I started by Time Series Analysis by Hamilton, but I am lost hopelessly. This book is really too theoretical for me to learn by myself.
Does anybody have a recommendation for a textbook on time series analysis that's suitable for self-study?
I started by Time Series Analysis by Hamilton, but I am lost hopelessly. This book is really too theoretical for me to learn by myself.
Does anybody have a recommendation for a textbook on time series analysis that's suitable for self-study?
I would recommed the following books:
I hope it helps you. Best of luck!
Forecasting: principles and practice by Rob J Hyndman and George Athanasopoulos is available free online: http://otexts.com/fpp/
It's a good book in its own right; Hyndman's previous forecasting book with Makridakis and Wheelright is highly regarded, but this has the added advantage that you can see what you're getting for the price.
There are three books that I keep referring to always from an R
programming and time series analysis perspective:
The first book by Shumway and Stoffer has an open source (abridged) version available online called EZgreen version.
If you are specifically looking into time series forecasting, I would recommend following books:
In my opinion, books 1, 4 and 5 are some of the best of the best books. Many like Forecasting Principles and Practice by Hyndman and Athanasopoulos because it's open source and has R
codes. It is no way closer to the breadth, the depth of coverage of forecasting methods and the writing style of it predecessor Makridakis et al.. Below are some contrasting features on why I like the Makridakis et al:
Forecasting is simply not running univariate methods like arima and exponential smoothing and producing outputs. It is much more than that, and especially strategic forecasting when you are looking into longer horizon. Principles of forecasting by Armstrong goes beyond the univariate extrapolation methods and is highly recommended for anyone who does real world forecasting especially strategic forecasting.
It depends on how much math you want. For a less mathematically-intense treatment, Applied Econometric Time Series by Enders is well-regarded.
Part Four of Damodar Gujarati and Dawn Porter's Basic Econometrics (5th ed) contains five chapters on time-series econometrics - a very popular book! It contains lots of exercises, regression outputs, interpretations, and best of all, you can download the data from the book's website and replicate the results for yourself. Another good book is Stock and Watson's Introduction to Econometrics.
Starting with Hamilton was admirable, but I'd say read through both of the time-series sections in the two books that I just mentioned and then move on to something like Walter Enders' Applied Econometric Time Series or Terrence C Mill's The Modelling of Financial Time Series.
After this (and probably after some review of mathematical economics) then you should be able to sit down and read Hamilton comfortably.
Note: Box & Jenkins' 1970 classic Time series analysis: Forecasting and control is obviously more concentrated (i.e. narrower in content) than the "modern textbooks" that I mentioned, but I'd say that anyone who wants to get a real good understanding of time-series shouldn't leave this off their reading list.
In addition to the other text there are two books introductory books in Springer's Use R! series that cover time series:
Introductory Time Series with R
and
Applied Econometrics in R
There is also an advanced econometrics text in the series, Analysis of Integrated and Co-integrated Time Series with R.
I have not used these but have found several others in the series to be excellent.
There are some good, free, online resources:
If you find Hamilton too difficult then there is Introduction to Econometric Modeling Princeton Uni Press by Bent Nielsen and David Hendry. It focuses more on intuition and practical how-tos than deeper theory. So if you're on a time constraint then that would be a good approach.
I would still recommend to persevere with Time Series Analysis by Hamilton. It is very deep mathematically and the first four chapters will keep you going for a long time and serve as a very strong introduction to the topic. It also covers Granger non-causality and cointegration and if you decide to pursue this topic more deeply then it is in invaluable resource.
For a more intuitive treatment of cointegration, I would also recommend Cointegration, Causality, and Forecasting by Engle and White.
Finally for very advanced treatments, there is Soren Johansen's book "Likelihood-Based Inference in Cointegrated VARs" and of course David Hendry's "Dynamic Econometrics".
Among those two, I would think Hendry's is more big-picture oriented and Johansen is pretty hard-going on the math.
Time Series Analysis: Univariate and Multivariate Methods by William Wei and David P. Reilly - is a very good book on time series and quite inexepnsive. There is am updated version but at a much higher price. It does not include R examples. It explicitely includes a great discussion/presentation of Intervention Detection procedures which are ignored in simplified solutions/introductory textbooks.
There's the NBER Summer Institute "What's New in Time Series Econometrics" (not sure whether this material is gated or not). There are videos with accompanying slides. The lectures are given by a pair of professors (Stock and Watson) who are known for their popular undergraduate econometrics textbook.
In my opinion, you really can't beat Forecasting: principles and practice. It's written by CV's own Rob Hyndman and George Athanasopoulos, it's available for free online, and it's got tons of example code in R, making use of the excellent forecast package.
If you use Stata, Introduction to Time Series Using Stata by Sean Becketti is a solid gentle introduction, with many examples and an emphasis on intuition over theory. I think this book would complement Ender rather well.
The book opens with an intro to Stata language, followed by a quick review of regression and hypothesis testing.
The time series part starts with moving-average and Holt–Winters techniques to smooth and forecast the data. The next section focuses on using these for techniques forecasting. These methods are often neglected, but they work rather well for automated forecasting and are easy to explain. Becketti explains when they will work and when they won't.
The next chapters cover single-equation time-series models like autocorrelated disturbances, ARIMA, and ARCH/GARCH modeling.
In the end, Becketti discusses multiple-equation models, particularly VARs and VECs, and non-stationary time series.
There are a few books that might be useful. If you are mathematically challenged you might want to start with two SAGE books by Mcdowall, Mcleary, Meidinger and Hay called "Interrupted Time Series Analysis" 1980 OR "Applied Time Series Analysis" by Richard McLeary. As you learn more about time series and decide that you you want more than prose and that you are willing to suffer through some math the Wei text published by Addison-Wessley entitled "Time Series Analysis" would be an excellent choice. In terms of web-based educational material, I have written a lot of useful material which can be viewed at http://www.autobox.com/cms/index.php/afs-university/intro-to-forecasting entitled "Introduction to Forecasting".
HILL GRIFFITHS LIM 2011 "Principles of Econometrics" 4E Wiley
Advantages:
(1) Very easy to follow. Topics are well presented. Even though I did not take any econometric course in my life, I easily grasped introductory econometrics with the book.
(2) There are supplemantary books to understand HILL's book:
a. Using EViews for Principles of Econometrics
b. Using Excel for Principles of Econometrics
c. Using Gretl for Principles of Econometrics
d. Using Stata for Principles of Econometrics
Disadvantages:
(1) There is no "Using R for Principles of Econometrics"!
R is industry standard. R is better than Python. Maths in mind can be best reflected to code via R (I am saying this as a person who wrote VBA modules in Excel, wrote Gretl codes, wrote Eviews codes).
I self-started Econometrics with "GREENE 2011 Econometric Analysis - W.H. GREENE 7E PearsonPrentice Hall" This is also nice, but more theoretical; may be difficult for starters.
In summary, I strongly recommend grasping Econometrics with Hill's book, and apply that understanding via another Econometry book that is based on R.