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The system perspective

A perspective which is important to convey in the teaching of econometrics is the system perspective. Real world economic variables are part of large, complex and dynamic systems. Even in the cases where the research question can be answered
by the use of single equation modelling, system thinking is usually needed in econometrics (eg validity of conditioning, and validity and relevance of instruments).

When the modelling purpose is to construct a usable macroeconometric model, the system perspective, and multiple equation modelling, becomes more evident. Stability of economic equilibria, and stationarity of variables are phenomena that are system properties.

Hence, an attempt has been made in this book to use the system
perspective as a red thread, starting with two simple models from economic dynamics in Chapter 1, and ending in Chapter 12, which is about model based forecasting.

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About the book

A concise presentation on the mathematics of difference equations and how it is used in dynamic econometric modelling

Methods for non-stationary and co-integrated variables

Structured chapters on automatic methods for variable selection and forecasting with empirical macroeconometric models

Complete with end-of-chapter exercises and solutions

The Ultimate Guide to Theory, Analysis, and Forecasting

Master Economic Modeling:

Dive in, and dig deep into the econometric theory and methodology that underpin empirical macroeconomic model building.

See the implications of the duality between equilibrium concepts in dynamic economics and the stationarity of economic variables, equally important for single-equation models, VARs, recursive systems, and simultaneous equations.

The book also delves into key topics, including:

Exogeneity

Its role in estimation, policy analysis, and forecasting.

Automatic Variable Selection

How computer-based tools enhance the development of empirical macroeconomic models.

Model-Based Economic Forecasting

A unified framework for forecasting in our developing world .

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World Scientific Publishing

World Scientific Publishing, founded in 1981, has grown from a small office in Singapore to a global leader in academic and professional publishing, with offices across major cities worldwide. As the largest international scientific publisher in the Asia-Pacific region, it releases 600 new titles and over 180 journals annually, many used by top institutions like Harvard and Stanford. Known for publishing the Nobel Lectures series and collaborating with Nobel laureates, World Scientific continues to make groundbreaking scientific and literary achievements accessible to a global audience, cementing its reputation as a cornerstone of academic excellence.

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Data sets

The following contains links to data sets for the computer exercises Solution suggestions to those exercises, as well as to the theoretical ones are included in Appendix C of the book.

All linked data files are compressed files and therefore have extension zip. They contain data sets, some times in more than one file format (eg PcGive data bank (also easy to use in Eviews), Stata, and Microsoft Excel). Extract the contents after download of the zipped files and use the desired file format.

The data sets are referred both in the main text and in the practical exercises.

Chapter 1: dgp_determandstoch (cobweb model series), Fulton (New York fish market data)

Chapter 2: NORPCMa (Norwegian inflation and unemployment, estimation of natural rate)

Chapter 3: SimdataAR2 (code to generate 2nd order dynamics)

Chapter 4: Fulton (New York fish market data)

Chapter 5: dgp_determandstoch (cobweb model series), Fulton (New York fish market data)

Chapter 6: NORPCMb (Norwegian inflation and unemployment, ADL), RLandRNB_final_modelCH6 (code for interest rate transmission model in Ch. 4.8), NAMdataMay17 (data set for interest rate transmisson)

Chapter 7: pcnaive_VARbyCond_d (VAR-EX series) , SEMbias_data (simultaneous equations bias), Fulton (New York fish market data)

Chapter 8: NORPCMb

Chapter 9: Simdata_trend_RW (generated non-stationary series)

Chapter 10: ECM-test_SpuriousADLmodel (data for ECM and trace tests of no relationship), weakly_cointegrated_ADLmodel_d (data for CVAR)

Chapter 11: DGPisNullModel_Zvariables_are_correlated_d , DGPisADL_Zvariables_are_correlated_d , NAMdataMay17

R code

In the book, the practical examples and exercises mainly refer to OxMetrics software programs PcGive and PcNaive (for Monte Carlo simulation), but also Eviews. Examples of code in R can be found by following these links:

Exercise 1.7: FultonCH1.zip

Chapter 6.7 (ADL model of interest rate transmission): RLandRNBCH6.zip

The files are in zip format, but they only contain a single R-code file each.

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