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

Understanding the dynamic evolution of the yield curve is critical to many financial tasks, including pricing financial assets and their derivatives, managing financial risk, allocating portfolios, structuring fiscal debt, conducting monetary policy, and valuing capital goods. Unfortunately, most yield curve models tend to be theoretically rigorous but empirically disappointing, or empirically successful but theoretically lacking. In this book, Francis Diebold and Glenn Rudebusch propose two extensions of the classic yield curve model of Nelson and Siegel that are both theoretically rigorous and empirically successful. The first extension is the dynamic Nelson-Siegel model (DNS), while the second takes this dynamic version and makes it arbitrage-free (AFNS). Diebold and Rudebusch show how these two models are just slightly different implementations of a single unified approach to dynamic yield curve modeling and forecasting. They emphasize both descriptive and efficient-markets aspects, they pay special attention to the links between the yield curve and macroeconomic fundamentals, and they show why DNS and AFNS are likely to remain of lasting appeal even as alternative arbitrage-free models are developed.

	Based on the Econometric and Tinbergen Institutes Lectures, Yield Curve Modeling and Forecasting contains essential tools with enhanced utility for academics, central banks, governments, and industry.

/pdf/yield-curve-modeling-and-forecasting-the-dynamic-nelson-2y3zsfje6g.pdf

Yield Curve Modeling and Forecasting: The Dynamic Nelson-Siegel Approach

Fifteen years after the seminal work of Charles R. Nelson and Charles I. Plosser ( 1982), the question of deterministic vs. stochastic trend in U.S. GNP (and other key aggregates) remains open. The surrounding controversy certainly is not due to lack of professional interest-the literature on the question is huge. Instead, the low power of tests of stochastic trend (or "difference stationarity" in the parlance of John H. Cochrane [1988]) against nearby deterministic-trend ("trend-stationary") alternatives, together with the fact that such nearby alternatives are the relevant ones, explains the lack of consensus. In an important paper, Glenn D. Rudebusch (1993) contributes to the "we don't know" literature initiated by Lawrence J. Christiano and Martin Eichenbaum (1990) by arguing that unit-root tests applied to U.S. quarterly real GNP per capita lack power even against distant alternatives. First, Rudebusch shows that the best-fitting trend-stationary and difference-stationary models imply very different mediumand long-run dynamics. Then he shows with an innovative procedure that, regardless of which of the two models obtains, the exact finite-sample distributions of the Dickey-Fuller test statistics are very similar. Thus, Rudebusch concludes that unit-root tests are unlikely to be capable of discriminating between deterministic and stochastic trends. The distinction between trend stationarity is not critical in some contexts. Often, for example, one wants a broad gauge of the persistence in aggregate output dynamics, in which case one may be better informed by an interval estimate of the dominant root in an autoregressive approximation. Hence the importance of James H. Stock's (1991) clever procedure for computing such intervals. But the distinction between trend stationarity and difference stationarity is potentially important in other contexts, such as economic forecasting, because the trendand differencestationary models may imply very different dynamics and hence different point forecasts, as argued by Stock and Mark W. Watson (1988) and John Y. Campbell and Pierre Perron (1991) . Motivated by the potential importance of unit roots for the forecasting of aggregate output, as well as other considerations that we discuss later, we extend Rudebusch's (1993) analysis to several long spans of annual U.S. real GNP data, and we examine the robustness of all results to variations in the sample period and the particular GNP measure employed. As we shall show, the outcome is both surprising and robust.

The Uncertain Unit Root in Real GNP: Comment

Further evidence on business cycle duration dependence

We consider the forecasting of cointegrated variables, and we show that at long horizons" nothing is lost by ignoring cointegration when forecasts are evaluated using standard multivariate" forecast accuracy measures. In fact, simple univariate Box-Jenkins forecasts are just as accurate. " Our results highlight a potentially important deficiency of standard forecast accuracy" measures they fail to value the maintenance of cointegrating relationships among" variables and we suggest alternatives that explicitly do so.

Cointegration and Long-Horizon Forecasting

The Svensson generalization of the popular Nelson-Siegel term structure model is widely used by practitioners and central banks Unfortunately, like the original Nelson-Siegel specification, this generalization, in its dynamic form, does not enforce arbitrage-free consistency over time Indeed, we show that the factor loadings of the Svensson generalization cannot be obtained in a standard finance arbitrage-free affine term structure representation Therefore, we introduce a closely related generalized Nelson-Siegel model on which the no-arbitrage condition can be imposed We estimate this new arbitrage-free generalized Nelson-Siegel model and demonstrate its tractability and good in-sample fit

/pdf/an-arbitrage-free-generalized-nelson-siegel-term-structure-51gstute7s.pdf

Francis X. Diebold

Papers

Yield Curve Modeling and Forecasting: The Dynamic Nelson-Siegel Approach

The Uncertain Unit Root in Real GNP: Comment

Further evidence on business cycle duration dependence

Cointegration and Long-Horizon Forecasting

An arbitrage‐free generalized Nelson–Siegel term structure model