| Record ID | marc_columbia/Columbia-extract-20221130-006.mrc:343752440:3332 |
| Source | marc_columbia |
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LEADER: 03332mam a22003614a 4500
001 2804122
005 20221013014142.0
008 000719t20012001nyua b 001 0 eng
010 $a 00059552
020 $a0387951121 (alk. paper)
035 $a(OCoLC)ocm44633037
035 $9ARS3606CU
035 $a2804122
040 $aDLC$cDLC$dC#P$dOrLoB-B
042 $apcc
050 00 $aHB3711$b.K26 2001
082 00 $a338.5/42/0151955$221
100 1 $aKaiser, Regina.$0http://id.loc.gov/authorities/names/no99084887
245 10 $aMeasuring business cycles in economic time series /$cRegina Kaiser, Agustín Maravall.
260 $aNew York :$bSpringer,$c[2001], ©2001.
300 $aviii, 190 pages :$billustrations ;$c24 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
490 1 $aLecture notes in statistics ;$v154
504 $aIncludes bibliographical references (p. [177]-183) and indexes.
505 00 $g1.$tIntroduction and Brief Summary --$g2.$tA Brief Review of Applied Time Series Analysis.$g2.1.$tSome Basic Concepts.$g2.2.$tStochastic Processes and Stationarity.$g2.3.$tDifferencing.$g2.4.$tLinear Stationary Process, Wold Representation, and Auto-correlation Function.$g2.5.$tThe Spectrum.$g2.6.$tLinear Filters and Their Squared Gain --$g3.$tARIMA Models and Signal Extraction.$g3.1.$tARIMA Models.$g3.2.$tModeling Strategy, Diagnostics and Inference.$g3.2.1.$tIdentification.$g3.2.2.$tEstimation and Diagnostics.$g3.2.3.$tInference.$g3.2.4.$tA Particular Class of Models.$g3.3.$tPreadjustment.$g3.4.$tUnobserved Components and Signal Extraction.$g3.5.$tARIMA-Model-Based Decomposition of a Time Series.$g3.6.$tShort-Term and Long-Term Trends --$g4.$tDetrending and the Hodrick-Prescott Filter.$g4.1.$tThe Hodrick-Prescott Filter: Equivalent Representations.$g4.2.$tBasic Characteristics of the Hodrick-Prescott Filter.$g4.3.$tSome Criticisms and Discussion of the Hodrick-Prescott Filter.
505 80 $g4.4.$tThe Hodrick-Prescott Filter as a Wiener-Kolmogorov Filter.$g4.4.1.$tAn Alternative Representation.$g4.4.2.$tDerivation of the Filter.$g4.4.3.$tThe Algorithm.$g4.4.4.$tA Note on Computation --$g5.$tSome Basic Limitations of the Hodrick-Prescott Filter.$g5.1.$tEndpoint Estimation and Revisions.$g5.1.1.$tPreliminary Estimation and Revisions.$g5.1.2.$tAn Example.$g5.2.$tSpurious Results.$g5.2.1.$tSpurious Crosscorrelation.$g5.2.2.$tSpurious Autocorrelation; Calibration.$g5.2.3.$tSpurious Periodic Cycle.$g5.3.$tNoisy Cyclical Signal --$g6.$tImproving the Hodrick-Prescott Filter.$g6.1.$tReducing Revisions.$g6.2.$tImproving the Cyclical Signal --$g7.$tHodrick-Prescott Filtering Within a Model-Based Approach.$g7.1.$tA Simple Model-Based Algorithm.$g7.2.$tA Complete Model-Based Method; Spuriousness Reconsidered.$g7.3.$tSome Comments on Model-Based Diagnostics and Inference.$g7.4.$tMMSE Estimation of the Cycle: A Paradox.
650 0 $aBusiness cycles.$0http://id.loc.gov/authorities/subjects/sh85018278
650 0 $aTime-series analysis.$0http://id.loc.gov/authorities/subjects/sh85135430
700 1 $aMaravall, Agustín.$0http://id.loc.gov/authorities/names/n79023269
830 0 $aLecture notes in statistics (Springer-Verlag) ;$vv. 154.$0http://id.loc.gov/authorities/names/n42015168
852 00 $bmat$hHB3711$i.K26 2001
852 00 $bmat$hHB3711$i.K26 2001