Skip to main content
×
Home
    • Aa
    • Aa

A Continuous-Time, Latent-Variable Model of Time Series Data

  • Alexander M. Tahk (a1)
Abstract

Many types of time series data in political science, including polling data and events data, exhibit important features'such as irregular spacing, noninstantaneous observation, overlapping observation times, and sampling or other measurement error'that are ignored in most statistical analyses because of model limitations. Ignoring these properties can lead not only to biased coefficients but also to incorrect inference about the direction of causality. This article develops a continuous-time model to overcome these limitations. This new model treats observations as noisy samples collected over an interval of time and can be viewed as a generalization of the vector autoregressive model. Monte Carlo simulations and two empirical examples demonstrate the importance of modeling these features of the data.

Copyright
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×
MathJax
Type Description Title
PDF
Supplementary Materials

Tahk supplementary material
Appendix

 PDF (3.6 MB)
3.6 MB

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 41 *
Loading metrics...

Abstract views

Total abstract views: 105 *
Loading metrics...

* Views captured on Cambridge Core between 4th January 2017 - 21st October 2017. This data will be updated every 24 hours.