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

Models that allow for autoregressive conditional heteroskedasticity (ARCH) in the error process have recently found widespread application. The purpose of this paper is to evaluate, through Monte Carlo analysis, the small sample properties of an exact Lagrange multiplier test for the presence of ARCH, and to compare the power of this test with that of an asymptotically equivalent TR2 version. The comparison involves first-and higher-order variants of these processes. The results indicate substantial power differentials in favor of the exact LM test, by up to 15% for sample sizes smaller than 100.

Small sample properties of asymptotically equivalent tests for autoregressive conditional heteroskedasticity

We provide an investigation of duration dependence in prewar business expansions, contractions, and whole cycles for France, Germany, and Great Britain. Our results, obtained using both nonparametric and parametric procedures, generally indicate the presence of positive duration dependence in expansions and whole cycles but not in contractions. Our results corroborate those of our earlier studies of the United States.

International evidence on business cycle duration dependence

The exact initial covariance matrix of the state vector of a general MA(q) process

Since 1968, the Survey of Professional Forecasters has asked respondents to provide a" complete probability distribution of expected future inflation. We evaluate the adequacy of" those density forecasts using the framework of Diebold, Gunther and Tay (1997). The analysis" reveals several interesting features of the density forecasts in relation to realized inflation including several deficiencies of the forecasts. The probability of a large negative inflation" shock is generally overestimated, and in more recent years the probability of a large shock of" either sign is overestimated. Inflation surprises are serially correlated eventually adapt. Expectations of low inflation are associated with reduced uncertainty. The" results suggest several promising directions for future research.

Francis X. Diebold

Papers

Small sample properties of asymptotically equivalent tests for autoregressive conditional heteroskedasticity

International evidence on business cycle duration dependence

The exact initial covariance matrix of the state vector of a general MA(q) process

Evaluating Density Forecasts of Inflation: The Survey of Professional Forecasters

Testing for bubbles, reflecting barriers and other anomalies☆