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  <titleInfo>
    <title>Multiple time series models</title>
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  <name type="personal">
    <namePart>Brandt, Patrick T.</namePart>
    <role>
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  <name type="personal">
    <namePart>Williams, John T. (John Taylor)</namePart>
    <namePart type="date">1958-</namePart>
  </name>
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    <publisher>Sage Publications</publisher>
    <dateIssued>c2007</dateIssued>
    <dateIssued encoding="marc">2007</dateIssued>
    <issuance>monographic</issuance>
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  <language>
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    <extent>xiii, 99 p. : ill. ; 22 cm.</extent>
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  <tableOfContents>List of Figures
List of Tables
Series Editor?s Introduction
Preface
1. Introduction to Multiple Time Series Models
1.1 Simultaneous Equation Approach
1.2 ARIMA Approach
1.3 Error Correction or LSE Approach
1.4 Vector Autoregression Approach
1.5 Comparison and Summary
2. Basic Vector Autoregression Models
2.1 Dynamic Structural Equation Models
2.2 Reduced Form Vector Autoregressions
2.3 Relationship of a Dynamic Structural Equation Model to a Vector Autoregression Model
2.4 Working With This Model
2.5 Specification and Analysis of VAR Models
2.6 Other Specification Issues
2.7 Unit Roots and Error Correction in VARs
2.8 Criticisms of VAR
3. Examples of VAR Analyses
3.1 Public Mood and Macropartisanship
3.2 Effective Corporate Tax Rates
3.3 Conclusion
Appendix: Software for Multiple Time Series Models
Notes
References
Index
About the Authors</tableOfContents>
  <note type="statement of responsibility">Patrick T. Brandt, John T. Williams.</note>
  <note>Includes bibliographical references (p. 92-95) and index.</note>
  <subject authority="lcsh">
    <topic>Time-series analysis</topic>
    <topic>Mathematical models</topic>
  </subject>
  <classification authority="lcc">HA30.3 .B73 2007</classification>
  <classification authority="ddc" edition="23">519.55 BRA W</classification>
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      <title>Sage university papers series. no. 07-148</title>
      <partName>Quantitative applications in the social sciences</partName>
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  <identifier type="isbn">1412906563 (pbk.)</identifier>
  <identifier type="isbn">9781412906562</identifier>
  <identifier type="lccn">2006010016</identifier>
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