<Root level> Textbook Examples |
The RATS distribution includes worked examples from many important textbooks across a broad range of time series and econometrics topics. Each book has a separate directory (with programs, data and any additional required procedures). See the appropriate "Where to Find It" (Windows or Macintosh) to see where those are located.
Most of the programs have names which include the page number at which the example starts: for instance, balt3p032.rpf is Baltagi, 3rd edition, p 32. (For some books, we also have examples from older editions on our web site).
The links below are to the textbook browser on our web site. This lists all the examples in the book with descriptions and the level of RATS coding required. Note that, while you can easily download the RATS program from that, the copy on your installed copy of RATS includes the data as well so you can run it directly. The best use of the textbook browser is to find examples which demonstrate the type of analysis that you want. (You can easily expand your search to cover all the textbooks by picking "Any" in the Textbook list and either the Topic or RATS Features. Then hit the Reset List button).
Baltagi (2002), Econometrics, 3rd Edition, Springer-Verlag.
This is intended for a first year graduate course in econometrics. It covers a broad range of topics with examples taken from the literature. The RATS examples tend to be fairly straightforward.
Baltagi (2008), Econometric Analysis of Panel Data, 4th ed, Wiley.
This is one of the leading textbooks for postgraduate courses in panel data. It includes recent empirical examples from the literature. It examines dynamic panel data models, non-linear panel models, and limited dependent variables panel data models.
Brockwell and Davis(2002), Introduction to Time Series and Forecasting, 2nd ed, Springer-Verlag.
This is an introductory time series book written by a pair of statisticians, so there are slight differences in presentation from what is commonly used in business/economics books of a similar type. For instance, vector autoregressions are estimated using Yule-Walker equations, which enforces stationarity, while econometricians generally use OLS which doesn't. Most of the examples are fairly short, demonstrating one operation at a time.
Commandeur and Koopman(2007), Introduction to State Space Time Series Analysis, Oxford University Press.
This is an introductory book to State Space Models. (The Durbin-Koopman book is a more advanced treatment).
DeLurgio(1998), Forecasting Principles and Applications, Irwin/McGraw-Hill.
This is an introductory time series/forecasting book which has has more examples of exponential smoothing, transfer function and intervention modelling than similar books.
Diebold(2004), Elements of Forecasting, 3rd ed, South-Western.
This is a survey of modern business and economics forecasting methods. It starts with a substantial section on designing readable graphics for time series data.
Dougherty(2011), Introduction to Econometrics, 4th ed, Oxford University Press.
This is an introductory book with a general emphasis on cross section data. It's not clear whether it was intended that the students be able to do this, but we've included the programs for doing the Monte Carlo experiments in the book. None of these are particularly difficult (they're listed as "Intermediate" difficulty since they use some programming skills not needed by the other examples) and can serve as an introduction to simulation methods in RATS.
Durbin and Koopman(2012), Time Series Analysis by State Space Methods, 2nd ed, Oxford University Press.
This is an advanced book on State Space Models. (The Commandeur-Koopman book has a more basic treatment). In addition to the standard linear models, it includes examples of the non-linear/extended Kalman filter.
Enders(2015), Applied Econometric Time Series, 4th ed, Wiley.
This provides an introduction to and discussion of most of the key topics in modern time series econometrics, including: ARIMA models, GARCH models, threshold models, VAR's, Cointegration and Error Correction models. It's written at the advanced undergraduate/introductory graduate level.
Franses, van Dijk and Opschoor, Times Series Analysis for Business and Economic Forecasting, 2nd ed, Cambridge University Press.
This is an introductory book on time series models. Compared with other books aimed at a similar audience, it has a greater emphasis on seasonality, and on “aberrant observations” (outliers and breaks)—the central chapters of the book are on trends, seasonals and aberrant observations, and how they should be handled. The final roughly 1/3 of the book includes GARCH models, non-linear models (mainly TAR and STAR) and VAR’s and VECM's.
Greene(2012), Econometric Analysis, 7th ed, Prentice Hall.
This is a graduate econometrics textbook with an emphasis on non-linear models, cross section and panel data. We used Greene's examples as the basis for quite a few of our RATS examples.
Gujarati(2003), Basic Econometrics, 4th ed, McGraw-Hill.
This is an introduction to applied econometrics, covering a wide range of topics. The examples tend to be relatively straightforward.
Hamilton(1994), Time Series Analysis, Princeton University Press.
This is a detailed, in-depth treatment of modern time series analysis and econometrics that can serve both as a textbook for the graduate student and an advanced reference for practicing researchers.
Harvey(1990), Forecasting, structural time series models and the Kalman filter, Cambridge University Press.
This deals primarily with State Space Models, particularly what Harvey calls "Basic Structural Models" which are combinations of standard unobservable components.
Hayashi(2000), Econometrics, Princeton University Press.
This is a graduate level text which puts a heavy emphasis on asymptotics using modern central limit theorems (allowing for heteroscedasticity and autocorrelation).
Hill, Griffiths and Lim(2008), Principles of Econometrics, 3rd ed, Wiley.
This is an introductory book for undergraduate students in economics and finance, as well as first-year graduate students in a variety of fields. It covers a broad range of topics with the RATS programs being relatively straightforward.
Johnston and DiNardo(1996), Econometric Methods, Princeton University Press.
This is an introductory graduate/advanced undergraduate text which does a substantially higher proportion of its examples with simulated data or by using simulation or bootstrapping for analysis than books aimed at the same market.
Kim and Nelson(1998), State-space Models with Regime Switching, MIT Press.
This works through State Space Models and then Markov Switching Models to finally analyze Markov Switching State-Space Models, which can be analyzed with the "Kim" filter. These are rather complicated models.
Koop(2003), Bayesian Econometrics, Wiley.
This is a wide ranging book on Bayesian techniques, starting with models which can be handled analytically, then moving to models which require simulation techniques such as Gibbs sampling and Metropolis-Hastings. Much of the contents of the RATS Bayesian Econometrics e-course is based upon this book.
Lutkepohl(2006), New Introduction to Multiple Time Series Analysis, Springer.
Most of the examples here are for various aspects of VAR analysis. Many of the examples do diagnostics of various types on a VAR with multivariate tests for normality, autocorrelation and stability.
Makridakis, Wheelwright and Hyndman, Forecasting Methods, 3rd, Wiley.
This is a text on forecasting methods covering seasonal adjustment, exponential smoothing and Box-Jenkins models. The examples tend to be relatively straightforward.
Martin, Hurn and Harris, Econometric Modelling with Time Series: Specification, Estimation and Testing, Cambridge University Press.
This is a book which covers a wide range of subjects, and includes some often quite sophisticated models taken from the literature. While the econometrics may often not be simple, in many cases, the RATS code itself is fairly straightforward, by making use of procedures like @SHORTANDLONG and @ADFAUTOSELECT.
Novales, Fernandez and Ruiz(2009), Economic Growth: Theory and Numerical Solution Methods, Springer-Verlag.
This studies economic growth using small macroeconomic models by looking at changes to exogenous processes and/or random number simulation. The RATS examples all use DSGE for solving the models.
Pindyck and Rubinfeld, Econometric Models and Economic Forecasts, 4th ed, Irwin-McGraw Hill.
This is an introductory econometrics book emphasizing models which can be used for forecasting. The examples are generally relatively simple.
Stock and Watson(2018), Introduction to Econometrics, 4th ed, Pearson.
This is an introductory book which works deeply through a relatively small number of practical examples.
Studenmund(2011), Using Econometrics: A Practical Guide, 6th ed, Addison-Wesley.
This is an introductory undergraduate text based primarily on linear regression. The examples are almost entirely done with LINREG and a few procedures, with AR1, PREGRESS and DDV worked into some of the examples from later chapters.
Tsay(2010), Analysis of Financial Time Series, 3rd, Wiley.
This is a graduate level text on time series analysis with a special emphasis on computations needed in finance. For instance, there are several examples which compute the Value-at-Risk (VaR) using different methods. This also has quite a few "non-standard" GARCH models that require estimation by MAXIMIZE, though it also uses more standard GARCH models as well. Several other areas not generally covered in textbooks are threshold models, and Gibbs sampling.
Verbeek(2008), A Guide to Modern Econometrics, 3rd, Wiley.
This is intended to fill a gap between introductory and advanced econometrics books, providing intuition behind some of the more modern techniques without the formal proofs that would be included in the advanced books. It has a broader range of topics than would be typical.
West and Harrison(1997), Bayesian Forecasting and Dynamic Models, Springer.
This analyzes State Space Models with an emphasis on their use for out-of-sample forecasting.
Wooldridge(2009), Introductory Econometrics, 4th ed, Southwestern.
The main emphasis in the book is on choosing the specification for a linear regression model and interpreting the results, so many of the examples are a DATA instruction, perhaps a few SET instructions for transformations, and one or two LINREG instructions for doing the regressions. Those examples won't make much sense unless you're following along in the book, since the main point is the discussion in the book about why these particular regressions are being chosen.
Wooldridge(2010), Econometric Analysis of Cross Section and Panel Data, 2nd ed, MIT Press.
This is the second edition of Wooldridge's highly acclaimed book, which is intended as a second semester graduate text. It examines the special problems that the econometrician must face in applying linear regression, instrumental variables/GMM and SUR estimators to cross section and panel data. It then covers a wide range of non-linear models: probit, logit, censoring and sample selection, count data and duration models. This includes almost all techniques covered in the panel data chapter of the RATS User's Guide, plus many more.
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