1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Summary. The availability of intraday data on the prices of speculative assets means that we can use quadratic variation-like measures of activity in financial markets, called realized volatility, to study the stochastic properties of returns. Here, under the assumption of a rather general stochastic volatility model, we derive the moments and the asymptotic distribution of the realized volatility error—the difference between realized volatility and the discretized integrated volatility (which we call actual volatility). These properties can be used to allow us to estimate the parameters of stochastic volatility models without recourse to the use of simulation-intensive methods.

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Econometric analysis of realized volatility and its use in estimating stochastic volatility models

This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library.

As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

The C programming language

Matrix Differential Calculus with Applications in Statistics and Econometrics

We suggest a convenient version of the omnibus test for normality, using skewness and kurtosis based on Shenton and Bowman [Journal of the American Statistical Association (1977) Vol. 72, pp. 206–211], which controls well for size, for samples as low as 10 observations. A multivariate version is introduced. Size and power are investigated in comparison with four other tests for multivariate normality. The first power experiments consider the whole skewness–kurtosis plane; the second use a bivariate distribution which has normal marginals. It is concluded that the proposed test has the best size and power properties of the tests considered.

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An Omnibus Test for Univariate and Multivariate Normality

This paper discusses and documents the algorithms of SsfPack 2.2. SsfPack is a suite of C routines for carrying out computations involving the statistical analysis of univariate and multivariate models in state space form. The emphasis is on documenting the link we have made to the Ox computing environment. SsfPack allows for a full range of different state space forms: from a simple time-invariant model to a complicated time-varying model. Functions can be used which put standard models such as ARMA and cubic spline models in state space form. Basic functions are available for filtering, moment smoothing and simulation smoothing. Ready-to-use functions are provided for standard tasks such as likelihood evaluation, forecasting and signal extraction. We show that SsfPack can be easily used for implementing, fitting and analysing Gaussian models relevant to many areas of econometrics and statistics. Some Gaussian illustrations are given.

/pdf/statistical-algorithms-for-models-in-state-space-using-5aou0su86u.pdf

Statistical algorithms for models in state space using SsfPack 2.2

This paper discusses and documents the algorithms of SsfPack 2.2. SsfPack is a suite of C routines for carrying out computations involving the statistical analysis of univariate and multivariate models in state space form. The emphasis is on documenting the link we have made to the Ox computing environment. SsfPack allows for a full range of different state space forms: from a simple time-invariant model to a complicated time-varying model. Functions can be used which put standard models such as ARIMA and cubic spline models in state space form. Basic functions are available for filtering, moment smoothing and simulation smoothing. Ready-to-use functions are provided for standard tasks such as likelihood evaluation, forecasting and signal extraction. We show that SsfPack can be easily used for implementing, fitting and analysing Gaussian models relevant to many areas of econometrics and statistics. Some Gaussian illustrations are given.

Existing methods of reconstructing historical Euro-zone data by aggregation of the individual countries' aggregate data raises numerous difficulties, especially due to past exchange rate changes. The approach proposed here is designed to avoid such distortions, and aggregate exactly when exchange rates are fixed. We first compute growth rates within states, aggregate these, then cumulate this Euro-zone growth rate to obtain the aggregated levels variables. The aggregate of the implicit-deflator plice index coincides with the implicit deflator of our aggregate nominal and real data. We apply the method to Euro-zone M3, GDP and prices over the previous two decades. 1. Background With the introduction on 31 December 1998 of the irrevocably-fixed Euro exchange rates, eleven countries of the European Union entered a new monetary union. From that date onwards, monetary policy for these countries is set by the newly-formed European Central Bank (ECB). The Governing Council of the ECB bases its monetary policy on 'medium-term assumptions regarding real GDP growth and the trend decline in the velocity of circulation of M3' (ECB, 1999a, p. 40). Therefore, the construction of historical data for aggregate M3 and GDP for the Euro zone is of practical relevance to policy makers. In addition, any econometric model for Euro-zone countries requires such historical data. This paper describes an approach to doing so from the aggregate data of the individual member states. A number of previous reconstructions are noted, but as these have important drawbacks, an approach related to that in T6rnqvist (1936) is proposed and its properties are discussed. Although there have been many important contributions to index-number theory (see Section 4), few are directly relevant to the problem confronted here, where we wish to use the national aggregates from member states - not the individual-item data usually input into index calculations - to construct a Euro-zone aggregate. Moreover, the inherently non-stationary nature of those national aggregates, both from unit-roots inducing integrated time series

/pdf/constructing-historical-euro-zone-data-4twcmqjh0y.pdf

Constructing historical euro-zone data*

The "PcGive" paperback is part of the "PcGive Professional 8.0" package. This package offers a range of data description facilities, methods of dynamic model estimation and evaluation tests. It is fully interactive and menu-driven, with features that include: 50,000 observations on 500 variables; data import from spreadsheets or ASCII files; up to 16 graphs on screen simultaneously; cross plot and time plot facilities; and documentation in the form of two books - PcGive and PcFiml. Licenses are available on application.

Jurgen A. Doornik

Papers

An Omnibus Test for Univariate and Multivariate Normality

Statistical algorithms for models in state space using SsfPack 2.2

Statistical algorithms for models in state space using SsfPack 2.2

Constructing historical euro-zone data*

PcGive 8.0 : an interactive econometric modelling system