Search Results

Covariance Structures for Dynamic Error Components

Manuel Arellano

in Panel Data Econometrics

This chapter analyses the time series properties of panel data sets, focusing on short panels. It discusses time effects and moving average covariances. It presents estimates of covariance structures ... More

This chapter analyses the time series properties of panel data sets, focusing on short panels. It discusses time effects and moving average covariances. It presents estimates of covariance structures and tests the permanent income hypothesis.Less

T. N. Thiele's contributions to statistics 77

A. Hald

in Thiele: Pioneer in Statistics

This chapter presents a reprint of Hald (1981), containing a detailed discussion of Thiele's contributions to statistics and a brief summary of some of his contributions to other areas. Topics ... More

This chapter presents a reprint of Hald (1981), containing a detailed discussion of Thiele's contributions to statistics and a brief summary of some of his contributions to other areas. Topics covered include skew distributions, cumulants, estimation methods and k statistics, the linear model with normally distributed errors, analysis of variance, and a time series model combining Brownian motion and the linear model with normally distributed errors. Thiele's work is placed in a historical perspective and explained in modern terms.Less

Non-Linear Time Series Models

Luc Bauwens, Michel Lubrano, and Jean-François Richard

in Bayesian Inference in Dynamic Econometric Models

This chapter examines non-linear time series models in relation to the Bayesian inference approach in econometric analysis. There are two types of models which are quite different for the treatment ... More

This chapter examines non-linear time series models in relation to the Bayesian inference approach in econometric analysis. There are two types of models which are quite different for the treatment of inference. The first class of models corresponds to models with a threshold while the second class has been advocated as a reasonable approximation to the disequilibrium models proposed in 1974 by Maddala and Nelson. This chapter also analyses the so-called one-market disequilibrium model.Less

Dynamic Time Series Model

Aman Ullah

in Finite Sample Econometrics

This chapter presents the finite sample analysis of the time series models used in economics and finance. It considers the autoregressive model (AR), AR with regressors, and autoregressive moving ... More

This chapter presents the finite sample analysis of the time series models used in economics and finance. It considers the autoregressive model (AR), AR with regressors, and autoregressive moving average models with regressors. The exact and approximate moments, as well as distributions of the estimators of the lag coefficients and regression coefficients were derived and analysed.Less

Conclusion

Anindya Banerjee, Juan J. Dolado, John W. Galbraith, and David F. Hendry

in Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data

The final chapter summarizes the main themes of the book, and considers the invariance of the matrix of co‐integrating vectors in a linear system under both linear transformations and seasonal ... More

The final chapter summarizes the main themes of the book, and considers the invariance of the matrix of co‐integrating vectors in a linear system under both linear transformations and seasonal adjustment. Next, co‐integration is related to structured time‐series models that offer an alternative approach to modelling integrated data. Further developments in the theory are described, and the book concludes by reinterpreting some old econometric problems in light of co‐integration theory.Less

Introduction

Aman Ullah

in Finite Sample Econometrics

This introductory chapter begins with a brief discussion on the importance of finite sample econometrics. The developments in this area are described. An overview of the chapters in this volume is ... More

This introductory chapter begins with a brief discussion on the importance of finite sample econometrics. The developments in this area are described. An overview of the chapters in this volume is then presented.Less

Illustrations of the use of the linear model

J. Durbin and S.J. Koopman

in Time Series Analysis by State Space Methods: Second Edition

This chapter discusses examples which show how the use of the linear model works in practice. The first example is an analysis of road accident data to estimate the reduction in car drivers killed ... More

This chapter discusses examples which show how the use of the linear model works in practice. The first example is an analysis of road accident data to estimate the reduction in car drivers killed and seriously injured in the UK due to the introduction of a seat belt law. The second example considers a bivariate model which includes data on numbers of front seat passengers killed and seriously injured and on numbers of rear seat passengers killed and seriously injured. The third example shows how state space methods can be applied to Box-Jenkins ARMA models employed to model series of users logged onto the Internet. The fourth example considers the state space solution to the spline smoothing of motorcycle acceleration data. The fifth example provides a dynamic factor analysis based on the linear Gaussian model for the term structure of interest rates paid on US Treasury securities.Less

Attribution Analysis

Nanne Brunia and Auke Plantinga

in Portfolio Theory and Management

This chapter discusses performance attribution models allowing the observer to identify market timing and security selection skills of portfolio managers. The focus is on holdings-based models ... More

This chapter discusses performance attribution models allowing the observer to identify market timing and security selection skills of portfolio managers. The focus is on holdings-based models because they generate more precise measurements of managers' skills than return-based models. The discussion starts with the basic attribution model and how to extend this model to accommodate internationally diversified portfolios. The basic model can also be extended to improve the precision of the measurements by allowing the user to create a risk-adjusted performance attribution without the need to run a time-series regression model. In order to capture the impact of investor timing, performance attribution models can be extended further by including a component based on the internal rate of return.Less

The development of a time series methodology: from recursive residuals to dynamic conditional score models

Andrew Harvey

in Unobserved Components and Time Series Econometrics

This chapter discusses the developments in unobserved components time series models and their statistical and econometric treatments from the early 1970s to the present day.

Consistent Testing of Functional Form in Time Series Models

James Davidson and Andreea G. Halunga

in Essays in Nonlinear Time Series Econometrics

We develop a consistent procedure for testing the adequacy of parametric time series models. The approach is to extend Herman Bierens’s idea of examining the covariances between regression residuals ... More

We develop a consistent procedure for testing the adequacy of parametric time series models. The approach is to extend Herman Bierens’s idea of examining the covariances between regression residuals and an exponential weight function, to check the full range of orthogonalities predicted for the score contributions in quasi-maximum likelihood estimation. Tests of this type, which involve nuisance parameters, are constructed as either "sup"ed or integrated conditional moment tests, and are often implemented using bootstrap methods. However, our emphasis in this study is on practical implementation. We study a two-statistic approach that aims to exploit the available power while keeping computing requirements to a minimum.Less

Reforming the Labour Market in Good and Bad Times: Any Differences?

Britta Gehrke and Enzo Weber

in The Political Economy of Structural Reforms in Europe

This chapter discusses how the effects of structural labour market reforms depend on whether the economy is in expansion or recession. Based on an empirical time series model with Markov switching ... More

This chapter discusses how the effects of structural labour market reforms depend on whether the economy is in expansion or recession. Based on an empirical time series model with Markov switching that draws on search and matching theory, we propose a novel identification of reform outcomes and distinguish the effects of structural reforms that increase the flexibility of the labour market in distinct phases of the business cycle. We find in applications to Germany and Spain that reforms which are implemented in recessions have weaker expansionary effects in the short run. For policymakers, these results emphasize the costs of introducing labour market reforms in recessions.Less