MARC Record from Library of Congress

LEADER: 02668cam a2200337 i 4500
001 2014010088
003 DLC
005 20150324083029.0
008 140318s2014 nyua b 001 0 eng
010 $a 2014010088
020 $a9780521871167 (hardback)
020 $a9780521691550 (paperback)
040 $aDLC$beng$erda$cDLC$dDLC
042 $apcc
050 00 $aHA30.3$b.B69 2014
082 00 $a300.1/51955$223
084 $aPOL000000$2bisacsh
100 1 $aBox-Steffensmeier, Janet M.,$d1965-
245 10 $aTime series analysis for social sciences /$cJanet Box-Steffensmeier, The Ohio State University, John R. Freeman, University of Minnesota, Jon C.W. Pevehouse, University of Wisconsin Madison, Matthew P. Hitt, The Louisiana State University.
264 1 $aNew York :$bCambridge University Press,$c[2014]
300 $axv, 280 pages :$billustrations ;$c23 cm
336 $atext$2rdacontent
337 $aunmediated$2rdamedia
338 $avolume$2rdacarrier
490 0 $aAnalytical methods for social research
504 $aIncludes bibliographical references and index.
520 $a"Time-series, or longitudinal, data are ubiquitous in the social sciences. Unfortunately, analysts often treat the time-series properties of their data as a nuisance rather than a substantively meaningful dynamic process to be modeled and interpreted. Time-Series Analysis for Social Sciences provides accessible, up-to-date instruction and examples of the core methods in time-series econometrics. Janet M. Box-Steffensmeier, John R. Freeman, Jon C. Pevehouse, and Matthew P. Hitt cover a wide range of topics including ARIMA models, time-series regression, unit-root diagnosis, vector autoregressive models, error-correction models, intervention models, fractional integration, ARCH models, structural breaks, and forecasting. This book is aimed at researchers and graduate students who have taken at least one course in multivariate regression. Examples are drawn from several areas of social science, including political behavior, elections, international conflict, criminology, and comparative political economy"--$cProvided by publisher.
505 8 $aMachine generated contents note: 1. Modeling social dynamics; 2. Univariate time-series models; 3. Dynamic regression models; 4. Modeling the dynamics of social systems; 5. Univariate, nonstationary processes: tests and modeling; 6. Co-integration and error-correction models; 7. Selections on time-series analysis; 8. Concluding thoughts for the time-series analyst.
650 0 $aTime-series analysis.
650 0 $aTime-series analysis$xMathematical models.
650 7 $aPOLITICAL SCIENCE / General.$2bisacsh