Table of contents:
Estimation of fractionally integrated panel data models with fixed effects and cross-section dependence / Yunus Emre Ergemen, Carlos Velasco Gómez. -- System estimation of panel data models under long-range dependence. -- Parametric portfolio policies with common volatility dynamics / Yunus Emre Ergemen, Abderrahim
Taamouti
Sponsor:
Financial support from the Spanish Plan Nacional de I+D+I (ECO2012-31748), Spanish Ministerio de Ciencia e Innovacion grant ECO2010-19357 and Consolider-2010.
Project:
Gobierno de España. ECO2012-31748
Keywords:
Análisis de series temporales
,
Modelo matemático
,
Estimación de parámetros
Rights:
Atribución-NoComercial-SinDerivadas 3.0 España
Abstract:
This thesis comprises of three chapters that study panel data models with long-range dependence.
The first chapter is a coauthored paper with Prof. Carlos Velasco. We consider large N; T panel data models with fixed effects, common factors allowing cross-sectThis thesis comprises of three chapters that study panel data models with long-range dependence.
The first chapter is a coauthored paper with Prof. Carlos Velasco. We consider large N; T panel data models with fixed effects, common factors allowing cross-section dependence, and
persistent data and shocks, which are assumed fractionally integrated. In a basic setup, the
main interest is on the fractional parameter of the idiosyncratic component, which is estimated
in first differences after factor removal by projection on the cross-section average. The pooled
conditional-sum-of-squares estimate is √NT consistent but the normal asymptotic distribution
might not be centered, requiring the time series dimension to grow faster than the cross-section size
for correction. Generalizing the basic setup to include covariates and heterogeneous parameters,
we propose individual and common-correlation estimates for the slope parameters, while error
memory parameters are estimated from regression residuals. The two parameter estimates are √T consistent and asymptotically normal and mutually uncorrelated, irrespective of possible
cointegration among idiosyncratic components. A study of small-sample performance and an
empirical application to realized volatility persistence are included.
The second chapter extends the first chapter. In this paper, a general dynamic panel data model
is considered that incorporates individual and interactive fixed effects and possibly correlated
innovations. The model accommodates general stationary or nonstationary long-range dependence
through interactive fixed effects and innovations, removing the necessity to perform a priori unitroot
or stationarity testing. Moreover, persistence in innovations and interactive fixed effects
allows for cointegration; innovations can also have vector-autoregressive dynamics; deterministic
trends can be nested. Estimations are performed using conditional-sum-of-squares criteria based
on projected series by which latent characteristics are proxied. Resulting estimates are consistent
and asymptotically normal at parametric rates. A simulation study provides reliability on the
estimation method. The method is then applied to the long-run relationship between debt and
GDP.
The third and final chapter of the thesis is a coauthored paper with Prof. Abderrahim
Taamouti. In this paper, a parametric portfolio policy function is considered that incorporates
common stock volatility dynamics to optimally determine portfolio weights. Reducing dimension
of the traditional portfolio selection problem signifficantly, only a number of policy parameters corresponding
to first- and second-order characteristics are estimated based on a standard methodof-
moments technique. The method, allowing for the calculation of portfolio weight and return statistics, is illustrated with an empirical application to 30 U.S. industries to study the economic
activity before and after the recent financial crisis.[+][-]