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

This package performs specialized tasks related to specification, estimation, prediction and diagnostic checking in the context of a particular class of unobserved-components models. It is menu-driven and easy to use. Although the present (first) release suffers from a number of limitations, omissions and bugs, it is nevertheless a useful tool in the hands of a knowledgeable user.

Structural time series analysis and modelling package: A review

Two often-divergent U.S. GDP estimates are available, a widely-used expenditure-side version GDPE, and a much less widely-used income-side version GDI . The authors propose and explore a "forecast combination" approach to combining them. They then put the theory to work, producing a superior combined estimate of GDP growth for the U.S., GDPC. The authors compare GDPC to GDPE and GDPI , with particular attention to behavior over the business cycle. They discuss several variations and extensions.

Improving GDP measurement: a forecast combination perspective

We provide a framework for evaluating and improving multivariate density forecasts. Among other things, the multivariate framework lets us evaluate the adequacy of density forecasts involving cross-variable interactions, such as time-varying conditional correlations. We also provide conditions under which a technique of density forecast "calibration" can be used to improve deficient density forecasts. Finally, motivated by recent advances in financial risk management, we provide a detailed application to multivariate high-frequency exchange rate density forecasts.

Real-Time Multivariate Density Forecast Evaluation and Calibration: Monitoring the Risk of High-Frequency Returns on Foreign Exchange

We study the usefulness of root tests as diagnostic tools for selecting forecasting models. Difference stationary and trend stationary models of economic and financial time series often imply very different predictions, so deciding which model to use is tremendously important for applied forecasters. Forecasters face three choices: always difference the data, never difference, or use a unit-root pretest. We characterize the predictive loss of these strategies for the canonical AR(1) process with trend, focusing on the effects of sample size, forecast horizon, and degree of persistence. We show that pretesting routinely improves forecast accuracy relative to forecasts from models in differences, and we give conditions under which pretesting is likely to improve forecast accuracy relative to forecasts from models in levels.

Unit Root Tests are Useful for Selecting Forecasting Models

We propose and implement a framework for characterizing and monitoring the global business cycle. Our framework utilizes high-frequency data, allows us to account for a potentially large amount of missing observations, and is designed to facilitate the updating of global activity estimates as data are released and revisions become available. We apply the framework to the G-7 countries and study various aspects of national and global business cycles, obtaining three main results. First, our measure of the global business cycle, the common G-7 real activity factor, explains a significant amount of cross-country variation and tracks the major global cyclical events of the past forty years. Second, the common G-7 factor and the idiosyncratic country factors play different roles at different times in shaping national economic activity. Finally, the degree of G-7 business cycle synchronization among country factors has changed over time.

Francis X. Diebold

Papers

Structural time series analysis and modelling package: A review

Improving GDP measurement: a forecast combination perspective

Real-Time Multivariate Density Forecast Evaluation and Calibration: Monitoring the Risk of High-Frequency Returns on Foreign Exchange

Unit Root Tests are Useful for Selecting Forecasting Models

Globalization, the Business Cycle, and Macroeconomic Monitoring