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

Using a generalized vector autoregressive framework in which forecast-error variance decompositions are invariant to variable ordering, we propose measures of both total and directional volatility spillovers. We use our methods to characterize daily volatility spillovers across U.S. stock, bond, foreign exchange and commodities markets, from January 1999 through January 2010. We show that despite significant volatility fluctuations in all four markets during the sample, cross-market volatility spillovers were quite limited until the global financial crisis that began in 2007. As the crisis intensified so too did the volatility spillovers, with particularly important spillovers from the stock market to other markets taking place after the collapse of Lehman Brothers in September 2008.

Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers

Tnnumerable media reports over the past few years purport to find evidence of decliningjob stability in the U.S. economy. Of course, articles in the media do not necessarily provide evidence of actual trends in the economy, since they are not based on representative sampling. However, in a recent issue of this journal, Swinnerton and Wial (1995) claimed to find empirical evidence of declining job stability in the U.S. economy over the 1980s, based on representative samples from the tenure supplements to the Current Population Surveys. Averaging over all workers, they reported that the four-year retention rate, or the probability of remaining in ajob for four or more years, fell from .55 in 1983 to .49 in 1987. Although they acknowledged that firm conclusions cannot be drawn from two data points, Swinnerton and Wial

Comment on Kenneth A. Swinnerton and Howard Wial, “Is Job Stability Declining in the U.S. Economy?”

A large literature over several decades reveals both extensive concern with the question of time-varying betas and an emerging consensus that betas are in fact time-varying, leading to the prominence of the conditional CAPM. Set against that background, we assess the dynamics in realized betas, vis-a-vis the dynamics in the underlying realized market variance and individual equity covariances with the market. Working in the recently-popularized framework of realized volatility, we are led to a framework of nonlinear fractional cointegration: although realized variances and covariances are very highly persistent and well approximated as fractionally-integrated, realized betas, which are simple nonlinear functions of those realized variances and covariances, are less persistent and arguably best modeled as stationary I(0) processes. We conclude by drawing implications for asset pricing and portfolio management.

Realized Beta: Persistence and Predictability

We introduce the financial economics of market microstructure to the financial econometrics of asset return volatility estimation. In particular, we derive the crosscorrelation function between latent returns and market microstructure noise in several leading microstructure environments. We propose and illustrate several corresponding theory-inspired volatility estimators, which we apply to stock and oil prices. Our analysis and results are useful for assessing the validity of the frequently-assumed independence of latent price and microstructure noise, for explaining observed cross-correlation patterns, for predicting as-yet undiscovered patterns, and most importantly, for promoting improved microstructure-based volatility empirics and improved empirical microstructure studies. Simultaneously and conversely, our analysis is far from the last word on the subject, as it is based on stylized benchmark models; it comes with a “call to action” for development and use of richer microstructure models in volatility estimation and beyond.

/pdf/on-the-correlation-structure-of-microstructure-noise-a-2irejx1u2b.pdf

On the Correlation Structure of Microstructure Noise: A Financial Economic Approach

https://www.federalreserve.gov/pubs/feds/1996/199608/199608pap.pdf

Francis X. Diebold

Papers

Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers

Comment on Kenneth A. Swinnerton and Howard Wial, “Is Job Stability Declining in the U.S. Economy?”

Realized Beta: Persistence and Predictability

On the Correlation Structure of Microstructure Noise: A Financial Economic Approach

Why are estimates of agricultural supply response so variable