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 assess and apply the term-structure model introduced by Nelson and Siegel (1987) and re-interpreted by Diebold and Li (2003) as a modern three-factor model of level, slope and curvature. First, we ask whether the model is a member of the a‐ne class, and we flnd that it is not. Hence the poor forecasting performance recently documented for a‐ne term structure models in no way implies that our model will forecast poorly, which is consistent with Diebold and Li’s (2003) flnding that it indeed forecasts quite well. Next, having clarifled the relationship between our three-factor model and the a‐ne class, we proceed to assess its adequacy directly, by testing whether its level, slope and curvature factors do indeed capture systematic risk. We flnd that they do, and that they are therefore priced. Finally, confldent in the ability of our three-factor model to capture the pricing relations present in the data, we proceed to explore its e‐cacy in bond portfolio risk management. Traditional Macaulay duration is appropriate only in a one-factor (level) context; hence we move to a three-factor generalized duration, and we show the superior performance of hedges constructed using it. Contact author:

/pdf/a-three-factor-yield-curve-model-non-affine-structure-1j3xl75ozo.pdf

A Three-Factor Yield Curve Model: Non-Affine Structure, Systematic Risk Sources and Generalized Duration

Notwithstanding its impressive contributions to empirical financial economics, there remains a significant gap in the volatility literature, namely its relative neglect of the connection between macroeconomic fundamentals and asset return volatility. We progress by analyzing a broad international cross section of stock markets covering approximately forty countries. We find a clear link between macroeconomic fundamentals and stock market volatilities, with volatile fundamentals translating into volatile stock markets.

Macroeconomic Volatility and Stock Market Volatility, World-Wide

We estimate a model with latent factors that summarize the yield curve (namely, level, slope, and curvature) as well as observable macroeconomic variables (real activity, inflation, and the stance of monetary policy). Our goal is to provide a characterization of the dynamic interactions between the macroeconomy and the yield curve. We find strong evidence of the effects of macro variables on future movements in the yield curve and much weaker evidence for a reverse influence. We also relate our results to a traditional macroeconomic approach based on the expectations hypothesis.

/pdf/the-macroeconomy-and-the-yield-curve-a-nonstructural-506nfx3omo.pdf

The Macroeconomy and the Yield Curve: A Nonstructural Analysis

The huge theoretical and empirical econometric literatures on long memory and on structural change have evolved largely independently, as the phenomena appear distinct. We argue, in contrast, that they are intimately related. In particular, we show analytically that stochastic regime switching is easily confused with long memory, so long as only a "small" amount of regime switching occurs (in a sense that we make precise). A Monte Carlo analysis supports the relevance of the asymptotic theory in finite samples and produces additional insights.

/pdf/long-memory-and-structural-change-1tttmsehc9.pdf

Long Memory and Structural Change

We sketch a framework for monitoring macroeconomic activity in real-time and push it in new directions. In particular, we focus not only on real activity, which has received most attention to date, but also on inflation and its interaction with real activity. As for the recent recession, we find that (1) it likely ended around July 2009; (2) its most extreme aspects concern a real activity decline that was unusually long but less unusually deep, and an inflation decline that was unusually deep but brief; and (3) its real activity and inflation interactions were strongly positive, consistent with an adverse demand shock.

/pdf/real-time-macroeconomic-monitoring-real-activity-inflation-weswwhtnev.pdf

Francis X. Diebold

Papers

A Three-Factor Yield Curve Model: Non-Affine Structure, Systematic Risk Sources and Generalized Duration

Macroeconomic Volatility and Stock Market Volatility, World-Wide

The Macroeconomy and the Yield Curve: A Nonstructural Analysis

Long Memory and Structural Change

Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions