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Full Description
This book provides a new contemporary time series approach for econometrics and finance. In a concrete manner a very general divergence between spectra is introduced, resulting in the development of a statistical inference that is efficient and robust, and leads to a new perspective. A measure of systemic risk is also developed in the energy market,which quantifies the cost of energy asset distress vis-à-vis the broader economy during crises, and examines the dynamic interaction between solvency and funding liquidity risk in banks using a panel vector autoregressive (VAR) model. This step shows that a forward-looking measure of capital shortfall under stress is both a predictor and an outcome of funding liquidity risk. Additionally, a new integrated likelihood-based approach for estimating nonlinear panel data models is described. Unlike existing integrated likelihoods, the new integrated likelihood is closer to a genuine likelihood. The book explains why this is due to first-order information unbiasedness, and why it seems to matter more for inference than for estimation. Results of studies in econometrics are provided for support.
Contents
1 Introduction.- 2 Hellinger Distance Estimation for Non-Regular Spectra.- 3 Local Whittle likelihood approach for generalized divergence.- 4 Systemic Risk in Energy Markets: Measuring Co-Movements in Energy Asset Prices During Crises.- 5 Modeling Solvency and Liquidity Interactions in Banking: A Panel VAR Analysis.- 6 Integrated likelihood based inference for nonlinear panel data models.- 7 Reducing score and information bias in panel data likelihoods.- 8 Shrinkage estimators of BLUE for time series regression models.
