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Book Categories |
| 1 | Time series analysis and simultaneous equation econometric models (1974) | 3 |
| 2 | Statistical analysis of econometric models (1979) | 44 |
| Comment (1979) | 79 | |
| Comment (1979) | 82 | |
| Comment (1979) | 84 | |
| Comment (1979) | 87 | |
| Rejoinder (1979) | 91 | |
| 3 | Structural econometric modeling and time series analysis : an integrated approach (1983) | 96 |
| Comment (1983) | 165 | |
| Comment (1983) | 169 | |
| Response to the discussions (1983) | 172 | |
| 4 | Time series analysis, forecasting, and econometric modeling : the structural econometric modeling, time series analysis (SEMTSA) approach (1994) | 175 |
| 5 | Large-sample estimation and testing procedures for dynamic equation systems (1980) | 201 |
| Rejoinder (1981) | 233 | |
| 6 | Time series and structural analysis of monetary models of the US economy (1975) | 243 |
| 7 | Time series versus structural models : a case study of Canadian manufacturing inventory behavior (1975) | 288 |
| 8 | Time series analysis of the German hyperinflation (1978) | 315 |
| 9 | A time series analysis of seasonality in econometric models (1978) | 332 |
| Comment (1978) | 382 | |
| Comment and implications for policy-makers and model builders (1978) | 388 | |
| Response to discussants (1978) | 394 | |
| 10 | The behavior of speculative prices and the consistency of economic models (1985) | 397 |
| 11 | A comparison of the stochastic processes of structural and time series exchange rate models (1987) | 405 |
| 12 | Encompassing univariate models in multivariate time series : a case study (1994) | 418 |
| 13 | Macroeconomic forecasting using pooled international data (1987) | 457 |
| 14 | Forecasting international growth rates using Bayesian shrinkage and other procedures (1989) | 485 |
| 15 | Turning points in economic time series, loss structures, and Bayesian forecasting (1990) | 506 |
| 16 | Forecasting turning points in international output growth rates using Bayesian exponentially weighted autoregression, time-varying parameter, and pooling techniques (1991) | 528 |
| 17 | Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates (1993) | 559 |
| 18 | Pooling in dynamic panel data models : an application to forecasting GDP growth rates (2000) | 590 |
| 19 | Forecasting turning points in countries' output growth rates : a response to Milton Friedman (1999) | 612 |
| 20 | Using Bayesian techniques for data pooling in regional payroll forecasting (1990) | 619 |
| 21 | Forecasting turing points in metropolitan employment growth rates using Bayesian techniques (1990) | 637 |
| 22 | A note on aggregation, disaggregation, and forecasting performance (2000) | 656 |
| 23 | The Marshallian macroeconomic model (2000) | 667 |
| 24 | Bayesian modeling of economies and data requirements (2000) | 677 |
| Price | Condition | Delivery | Seller | Action |
| $260.00 | Digital |
| WonderClub |
Brian Roland
reviewed Structural Econometric Time Series Analysis Approach on September 17, 2011
Structural Econometric Time Series Analysis Approach
I have a bachelor degree in International Studies and almost have a masters degree in International Economics, both entailed subjects about statistical analysis, yet here was I still unable to do a simple research. As I felt ashamed by my ignorance I decided to pick up this book at the publisher's sale for a great price. Best decision I made in a long time.
If you meticulously go through the book you will understand the relationship between two variables (correlation) and more than two variables (regression). The book mainly concerns these two models and their intricacies, with an emphasis on regression and time-series data. The main catch the author offers is that you don't need deep mathematical knowledge to either do or understand these kinds of operations, it can be understood logically or by common sense. He mostly delivers on this promise (sometimes the lack of maths make some explanations confusing, but this is the price to pay) so anyone can pick this up and understand it.
The explanations are clear, the exercises are on par with your understanding and help to prove the underlying concepts. The whole arch of the book is so that you gradually, without even noticing, start doing much more lifelike and interesting problems, building on the previously learnt knowledge.
If you ever need to do some research with data and don't know how this book is a great help and you don't need to stress yourself with the difficult math that makes it all work if you don't want to (although the author admits that in order to achieve full understanding, and some difficult problems will require you to pick up more comprehensive books).
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