Empirical Vector Autoregressive Modeling:
1. 1 Integrating results The empirical study of macroeconomic time series is interesting. It is also difficult and not immediately rewarding. Many statistical and economic issues are involved. The main problems is that these issues are so interrelated that it does not seem sensible to address them o...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berlin, Heidelberg
Springer Berlin Heidelberg
1994
|
| Ausgabe: | 1st ed. 1994 |
| Schriftenreihe: | Lecture Notes in Economics and Mathematical Systems
407 |
| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | 1. 1 Integrating results The empirical study of macroeconomic time series is interesting. It is also difficult and not immediately rewarding. Many statistical and economic issues are involved. The main problems is that these issues are so interrelated that it does not seem sensible to address them one at a time. As soon as one sets about the making of a model of macroeconomic time series one has to choose which problems one will try to tackle oneself and which problems one will leave unresolved or to be solved by others. From a theoretic point of view it can be fruitful to concentrate oneself on only one problem. If one follows this strategy in empirical application one runs a serious risk of making a seemingly interesting model, that is just a corollary of some important mistake in the handling of other problems. Two well known examples of statistical artifacts are the finding of Kuznets "pseudo-waves" of about 20 years in economic activity (Sargent (1979, p. 248)) and the "spurious regression" of macroeconomic time series described in Granger and Newbold (1986, §6. 4). The easiest way to get away with possible mistakes is to admit they may be there in the first place, but that time constraints and unfamiliarity with the solution do not allow the researcher to do something about them. This can be a viable argument |
| Beschreibung: | 1 Online-Ressource (XIII, 382 p) |
| ISBN: | 9783642487927 |
| DOI: | 10.1007/978-3-642-48792-7 |
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| 520 | |a 1. 1 Integrating results The empirical study of macroeconomic time series is interesting. It is also difficult and not immediately rewarding. Many statistical and economic issues are involved. The main problems is that these issues are so interrelated that it does not seem sensible to address them one at a time. As soon as one sets about the making of a model of macroeconomic time series one has to choose which problems one will try to tackle oneself and which problems one will leave unresolved or to be solved by others. From a theoretic point of view it can be fruitful to concentrate oneself on only one problem. If one follows this strategy in empirical application one runs a serious risk of making a seemingly interesting model, that is just a corollary of some important mistake in the handling of other problems. Two well known examples of statistical artifacts are the finding of Kuznets "pseudo-waves" of about 20 years in economic activity (Sargent (1979, p. 248)) and the "spurious regression" of macroeconomic time series described in Granger and Newbold (1986, §6. 4). The easiest way to get away with possible mistakes is to admit they may be there in the first place, but that time constraints and unfamiliarity with the solution do not allow the researcher to do something about them. This can be a viable argument | ||
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Datensatz im Suchindex
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| spelling | Ooms, Marius Verfasser aut Empirical Vector Autoregressive Modeling by Marius Ooms 1st ed. 1994 Berlin, Heidelberg Springer Berlin Heidelberg 1994 1 Online-Ressource (XIII, 382 p) txt rdacontent c rdamedia cr rdacarrier Lecture Notes in Economics and Mathematical Systems 407 1. 1 Integrating results The empirical study of macroeconomic time series is interesting. It is also difficult and not immediately rewarding. Many statistical and economic issues are involved. The main problems is that these issues are so interrelated that it does not seem sensible to address them one at a time. As soon as one sets about the making of a model of macroeconomic time series one has to choose which problems one will try to tackle oneself and which problems one will leave unresolved or to be solved by others. From a theoretic point of view it can be fruitful to concentrate oneself on only one problem. If one follows this strategy in empirical application one runs a serious risk of making a seemingly interesting model, that is just a corollary of some important mistake in the handling of other problems. Two well known examples of statistical artifacts are the finding of Kuznets "pseudo-waves" of about 20 years in economic activity (Sargent (1979, p. 248)) and the "spurious regression" of macroeconomic time series described in Granger and Newbold (1986, §6. 4). The easiest way to get away with possible mistakes is to admit they may be there in the first place, but that time constraints and unfamiliarity with the solution do not allow the researcher to do something about them. This can be a viable argument Economic Theory/Quantitative Economics/Mathematical Methods Statistics for Business, Management, Economics, Finance, Insurance Economic theory Statistics Ökonometrisches Modell (DE-588)4043212-9 gnd rswk-swf Vektor-autoregressives Modell (DE-588)4288533-4 gnd rswk-swf Vektor (DE-588)4202708-1 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Ökonometrisches Modell (DE-588)4043212-9 s DE-604 Vektor (DE-588)4202708-1 s Vektor-autoregressives Modell (DE-588)4288533-4 s Erscheint auch als Druck-Ausgabe 9783540577072 Erscheint auch als Druck-Ausgabe 9783642487934 https://doi.org/10.1007/978-3-642-48792-7 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Ooms, Marius Empirical Vector Autoregressive Modeling Economic Theory/Quantitative Economics/Mathematical Methods Statistics for Business, Management, Economics, Finance, Insurance Economic theory Statistics Ökonometrisches Modell (DE-588)4043212-9 gnd Vektor-autoregressives Modell (DE-588)4288533-4 gnd Vektor (DE-588)4202708-1 gnd |
| subject_GND | (DE-588)4043212-9 (DE-588)4288533-4 (DE-588)4202708-1 (DE-588)4113937-9 |
| title | Empirical Vector Autoregressive Modeling |
| title_auth | Empirical Vector Autoregressive Modeling |
| title_exact_search | Empirical Vector Autoregressive Modeling |
| title_exact_search_txtP | Empirical Vector Autoregressive Modeling |
| title_full | Empirical Vector Autoregressive Modeling by Marius Ooms |
| title_fullStr | Empirical Vector Autoregressive Modeling by Marius Ooms |
| title_full_unstemmed | Empirical Vector Autoregressive Modeling by Marius Ooms |
| title_short | Empirical Vector Autoregressive Modeling |
| title_sort | empirical vector autoregressive modeling |
| topic | Economic Theory/Quantitative Economics/Mathematical Methods Statistics for Business, Management, Economics, Finance, Insurance Economic theory Statistics Ökonometrisches Modell (DE-588)4043212-9 gnd Vektor-autoregressives Modell (DE-588)4288533-4 gnd Vektor (DE-588)4202708-1 gnd |
| topic_facet | Economic Theory/Quantitative Economics/Mathematical Methods Statistics for Business, Management, Economics, Finance, Insurance Economic theory Statistics Ökonometrisches Modell Vektor-autoregressives Modell Vektor Hochschulschrift |
| url | https://doi.org/10.1007/978-3-642-48792-7 |
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