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

Sovereign Bond Markets

Prediction, Extraction, and Estimation in Unobserved Components Model

Intertemporal consumer behavior under changes in income: a comment

The problem of optimal forecast combination is considered in situations of structural change. We develop a rather general approach, which combines the time-varying-parameter models of Diebold and Pauly (1987a) with allowance for prediction-error serial correlation as in Diebold (1988). The methodology is based on the regression-based paradigm of Granger and Ramanathan (1984), so that many earlier results emerge as special (and often restrictive) cases. Both deterministic and stochastic parameter variations are considered, with and without allowance for serial correlation. The results are illustrated in a series of examples.

Forecasting in Situations of Structural Change: A General Approach

In this paper we propose a systematic procedure to identify a set of representative yield curve shocks and use them for stress-testing purposes. We first fit a factor model to actual bond yields and estimate the main shape factors of the yield curve. We then partition the factors into non-overlapping sets of representative shocks. The key feature of our procedure is that it provides a wide variety of yield curve shocks including typical, uncommon, and extreme ones. We apply our methodology to a variety of bond strategies using actual U.S. yields.

/pdf/representative-yield-curve-shocks-and-stress-testing-10454bevjf.pdf

Francis X. Diebold

Papers

Sovereign Bond Markets

Prediction, Extraction, and Estimation in Unobserved Components Model

Intertemporal consumer behavior under changes in income: a comment

Forecasting in Situations of Structural Change: A General Approach

Representative yield curve shocks and stress testing