Advances in Econometrics publishes original scholarly econometric papers with the intention of expanding the use of developed and emerging econometric techniques by disseminating ideas on the theory and practice of econometrics, throughout the empirical economic, business and social science literature.

This valuable text provides a comprehensive introduction to VAR modelling and how it can be applied. In particular, the author focuses on the properties of the Cointegrated VAR model and its implications for macroeconomic inference when data are non-stationary. The text provides a number of insights into the links between statistical econometric modelling and economic theory and gives a thorough treatment of identification of the long-run and short-run structure as well as of the common stochastic trends and the impulse response functions, providing in each case illustrations of applicability. This book presents the main ingredients of the Copenhagen School of Time-Series Econometrics in a transparent and coherent framework. The distinguishing feature of this school is that econometric theory and applications have been developed in close cooperation. The guiding principle is that good econometric work should take econometrics, institutions, and economics seriously. The author uses a single data set throughout most of the book to guide the reader through the econometric theory while also revealing the full implications for the underlying economic model. To test ensure full understanding the book concludes with the introduction of two new data sets to combine readers understanding of econometric theory and economic models, with economic reality.

This book discusses the econometric foundations of structural vector autoregressive modeling, as used in empirical macroeconomics, finance, and related fields.

This volume of Advances in Econometrics contains articles that examine key topics in the modeling and estimation of dynamic stochastic general equilibrium (DSGE) models. Because DSGE models combine micro- and macroeconomic theory with formal econometric modeling and inference, over the past decade they have become an established framework for analy

Bringing together the recent advances and innovative methods in macroeconomic forecasting, this erudite Handbook outlines how to forecast, including following world events such as the Covid-19 pandemic and the global financial crisis. With contributions from global experts, chapters explore the use of machine-learning techniques, the value of social media data, and climate change forecasting. This title contains one or more Open Access chapters.

Each chapter of Macroeconometrics is written by respected econometricians in order to provide useful information and perspectives for those who wish to apply econometrics in macroeconomics. The chapters are all written with clear methodological perspectives, making the virtues and limitations of particular econometric approaches accessible to a general readership familiar with applied macroeconomics. The real tensions in macroeconometrics are revealed by the critical comments from different econometricians, having an alternative perspective, which follow each chapter.

The GVAR is a global Vector autoregression model of the global economy. The model was initially developed in the early 2000 by Professor Pesaran and co-authors, for the main purpose of analysing credit risk in a globalised economy. Starting from mid-2000 the model was substantially enlarged in the context of a project financed by the ECB, to comprise all major economies and the Euro area as a whole. The purpose of this version was to exploit the rich modelisation of international linkages in order to simulate and analyse global macro scenarios of high policy interest. The rich, yet manageable, specification of international linkages has stimulated a vast literature on the GVAR. Since early 2011, the basic model - and its data base - has also available on a dedicated GVAR-Toolbox website with an easy-to-use interface allowing practical applications by an extended audience, as well as more complex analysis by the expert public. The book provides an overview of the extensions and applications of the GVAR which have been developed in recent years. Such applications are grouped in three main categories: 1) International transmission and forecasting; 2) Finance applications; and 3) Regional applications. By using a language which is accessible to not econometricians, the book reaches out to the extended audience of practitioners and policy makers interested in understanding channels and impacts of international linkages.

Macroeconomic policy is one of the most important policy domains, and the tools of macroeconomics are among the most valuable for policy makers. Yet there has been, up to now, a wide gulf between the level at which macroeconomics is taught at the undergraduate level and the level at which it is practiced. At the same time, doctoral-level textbooks are usually not targeted at a policy audience, making advanced macroeconomics less accessible to current and aspiring practitioners. This book, born out of the Masters course the authors taught for many years at the Harvard Kennedy School, fills this gap. It introduces the tools of dynamic optimization in the context of economic growth, and then applies them to a wide range of policy questions – ranging from pensions, consumption, investment and finance, to the most recent developments in fiscal and monetary policy. It does so with the requisite rigor, but also with a light touch, and an unyielding focus on their application to policy-making, as befits the authors’ own practical experience. Advanced Macroeconomics: An Easy Guide is bound to become a great resource for graduate and advanced undergraduate students, and practitioners alike.

This volume includes papers delivered at the Fourth World Congress of the Econometric Society. It will interest economic theorists and econometricians working in universities, government, and business and financial institutions.

This text presents modern developments in time series analysis and focuses on their application to economic problems. The book first introduces the fundamental concept of a stationary time series and the basic properties of covariance, investigating the structure and estimation of autoregressive-moving average (ARMA) models and their relations to the covariance structure. The book then moves on to non-stationary time series, highlighting its consequences for modeling and forecasting and presenting standard statistical tests and regressions. Next, the text discusses volatility models and their applications in the analysis of financial market data, focusing on generalized autoregressive conditional heteroskedastic (GARCH) models. The second part of the text devoted to multivariate processes, such as vector autoregressive (VAR) models and structural vector autoregressive (SVAR) models, which have become the main tools in empirical macroeconomics. The text concludes with a discussion of co-integrated models and the Kalman Filter, which is being used with increasing frequency. Mathematically rigorous, yet application-oriented, this self-contained text will help students develop a deeper understanding of theory and better command of the models that are vital to the field. Assuming a basic knowledge of statistics and/or econometrics, this text is best suited for advanced undergraduate and beginning graduate students.