By Ralf Brüggemann (auth.)

ISBN-10: 3540206434

ISBN-13: 9783540206439

ISBN-10: 3642170293

ISBN-13: 9783642170294

1. 1 goal of the examine Vector autoregressive (VAR) versions became one of many dominant examine instruments within the research of macroeconomic time sequence over the past 20 years. the good good fortune of this modeling type begun with Sims' (1980) critique of the normal simultaneous equation types (SEM). Sims criticized using 'too many great regulations' in keeping with 'supposed a priori wisdom' in huge scale macroeconometric types which have been renowned at the moment. as a result, he advo­ cated mostly unrestricted lowered shape multivariate time sequence versions, unrestricted VAR types specifically. Ever due to the fact that his influential paper those types were hired commonly to represent the underlying dynamics in platforms of time sequence. specifically, instruments to summarize the dynamic interplay among the procedure variables, similar to impulse reaction research or forecast mistakes variance decompo­ sitions, were built through the years. The econometrics of VAR versions and similar amounts is now good proven and has discovered its method into numerous textbooks together with inter alia Llitkepohl (1991), Hamilton (1994), Enders (1995), Hendry (1995) and Greene (2002). The unrestricted VAR version presents a common and extremely versatile framework that proved to be precious to summarize the information features of monetary time sequence. regrettably, the pliability of those versions motives critical difficulties: In an unrestricted VAR version, each one variable is expressed as a linear functionality of lagged values of itself and all different variables within the system.

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Additional info for Model Reduction Methods for Vector Autoregressive Processes

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2 ItI Y2 1 . 0 . 5 , , ,,+ Yl

0 , ,, Y2 + ~ ... 0 III = 2 III = 3 ItI = 4 It! " /@ /~ ,/,,4. . 4. Selection vs. 0 5 o. 3. + YI r r r It! 5. 2 r r ItI = 5 = o. 6 o. 2- - - - . . 6. 44), p I tf =fl It I Y3 = o. 5 46 Chapter 2. 7. 43), h = 1 = o. 5 ! 50 i * * J I I ~ i l' l' l' Itl=J 1t1=4 Itl=:;S ~True VAR OFS-AIC EJFS-HQ AFS-SC ~r-AIC . S. Normalized MSEs of impulse responses relative to unrestricted VAR(4), p=o. 3. ~=---------, . ' . 7 SSER-HQ o. - - , , . , - - - . 9. 5 YI, P = 0 YhP /§:,~- /;odfj 'b '. 9. 7 + + <;-/:. ' O.

The difference between the subset strategies FS, TP and TD is usually fairly small. In fact, we often observe that for a given information criterion, FS, TP and TD lead to improvements with the same order of magnitude. e. SC) leads to the largest improvement, especially for large population t-values. 44). Similar to the results in the bivariate case, we find that PcGets consistently leads to the largest improvements in forecasting performance and again provides forecasts of similar precision as the 'true' model.

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Model Reduction Methods for Vector Autoregressive Processes by Ralf Brüggemann (auth.)

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