Can we use sesonally adjusted indicators in dynamic factor models?
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(2) CAN WE USE SEASONALLY ADJUSTED INDICATORS IN DYNAMIC FACTOR MODELS?.
(3) CAN WE USE SEASONALLY ADJUSTED INDICATORS IN DYNAMIC FACTOR MODELS? (*). Maximo Camacho (**) UNIVERSIDAD DE MURCIA. Yuliya Lovcha UNIVERSIDAD DE NAVARRA. Gabriel Perez-Quiros BANCO DE ESPAÑA AND CEPR. (*) Maximo Camacho thanks CICYT for its support through grant ECO2010-19830. The views in this paper are those of the authors and do not represent the views of the Bank of Spain or the Eurosystem. (**) Corresponding author: Universidad de Murcia, Facultad de Economia y Empresa, Departamento de Métodos Cuantitativos para la Economía y la Empresa, 30100, Murcia, Spain. E-mail: [email protected].. Documentos de Trabajo. N.º 1235 2012.
(4) The Working Paper Series seeks to disseminate original research in economics and finance. All papers have been anonymously refereed. By publishing these papers, the Banco de España aims to contribute to economic analysis and, in particular, to knowledge of the Spanish economy and its international environment. The opinions and analyses in the Working Paper Series are the responsibility of the authors and, therefore, do not necessarily coincide with those of the Banco de España or the Eurosystem.. The Banco de España disseminates its main reports and most of its publications via the INTERNET at the following website: http://www.bde.es.. Reproduction for educational and non-commercial purposes is permitted provided that the source is acknowledged. © BANCO DE ESPAÑA, Madrid, 2012 ISSN: 1579-8666 (on line).
(5) Abstract. We examine the short-term performance of two alternative approaches to forecasting using dynamic factor models. The first approach extracts the seasonal component of the individual indicators before estimating the dynamic factor model, while the alternative uses the nonseasonally adjusted data in a model that endogenously accounts for seasonal adjustment. Our Monte Carlo analysis reveals that the performance of the former is always comparable to or even better than that of the latter in all the simulated scenarios. Our results have important implications for the factor models literature because they show that the common practice of using seasonally adjusted data in this type of model is very accurate in terms of forecasting ability. Drawing on five coincident indicators, we illustrate this result for US data. Keywords: Dynamic factor models, seasonal adjustment, short-term forecasting. JEL classification: E32, C22, E27..
(6) Resumen. En el trabajo se examina el comportamiento en predicción de dos aproximaciones alternativas a los modelos de factores dinámicos. La primera aproximación extrae el componente estacional de los indicadores individuales antes de estimar el modelo de factores dinámicos. La segunda utiliza series no ajustadas de estacionalidad en un modelo de factores que endógenamente realiza el ajuste estacional. Nuestro análisis de Montecarlo demuestra que el comportamiento de la primera aproximación es siempre igual o mejor que el de la segunda, sea cual sea el escenario simulado. Nuestros resultados tienen implicaciones muy relevantes sobre la literatura de modelos factoriales porque muestran que la práctica común de usar datos ajustados de estacionalidad en estos modelos es la apropiada y genera los mejores resultados en predicción. Usando cinco indicadores, ilustramos este resultado para el caso de Estados Unidos. Palabras clave: Modelo de factores dinámicos, ajuste estacional, predicción de corto plazo. Códigos JEL: E32, C22, E27..
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