Introduction to Time Series and Forecasting (Springer Texts in Statistics)

This book builds on previous econometric theory and focuses on the most common or important models for econometric time-series and econometric forecasting. The focus of the book is on the two models at the core of econometric time-series/forecasting: ARMA and ARIMA. It covers different versions of AR and MA models in other words. Pre-requisites for this book are: 1. mathematical economics covering integrals, differentiation...as well as matrix algebra 2. statistics on first-year undergraduate level You should also know some 1st-year econometrics before you start with this book, through books such as Hill's Principles of Econometrics, Gujarati's Basic Econometrics, or Johnston's Econometric Methods and similar. It is true what a previous reviewer complained about - that this is not an introductory book on econometrics: you need to have studied econometrics before, the more mathematical the better, i.e Gujarati or Johnston's books. This is an introductory book on time-series, not econometrics. A very nice things is that the book uses many real-life examples, alot of graphs, and has detailed explanations of the mathematics. The examples are not just from the field of economics but other social sciences as well. My only complaint about the book is it's choice of software. The book uses a special program called ITSM, which comes with the book (2002 edition +), and they teach you how to use it. I think it would have been much better to have done the material with more "professional" software that is more frequently used in real-life applications - like Stata, Gauss, SAS, R or even EViews.

In contrast to their graduate text "Time Series: Theory and Methods" this book is more elementary and introductory and is pitched at the advanced undergraduate level requiring only calculus, elementary statistics and matrix algebra. It gives very good coverage to a wide variety of time series models and includes some nonstationary models. In this second edition the chapter on nonstationary models includes the latest coverage of ARCH and GARCH models presented in a way that I found very accessible. Computations are done with ITSM and in this edition the ITSM 2000 version 7.0 edition is included on a CD so that students can reproduce the authors' calculations and run analyses of their own. Another nice feature of the text that distinguishes it from other texts at this level is the introduction of multivariate time series, coverage of state space models, chaos and cointegration. Ideas are illustrated with examples. Important theory is discussed but is kept brief and theorems and proofs are not given to the extent of their other more theoretical text.

This is one of those books that you can't find much cons to it. The book is inexpensive, and it's unbelievably lightweight. The material is rich, and yet easy to understand. The author actually brings you step by step from elementary to theorectical proofs.

Very good introductory book to ARMA models. Full of real-life examples that provide some intuitive insight about the issues that may arise when modelling time series and forecasting. Requires some initial knowledge in statistics and algebra but if you're involved in time series modelling, it should be your first book.

Very good introductory book to ARMA models. Full of real-life examples that provide some intuitive insight about the issues that may arise when modelling time series and forecasting. Requires some initial knowledge in statistics and algebra but if you're involved in time series modelling, it should be your first book.