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Dynamic Economics: Quantitative Methods and Applications
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Sales rank 39,960
Customers rating (based on 2 reviews)
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This book is an effective, concise text for students and researchers that combines the tools of dynamic programming with numerical techniques and simulation-based econometric methods. Doing so, it bridges the traditional gap between theoretical and empirical research and offers an integrated framework for studying applied problems in macroeconomics and microeconomics. In part I the authors first review the formal theory of dynamic optimization; they then present the numerical tools and econometric techniques necessary to evaluate the theoretical models. In language accessible to a reader with a limited background in econometrics, they explain most of the methods used in applied dynamic research today, from the estimation of probability in a coin flip to a complicated nonlinear stochastic structural model. These econometric techniques provide the final link between the dynamic programming problem and data. Part II is devoted to the application of dynamic programming to specific areas of applied economics, including the study of business cycles, consumption, and investment behavior. In each instance the authors present the specific optimization problem as a dynamic programming problem, characterize the optimal policy functions, estimate the parameters, and use models for policy evaluation. The original contribution of Dynamic Economics: Quantitative Methods and Applications lies in the integrated approach to the empirical application of dynamic optimization programming models. This integration shows that empirical applications actually complement the underlying theory of optimization, while dynamic programming problems provide needed structure for estimation and policy evaluation.
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| Publisher | The MIT Press | | Release date | 10/2003 | | Availability | Usually ships in 24 hours | | Edition | Hardcover |
| | List price | $40 | | Our price | $32 (you save 20.00%) | | Used price | from $30 |
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 nice easy book
this book is nice to have as a compliment to sargent and lindquist. its a nice way to get your feet wet in dynamic programming.
 A good work of synthesis!
This is a perfect book for graduate students and professionals looking to get in touch with contemporary macroeconomic research, but only for those who already learned much economic theory, some dynamic programming, some graduate econometrics and who's aren't strangers to basic computer programming or software like Matlab or Mathematica.
The problem with this slim, synthesis-oriented book is that it's not useful for a first exposure to many of the tools and topics discussed. The first part overviews these, but in a way that will often make you reach for a more fundamental reference/treatment. You'll want to keep Stokey, Lucas & Prescott near (or something equivalent) as well as an introductory text for numerical methods. Sargent and Ljungqvist is referenced for Markov chains although it might be overkill.
I like the way the authors manage to make a balanced and clear case for the underlying methodology: develop dynamic, economic theory, formulate it as a dynamic programming problem, solve using numerical methods, estimate parameters by matching moments or maximum likelihood and then study counterfactuals (e.g. policy evaluation). This forces you to be clear about the sources of volatility/uncertainty in your model, among other virtues.
The exercises (that follow most sections) are enlightening and OK in terms of difficulty but too often I was frustrated by open-ended questions (e.g. "What happens when uncertainty is added?"). I would love to buy a solution manual, but one is unavailable. This is particularly frustrating when the theory section is too short and of little help (e.g. the stopping problem section in part I should have been much more verbose).
All in all, this book deserves 5 stars. It could have been thicker, more encyclopedic, but all the elements are there, to get one productive with these awesome tools, as long as one is willing to grab references. It should also be much better when used in class, as opposed to working through it on one's own.
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