http://homepage.ntu.edu.tw/~ntuperc/docs/publication/2002_25_Lin.pdf

Unit root tests in panel data: asymptotic and finite-sample properties

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?

Preface1Difference Equations12Lag Operators253Stationary ARMA Processes434Forecasting725Maximum Likelihood Estimation1176Spectral Analysis1527Asymptotic Distribution Theory1808Linear Regression Models2009Linear Systems of Simultaneous Equations23310Covariance-Stationary Vector Processes25711Vector Autoregressions29112Bayesian Analysis35113The Kalman Filter37214Generalized Method of Moments40915Models of Nonstationary Time Series43516Processes with Deterministic Time Trends45417Univariate Processes with Unit Roots47518Unit Roots in Multivariate Time Series54419Cointegration57120Full-Information Maximum Likelihood Analysis of Cointegrated Systems63021Time Series Models of Heteroskedasticity65722Modeling Time Series with Changes in Regime677A Mathematical Review704B Statistical Tables751C Answers to Selected Exercises769D Greek Letters and Mathematical Symbols Used in the Text786Author Index789Subject Index792

https://sisis.rz.htw-berlin.de/inh2007/12357004.pdf

Time series analysis

Any series of observations ordered along a single dimension, such as time, may be thought of as a time series. The emphasis in time series analysis is on studying the dependence among observations at different points in time. What distinguishes time series analysis from general multivariate analysis is precisely the temporal order imposed on the observations. Many economic variables, such as GNP and its components, price indices, sales, and stock returns are observed over time. In addition to being interested in the contemporaneous relationships among such variables, we are often concerned with relationships between their current and past values, that is, relationships over time.

Time Series Analysis

We characterize equity return volatility connectedness in the network of major American and European financial institutions, 2004-2014. Our methods enable precise characterization of the timing and evolution of key aspects of the financial crisis. First, we find that during 2007-2008 the direction of connectedness was clearly from the U.S. to Europe, but that connectedness became bi-directional starting in late 2008. Second, we find an unprecedented surge in directional connectedness from European to U.S. financial institutions in June 2011, consistent with massive deterioration in the health of EU financial institutions. Third, we identify particular institutions that played disproportionately important roles in generating connectedness during the U.S. and the European crises.

/pdf/trans-atlantic-equity-volatility-connectedness-u-s-and-vodm64tq8i.pdf

Trans-Atlantic Equity Volatility Connectedness: U.S. and European Financial Institutions, 2004-2014

I investigate the origins of the now-ubiquitous term â€ Big Data," in industry and academics, in computer science and statistics/econometrics. Credit for coining the term must be shared. In particular, John Mashey and others at Silicon Graphics produced highly relevant (unpublished, non-academic) work in the mid-1990s. The first significant academic references (independent of each other and of Silicon Graphics) appear to be Weiss and Indurkhya (1998) in computer science and Diebold (2000) in statistics /econometrics. Douglas Laney of Gartner also produced insightful work (again unpublished and non-academic) slightly later. Big Data the term is now firmly entrenched, Big Data the phenomenon continues unabated, and Big Data the discipline is emerging.

On the Origin(s) and Development of the Term â€œBig Data"

The econometrics of macroeconomics, finance, and the interface

By exploiting the insight that the misspecification of dynamic stochastic general equilibrium (DSGE) models is more prevalent at some frequencies than at others, we develop methods that enable different degrees of relaxation of the DSGE restrictions in different directions. We approximate the DSGE model by a vector autoregression. Dummy observations are constructed from the DSGE model and converted into the frequency domain. By re-weighting the frequency domain dummy observations we can control the extent to which the restrictions derived from economic theory are relaxed. Bayesian marginal data densities can then be used to obtain a data-driven procedure that determines the optimal degree of shrinkage toward the DSGE model restrictions. We provide several numerical illustrations of our procedure. JEL CLASSIFICATION: C32, E52, F41

/pdf/priors-from-frequency-domain-dummy-observations-3fh8otd164.pdf

Francis X. Diebold

Papers

Trans-Atlantic Equity Volatility Connectedness: U.S. and European Financial Institutions, 2004-2014

On the Origin(s) and Development of the Term â€œBig Data"

The econometrics of macroeconomics, finance, and the interface

Priors from Frequency-Domain Dummy Observations

Exact maximum likelihood estimation of ARCH models