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.

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.

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.

Measuring the Sources of Cyclical Fluctuations in the G7 Economies.

We analyze herein the importance of four types of shocks in contributing to the business cycles of the G7 economies. After disentangling the common permanent and transitory shocks in the G7 outputs, we identify the domestic and foreign components of such shocks for each country. This provides us with quite a flexible palette for understanding the degree of openness of the G7 countries, useful information for the analysis of the strengths and weaknesses of each national economy. Our empirical analysis reveals that the cycles of most of the G7 outputs are dominated by their domestic components and that the foreign components are almost entirely due to permanent shocks.International business cycles, Permanent-Transitory decompositions, serial correlation common features, Frequency domain analysis.

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.

Technology shocks, structural breaks and the effects on the business cycle.

This paper contributes to the literature on the role of technology shocks as source of the business cycle in two ways. First, we document that time-series of US productivity and hours are apparently affected by a structural break in the late 60’s, which is likely due to a major change in the monetary policy. Second, we show that the importance of demand shocks over the business cycle has sharply increased after the break.Business cycle, technology shocks, structural breaks.