000			03454nam a22003615i 4500
001			294252
003			MX-SnUAN
005			20160429155139.0
007			cr nn 008mamaa
008			150903s2005 gw | o |||| 0|eng d
020			_a9783540277521 _99783540277521
024	7		_a10.1007/9783540277521 _2doi
035			_avtls000347034
039		9	_a201509030918 _bVLOAD _c201405050337 _dVLOAD _y201402070922 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aHB139-141
100	1		_aLütkepohl, Helmut. _eautor _9327639
245	1	0	_aNew Introduction to Multiple Time Series Analysis / _cby Helmut Lütkepohl.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2005.
300			_axxI, 764 páginas _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
505	0		_aFinite Order Vector Autoregressive Processes -- Stable Vector Autoregressive Processes -- Estimation of Vector Autoregressive Processes -- VAR Order Selection and Checking the Model Adequacy -- VAR Processes with Parameter Constraints -- Cointegrated Processes -- Vector Error Correction Models -- Estimation of Vector Error Correction Models -- Specification of VECMs -- Structural and Conditional Models -- Structural VARs and VECMs -- Systems of Dynamic Simultaneous Equations -- Infinite Order Vector Autoregressive Processes -- Vector Autoregressive Moving Average Processes -- Estimation of VARMA Models -- Specification and Checking the Adequacy of VARMA Models -- Cointegrated VARMA Processes -- Fitting Finite Order VAR Models to Infinite Order Processes -- Time Series Topics -- Multivariate ARCH and GARCH Models -- Periodic VAR Processes and Intervention Models -- State Space Models.
520			_aThis reference work and graduate level textbook considers a wide range of models and methods for analyzing and forecasting multiple time series. The models covered include vector autoregressive, cointegrated, vector autoregressive moving average, multivariate ARCH and periodic processes as well as dynamic simultaneous equations and state space models. Least squares, maximum likelihood, and Bayesian methods are considered for estimating these models. Different procedures for model selection and model specification are treated and a wide range of tests and criteria for model checking are introduced. Causality analysis, impulse response analysis and innovation accounting are presented as tools for structural analysis. The book is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it. Applied researchers involved in analyzing multiple time series may benefit from the book as it provides the background and tools for their tasks. It bridges the gap to the difficult technical literature on the topic.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9783540401728
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-540-27752-1 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c294252 _d294252