common cyclical feature restrictions provide an attractive framework for parsimonious implied univariate final equations, justifying on the one hand the estimation of homogenous panels with dynamic heterogeneity and a common factor structure, and on the other hand the aggregation of time series. However, starting with a too restrictive DGP might preclude from looking at interesting empirical issues.Economics (Jel: A)

Macroeconomic forecasting and structural analysis through regularized reduced-rank regression

This paper proposes a strategy for detecting and imposing reduced-rank restrictions in medium vector autoregressive models. It is known that Canonical Correlation Analysis (CCA) does not perform well in this framework, because inversions of large covariance matrices are required. We propose a method that combines the richness of reduced-rank regression with the simplicity of naïve univariate forecasting methods. In particular, we suggest the usage of a proper shrinkage estimator of the autocovariance matrices that are involved in the computation of CCA, in order to obtain a method that is asymptotically equivalent to CCA, but numerically more stable in finite samples. Simulations and empirical applications document the merits of the proposed approach for both forecasting and structural analysis