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Title: Panel time series analysis : some theory and applications
Author: Nocera, Andrea
ISNI:       0000 0004 6349 8088
Awarding Body: Birkbeck, University of London
Current Institution: Birkbeck (University of London)
Date of Award: 2017
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This thesis offers some theoretical contributions to the literature on large heterogeneous panel data models. It also demonstrates their practical use in empirical research, in the field of housing in macroeconomics, and for the analysis of the determinants of sovereign credit spreads. The first chapter provides the motivation for the research presented in this thesis. In the second chapter, we investigate the causes and the finite-sample consequences of negative definite covariance matrices in Swamy type random coefficient models. Monte Carlo experiments reveal that the negative definiteness problem is less severe when the degree of coefficient dispersion is substantial, and the precision of the regression disturbances is high. The sample size also plays a crucial role. We then evaluate the direct consequences of relying on the asymptotic properties of the estimator of the random coefficient covariance for hypothesis tests. A solution to the aforementioned problem is proposed in the third chapter. In particular, we propose to implement the EM algorithm to compute restricted maximum likelihood estimates of both the average effects and the unit-specific coefficients as well as of the variance components in a wide class of heterogeneous panel data models. Compared to existing methods, our approach leads to unbiased and more efficient estimation of the variance components of the model without running into the problem of negative definite covariance matrices typically encountered in random coefficient models. This in turn leads to more accurate estimated standard errors and hypothesis tests. Monte Carlo simulations reveal that the proposed estimator has relatively good finite sample properties. In evaluating the merits of our estimator, we also provide an overview of the sampling and Bayesian methods commonly used to estimate heterogeneous panel data. A novel approach to investigate heterogeneity of the sensitivity of sovereign spreads to government debt is presented. In a final chapter, we use a structural Bayesian (stochastic search variable selection) vector autoregressive model to investigate the heterogeneous impact of housing demand shocks on the macro-economy and the role of house prices in the monetary policy transmission, across euro area countries. A novel set of identification restrictions, which combines zero and sign restrictions, is proposed. By exploiting the cross-sectional dimension of our data, we explore the differences in the propagation channels of house prices and monetary policy and the challenges they pose in the process of real and nominal convergence in the Eurozone. Among the main results, we find a comparatively stronger housing wealth effect on consumption in Ireland and Spain. We provide new evidence in support of the financial accelerator hypothesis, showing that house prices play an important role in the availability of loans. A significant and highly heterogeneous effect of monetary policy on house price dynamics is also documented.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available