Gonzalo Camba-Mendez

An automatic leading indicator of economic activity: forecasting GDP growth for European countries

In the construction of a leading indicator model of economic activity, economists must select among a pool of variables which lead output growth. Usually the pool of variables is large and a selection of a subset must be carried out. This paper proposes an automatic leading indicator model which, rather than preselection, uses a dynamic factor model to summarize the information content of a pool of variables. Results using quarterly data for France, Germany, Italy and the United Kingdom show that the overall forecasting performance of the automatic leading indicator model appears better than that of more traditional VAR and BVAR models.

Tests of Rank in Reduced Rank Regression Models

There has recently been renewed research interest in the development of tests of the rank of a matrix. This article evaluates the performance of some asymptotic tests of rank determination in reduced rank regression models together with bootstrapped versions through simulation experiments. The bootstrapped procedures significantly improve on the performance of the corresponding asymptotic tests. The article also presents a Monte Carlo exercise comparing the forecasting performance of reduced rank and unrestricted vector autoregressive (VAR) models in which the former appear superior. The tests of rank considered here are then applied to construct reduced rank VAR models for leading indicators of U.K. economic activity. These more parsimonious multivariate representations display an improvement in forecasting performance over that of unrestricted VAR models.

Testing the rank of the Hankel covariance matrix: a statistical approach

The rank of the Hankel matrix, corresponding to a system transfer function, is equal to the order of its minimal state space realization. The computation of the rank of the Hankel matrix is complicated by the fact that its block elements are rarely given exactly but are estimated instead. We propose statistical tests to determine the rank of the Hankel matrix. We also provide a Monte Carlo study on the reliability of these tests compared to existing procedures.

Bootstrap statistical tests of rank determination for system identification

Identification of multivariate state-space models involves the specification of the dimension of the state vector. This note evaluates the performance of some asymptotic statistical tests of rank determination together with their bootstrapped versions against information criteria (IC) methods. Results show that certain bootstrapped procedures improve upon the performance of the corresponding asymptotic tests, and statistical tests have in general a better performance than IC methods.

Filtered least squares and measurement error

Li, Maddala, and Rush (1995) proposed a low-pass spectral filter method to estimate cointegrating vectors in small samples. This paper tests the effectiveness of the approach in the presence of measurement error. Two other methods, valid under the assumption of stationarity, are also tested.