This book addresses one of the most important research activities in empirical macroeconomics.
It provides a course of advanced but intuitive methods and tools enabling the spatial and temporal disaggregation of basic macroeconomic variables and the assessment of the statistical uncertainty of the outcomes of disaggregation. The empirical analysis focuses mainly on GDP and its growth in the country context of Poland, however, all of the methods discussed can be easily applied to other countries. The approach used in the book, views spatial and temporal disaggregation as a special case of the estimation of missing observations (a topic on missing data analysis). The book presents an econometric course of models of Seemingly Unrelated Regression Equations (SURE). The main advantage of using the SURE specification is to tackle the presented research problem so that it allows for the heterogeneity of the parameters describing relations between macroeconomic indicators. The book contains model specification, as well as descriptions of stochastic assumptions and resulting procedures of estimation and testing. The method also addresses uncertainty in the estimates produced. All of the necessary tests and assumptions are presented in detail. The results will be designed to serve as a source of invaluable information making regional analyses more convenient and - more importantly - comparable. It will create a solid basis for making conclusions and recommendations concerning regional economic policy in Poland, particularly regarding the assessment of the economic situation.
This is essential reading for academics, researchers and economists with regional analysis as their field of expertise, as well as, central bankers and policymakers.
List of Figures
List of Tables
2. Importance of regional data for policy evaluation
3. A review of official statistics describing economic conditions in NUTS2 regions in Poland
4. Basic properties of the model of Seemingly Unrelated Regression Equations
4.1. A brief look at estimation and testing within the frameworks of simple and generalised linear regression
4.2. Seemingly Unrelated Regression Equations as an example of generalised linear regression
5. NUTS2 disaggregation of the Polish GDP – preliminary analyses within SUREdiag
5.1. Basic model setting
5.2. Empirical results
5.4. Tables and Figures
6. NUTS2 disaggregation of the Polish GDP – including other explanatory variables
6.1. NUTS2 disaggregation of the Polish GDP - analyses within a simple regression framework
6.1.1. Basic model setting
6.1.2. Empirical results
6.1.3. Tables and Figures
6.2. NUTS2 disaggregation of the Polish GDP - analyses within the unconstrained SURE model
6.2.1. Basic model setting
6.2.2. Discussion of empirical results
6.2.3. Tables containing estimation results
6.2.4. Tables containing results obtained in case of model M0, i.e. SUREdiag
6.2.5. Tables containing results obtained in case of model M1, i.e. unconstrained SURE model
7. Concluding remarks
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