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020 _a9783642487927
_9978-3-642-48792-7
024 7 _a10.1007/978-3-642-48792-7
_2doi
050 4 _aHB1-846.8
072 7 _aKCA
_2bicssc
072 7 _aBUS069030
_2bisacsh
072 7 _aKCA
_2thema
082 0 4 _a330.1
100 1 _aOoms, Marius.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aEmpirical Vector Autoregressive Modeling
_h[electronic resource] /
_cby Marius Ooms.
250 _a1st ed. 1994.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c1994.
300 _aXIII, 382 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Economics and Mathematical Systems,
_x0075-8442 ;
_v407
505 0 _a1 Introduction -- 1.1 Integrating results -- 1.2 Goal of the study -- 1.3 Data and measurement model -- 1.4 Baseline model and methodology -- 1.5 Outline of the study -- 1.6 What is new? -- 2 The Unrestricted VAR and its components -- 2.1 Introduction -- 2.2 The model -- 2.3 Univariate processes and unit roots -- 2.4 Integrated processes -- 2.5 Alternative models for nonstationarity, long memory and persistence -- Appendix A2.1 MA representation integrated process -- Appendix A2.2 Univariate testing for unit root nonstationarity -- 3 Data Analysis by Vector Autoregression -- 3.1 Introduction -- 3.2 Data-oriented measures of influence -- 3.3 Diagnostic checking -- Appendix A3.1 Influence measures for the normal linear model -- Appendix A3.2 Influence measures for the multivariate general linear model -- Appendix A3.3 Influence measures in principal component analysis -- 4 Seasonality -- 4.1 Introduction -- 4.2 Application of the idea of unobserved components -- 4.3 Application of linear filters to estimate unobserved components -- 4.4 Data analysis of the seasonal component -- 4.5 Application of the Census X-11 filter in a VAR -- Appendix 4.1 Trigonometric seasonal processes in regression -- Appendix 4.2 Backforecasts and deterministic changes in mean -- 5 Outliers -- 5.1 Introduction -- 5.2 The outlier model -- 5.3 Some effects of outliers on VAR estimates -- 5.4 Derivation of the LM-statistics -- 5.5 An artificial example -- 5.6 Application to macroeconomic series -- 5.7 Two simple ways to study the influence of outliers -- Appendix 5.1 Some proofs concerning outlier test statistics -- Appendix 5.2 Subsample analysis outlier influence -- Appendix 5.3 Robust estimation by extraction of additive outliers -- 6 Restrictions on the VAR -- 6.1 Introduction -- 6.2 Cointegration, the number of unit roots, and common trends -- 6.3 Straightforward transformation formulae -- 6.4 Trend stationary processes and quadratic trends -- 6.5 Estimating pushing trends and pulling equilibria -- 6.6 Multivariate tests for unit roots -- Appendix 6.1 Computation and distribution multivariate unit root test statistics -- 7 Applied VAR Analysis for Aggregate Investment -- 7.1 Introduction -- 7.2 The variable of interest and some of its supposed relationships -- 7.3 Measurement model -- 7.4 Univariate analysis -- 7.5 Multivariate analysis -- Appendix 7.1 Data sources and construction -- Appendix 7.2 Results of final VECM model -- Appendix 7.3 Open economy stochastic dynamic general equilibrium models -- Summary -- References -- Name index.
520 _a1. 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.
650 0 _aEconomic theory.
650 0 _aStatistics .
650 1 4 _aEconomic Theory/Quantitative Economics/Mathematical Methods.
_0https://scigraph.springernature.com/ontologies/product-market-codes/W29000
650 2 4 _aStatistics for Business, Management, Economics, Finance, Insurance.
_0https://scigraph.springernature.com/ontologies/product-market-codes/S17010
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540577072
776 0 8 _iPrinted edition:
_z9783642487934
830 0 _aLecture Notes in Economics and Mathematical Systems,
_x0075-8442 ;
_v407
856 4 0 _uhttps://s443-doi-org.br.lsproxy.net/10.1007/978-3-642-48792-7
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