Essays in high dimensional factor models

• Autores: Liang Chen
• Directores de la Tesis: Juan José Dolado (dir. tes.), Jesús Gonzalo Muñoz (dir. tes.)
• Lectura: En la Universidad Carlos III de Madrid ( España ) en 2013
• Idioma: inglés
• Tribunal Calificador de la Tesis: Gabriel Pérez-Quirós (presid.), Abderrahim Taamouti (secret.), Josep Lluís Carrion Silvestre (voc.)
• Materias:
  • Ciencias económicas
    • Econometría
      • Modelos econométricos
• Enlaces
  • Tesis en acceso abierto en: e-Archivo
• Resumen

  • My PhD thesis consists of three chapters on high dimensional factor models and their applications. In Chapter 1, I study how to test for structural breaks in large factor models. Time invariance of factor loadings is a standard assumption in the analysis of large factor models. Yet, this assumption may be restrictive unless parameter shifts are mild. In this chapter we develop a new testing procedure to detect big breaks in these loadings at either known or unknown dates. The test fares well in terms of power relative to other recently proposed tests on this issue, and can be easily implemented to avoid forecasting failures in standard factor-augmented models where the number of factors is a priori imposed on the basis of theoretical considerations. Despite their growing popularity, factor models have been often criticized for lack of identification of the factors. In Chapter 2, I try to identify the orthogonal factors estimated using principal component by associating them to a relevant subset of observed variables. I first propose a selection procedure to choose such a subset, and then test the hypothesis that true factors are exact linear combinations of the selected variables. The good performance of my method in finite samples and its advantages relative to the other available procedures are confirmed through simulations. Empirical applications include the identification of the underlying risk factors in large dataset of stock and portfolio returns, as well as interpreting the factors in a large panel of macroeconomic time series. In both cases, it is shown that the underlying factors can be closely approximated by a few observed variables. In Chapter 3 I investigate the source of the aggregate volatility in industrial productions (IP) using factor models. I consider 3 structural dynamic macro models with multiple producing sectors. General conditions are given to show how the sectoral IP growth rates can be represented as a dynamic factor model (DFM) through input-output linkages. Using available data, we first investigate whether the input-output linkages in these models are strong enough to generate a DFM representation for the sectoral IP growth rates. We also find that after the great moderation in 1984, the sectoral IP growth rates can be characterized by an approximate factor model with only 1 common factor, which is found to be connected primarily to a aggregate technology shock that affects most of the sectors, and possibly to 1 or 2 sectoral shocks that only affect the key sectors that provide inputs for many other sectors.