Applied Econometric Time Series 3rd Pdf 125
Applied Econometric Time Series: A Review of the Third Edition
Applied Econometric Time Series is a popular textbook by Walter Enders, a professor of economics at the University of Alabama. The book covers various topics in time series analysis, such as stationarity, cointegration, vector autoregression, forecasting, and nonlinear models. The book is aimed at graduate students and researchers who want to learn the theory and practice of econometric methods for time series data.
The third edition of the book was published in 2009 by Wiley. It has 125 more pages than the second edition, which was published in 2003. The third edition includes new chapters on unit root tests, structural breaks, Markov switching models, and state-space models. It also updates and expands the coverage of other topics, such as impulse response analysis, Granger causality tests, and volatility models. The book provides many examples and applications using real-world data and software packages, such as EViews, RATS, and SAS.
applied econometric time series 3rd pdf 125
The book has received positive reviews from many readers and instructors. Some of the strengths of the book are its clear and concise writing style, its comprehensive and up-to-date coverage of the literature, its balance between theory and practice, and its pedagogical features, such as end-of-chapter exercises, problems, and complements. Some of the weaknesses of the book are its occasional typos and errors, its lack of mathematical rigor and proofs, and its omission of some important topics, such as frequency domain analysis, spectral methods, and multivariate time series models.
Section 2: Basic Concepts and Tools of Time Series Analysis
Time series data are observations of a variable or variables over time, such as GDP, inflation, stock prices, or exchange rates. Time series analysis is the study of the properties and behavior of time series data, such as their patterns, trends, cycles, and relationships. Time series analysis is useful for understanding the past, predicting the future, and testing hypotheses about the causes and effects of economic phenomena.
One of the fundamental concepts in time series analysis is stationarity. A time series is said to be stationary if its statistical properties, such as its mean, variance, and autocorrelation, do not change over time. Stationarity is important because many of the methods and results in time series analysis are based on the assumption of stationarity. If a time series is not stationary, it may need to be transformed or differenced to make it stationary.
Another important concept in time series analysis is autocorrelation. Autocorrelation measures the degree of dependence or correlation between a time series and its own past values. Autocorrelation can be positive or negative, and it can decay or persist over time. Autocorrelation can reveal the dynamics and structure of a time series, and it can also affect the estimation and inference of econometric models.
Section 3: Unit Roots and Cointegration
Many economic time series are nonstationary, meaning that their statistical properties change over time. One common source of nonstationarity is a unit root, which means that a time series has a stochastic trend or a random walk component. A unit root can cause problems for econometric analysis, such as spurious regression, invalid inference, and poor forecasting. Therefore, it is important to test whether a time series has a unit root or not, and to remove the unit root if necessary.
There are various methods and tests for detecting and removing unit roots in time series data. Some of the most widely used tests are the Dickey-Fuller (DF) test, the augmented Dickey-Fuller (ADF) test, the Phillips-Perron (PP) test, and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test. These tests are based on different assumptions and have different advantages and disadvantages. The book explains how to perform and interpret these tests using examples and software packages.
Another important concept related to nonstationary time series is cointegration. Cointegration means that two or more nonstationary time series have a long-run equilibrium relationship or a common stochastic trend. Cointegration can imply that there is a causal or meaningful relationship between the variables, and that there are opportunities for arbitrage or error correction. Therefore, it is useful to test whether a set of time series are cointegrated or not, and to estimate the cointegrating equation or vector if they are.
Section 4: Vector Autoregression (VAR) Model
A vector autoregression (VAR) model is a general and flexible framework for modeling and analyzing multivariate time series data. A VAR model consists of a system of equations, where each equation describes how a variable depends on its own past values and the past values of other variables. A VAR model can capture the dynamic interactions and feedback effects among the variables, and it can also accommodate various extensions and modifications, such as exogenous variables, deterministic trends, cointegration, and structural identification.
The book explains how to specify, estimate, and evaluate a VAR model using examples and software packages. The book also discusses some of the issues and challenges involved in VAR modeling, such as choosing the optimal lag length, testing for stationarity and cointegration, imposing restrictions and identifying structures, and dealing with overparameterization and multicollinearity.