Previous studies about the relationship between the cyclical components of Mexico's output and unemployment suggest that it closely resembles that found in the economy of the United States of America. This would indicate that the dynamics between output and labour markets in the two economies are rather similar. However, these estimates are puzzling for they do not correspond to a characterization made to Mexico's labour market. Using a methodology first proposed by Clark (1989), we find that the correlation between the transitory components of output and unemployment is much lower than previously thought.

Se considera el problema de estimar conjuntamente las tendencias y los ciclos no observables de una serie de tiempo bivariada, en el contexto macroeconómico de la Ley de Okun, para relacionar las brechas del producto y la tasa de desempleo. La estimación se realiza con un filtro de Hodrick-Prescott bivariado que estima simultáneamente la correlación entre los ciclos. La contribución principal de este enfoque es que se puede controlar la suavidad de las tendencias al fijar un parámetro de suavizamiento en forma apropiada. La ilustración empírica utiliza datos del PIB y de la tasa de desempleo de Estados Unidos.

We examine the problem of combining Mexican inflation predictions or projections provided by a biweekly survey of professional forecasters. Consumer price inflation in Mexico is measured twice a month. We consider several combining methods and advocate the use of dimension reduction techniques whose performance is compared with different benchmark methods, including the simplest average prediction. Missing values in the database are imputed by two different databased methods. The results obtained are basically robust to the choice of the imputation method. A preliminary analysis of the data was based on its panel data structure and showed the potential usefulness of using dimension reduction techniques to combine the experts' predictions. The main findings are: the first monthly predictions are best combined by way of the first principal component of the predictions available; the best second monthly prediction is obtained by calculating the median prediction and is more accurate than the first one. KEYWORDS