IDEAS home Printed from https://ideas.repec.org/a/wly/amposc/v54y2010i3p766-782.html
   My bibliography  Save this article

Structural Change in U.S. Presidents' Use of Force

Author

Listed:
  • Jong Hee Park

Abstract

Has there been a structural change in the way U.S. presidents use force abroad since the nineteenth century? In this article, I investigate historical changes in the use of force by U.S. presidents using Bayesian changepoint analysis. In doing so, I present an integrated Bayesian approach for analyzing changepoint problems in a Poisson regression model. To find the nature of the breaks, I estimate parameters of the Poisson regression changepoint model using Chib's (1998) hidden Markov model algorithm and Frühwirth‐Schnatter and Wagner's (2006) data augmentation method. Then, I utilize transdimensional Markov chain Monte Carlo methods to detect the number of breaks. Analyzing yearly use of force data from 1890 to 1995, I find that, controlling for the effects of the Great Depression and the two world wars, the relationship between domestic conditions and the frequency of the use of force abroad fundamentally shifted in the 1940s.

Suggested Citation

  • Jong Hee Park, 2010. "Structural Change in U.S. Presidents' Use of Force," American Journal of Political Science, John Wiley & Sons, vol. 54(3), pages 766-782, July.
    • Handle: RePEc:wly:amposc:v:54:y:2010:i:3:p:766-782
    DOI: 10.1111/j.1540-5907.2010.00459.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1540-5907.2010.00459.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1540-5907.2010.00459.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Mueller, John E., 1970. "Presidential Popularity from Truman to Johnson1," American Political Science Review, Cambridge University Press, vol. 64(1), pages 18-34, March.
    2. Bãœthe, Tim, 2002. "Taking Temporality Seriously: Modeling History and the Use of Narratives as Evidence," American Political Science Review, Cambridge University Press, vol. 96(3), pages 481-493, September.
    3. Jushan Bai & Pierre Perron, 1998. "Estimating and Testing Linear Models with Multiple Structural Changes," Econometrica, Econometric Society, vol. 66(1), pages 47-78, January.
    4. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
    5. Vrontos, I D & Dellaportas, P & Politis, D N, 2000. "Full Bayesian Inference for GARCH and EGARCH Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(2), pages 187-198, April.
    6. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    7. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    8. Ostrom, Charles W. & Job, Brian L., 1986. "The President and the Political Use of Force," American Political Science Review, Cambridge University Press, vol. 80(2), pages 541-566, June.
    9. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    10. Kim, Chang-Jin & Piger, Jeremy & Startz, Richard, 2008. "Estimation of Markov regime-switching regression models with endogenous switching," Journal of Econometrics, Elsevier, vol. 143(2), pages 263-273, April.
    11. Bradley P. Carlin & Alan E. Gelfand & Adrian F. M. Smith, 1992. "Hierarchical Bayesian Analysis of Changepoint Problems," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 389-405, June.
    12. Howell, William G. & Pevehouse, Jon C., 2005. "Presidents, Congress, and the Use of Force," International Organization, Cambridge University Press, vol. 59(1), pages 209-232, January.
    13. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    14. Martin, Andrew D. & Quinn, Kevin M., 2002. "Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999," Political Analysis, Cambridge University Press, vol. 10(2), pages 134-153, April.
    15. Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, vol. 86(2), pages 221-241, June.
    16. McGinnis, Michael D. & Williams, John T., 1989. "Change and Stability in Superpower Rivalry," American Political Science Review, Cambridge University Press, vol. 83(4), pages 1101-1123, December.
    17. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
    18. Spirling, Arthur, 2007. "Bayesian Approaches for Limited Dependent Variable Change Point Problems," Political Analysis, Cambridge University Press, vol. 15(4), pages 387-405.
    19. Sims, Christopher A, 1972. "Money, Income, and Causality," American Economic Review, American Economic Association, vol. 62(4), pages 540-552, September.
    20. Lieberman, Robert C., 2002. "Ideas, Institutions, and Political Order: Explaining Political Change," American Political Science Review, Cambridge University Press, vol. 96(4), pages 697-712, December.
    21. Alt, James E. & King, Gary & Signorino, Curtis S., 2001. "Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data," Political Analysis, Cambridge University Press, vol. 9(1), pages 21-44, January.
    22. Brandt, Patrick T. & Freeman, John R., 2006. "Advances in Bayesian Time Series Modeling and the Study of Politics: Theory Testing, Forecasting, and Policy Analysis," Political Analysis, Cambridge University Press, vol. 14(1), pages 1-36, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Brandt, Patrick T. & George, Justin & Sandler, Todd, 2016. "Why concessions should not be made to terrorist kidnappers," European Journal of Political Economy, Elsevier, vol. 44(C), pages 41-52.
    2. Hannah L. Paul & Andrew Q. Philips, 2022. "What goes up must come down: Theory and model specification of threshold dynamics," Social Science Quarterly, Southwestern Social Science Association, vol. 103(5), pages 1273-1289, September.
    3. Mark David Nieman, 2016. "Moments in time: Temporal patterns in the effect of democracy and trade on conflict," Conflict Management and Peace Science, Peace Science Society (International), vol. 33(3), pages 273-293, July.
    4. Sushil Gupta & Martin K. Starr & Reza Zanjirani Farahani & Mahsa Mahboob Ghodsi, 2020. "Prevention of Terrorism–An Assessment of Prior POM Work and Future Potentials," Production and Operations Management, Production and Operations Management Society, vol. 29(7), pages 1789-1815, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Simon C. Smith, 2020. "Equity premium prediction and structural breaks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 25(3), pages 412-429, July.
    2. John M. Maheu & Stephen Gordon, 2008. "Learning, forecasting and structural breaks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 553-583.
    3. Nidhal Mgadmi & Houssem Rachdi & Hichem Saidi & Khaled Guesmi, 2019. "On the Instability of Tunisian Money Demand: Some Empirical Issues with Structural Breaks," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(1), pages 153-165, March.
    4. Geweke, J. & Joel Horowitz & Pesaran, M.H., 2006. "Econometrics: A Bird’s Eye View," Cambridge Working Papers in Economics 0655, Faculty of Economics, University of Cambridge.
    5. Wang, Xia & Zheng, Tingguo & Zhu, Yanli, 2014. "Money–output Granger causal dynamics in China," Economic Modelling, Elsevier, vol. 43(C), pages 192-200.
    6. Gary M. Koop & Simon M. Potter, 2004. "Forecasting and Estimating Multiple Change-point Models with an Unknown Number of Change-points," Discussion Papers in Economics 04/31, Division of Economics, School of Business, University of Leicester.
    7. Sjoerd van den Hauwe & Richard Paap & Dick J.C. van Dijk, 2011. "An Alternative Bayesian Approach to Structural Breaks in Time Series Models," Tinbergen Institute Discussion Papers 11-023/4, Tinbergen Institute.
    8. Iiboshi, Hirokuni & Watanabe, Toshiaki, 2005. "Has the Business Cycle Changed in Japan? A Bayesian Analysis Based on a Markov-Switching Model with Multiple Change-Points," MPRA Paper 93865, University Library of Munich, Germany.
    9. Kandemir Kocaaslan, Ozge, 2013. "The causal link between energy and output growth: Evidence from Markov switching Granger causality," Energy Policy, Elsevier, vol. 63(C), pages 1196-1206.
    10. Otilia Boldea & Alastair R. Hall, 2013. "Testing structural stability in macroeconometric models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 9, pages 206-228, Edward Elgar Publishing.
    11. Yong Song & Tomasz Wo'zniak, 2020. "Markov Switching," Papers 2002.03598, arXiv.org.
    12. Max Gillman & Anton Nakov, 2004. "Granger causality of the inflation–growth mirror in accession countries," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 12(4), pages 653-681, December.
    13. Chauvet, Marcelle & Senyuz, Zeynep, 2016. "A dynamic factor model of the yield curve components as a predictor of the economy," International Journal of Forecasting, Elsevier, vol. 32(2), pages 324-343.
    14. Chauvet, Marcelle & Senyuz, Zeynep, 2008. "A Joint Dynamic Bi-Factor Model of the Yield Curve and the Economy as a Predictor of Business Cycles," MPRA Paper 15076, University Library of Munich, Germany, revised Apr 2009.
    15. Marcelle Chauvet & Zeynep Senyuz, 2012. "A Dynamic Factor Model of the Yield Curve as a Predictor of the Economy," Finance and Economics Discussion Series 2012-32, Board of Governors of the Federal Reserve System (U.S.).
    16. repec:hal:journl:peer-00732535 is not listed on IDEAS
    17. Anne Morrison Piehl & Suzanne J. Cooper & Anthony A. Braga & David M. Kennedy, 2003. "Testing for Structural Breaks in the Evaluation of Programs," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 550-558, August.
    18. Gómez-Puig, Marta & Sosvilla-Rivero, Simón, 2014. "Causality and contagion in EMU sovereign debt markets," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 12-27.
    19. Yunmi Kim & Lijuan Huo & Tae-Hwan Kim, 2020. "Dealing with Markov-Switching Parameters in Quantile Regression Models," Working papers 2020rwp-166, Yonsei University, Yonsei Economics Research Institute.
    20. Gary Koop & Simon M. Potter, 2007. "A flexible approach to parametric inference in nonlinear time series models," Staff Reports 285, Federal Reserve Bank of New York.
    21. Geweke, John & Jiang, Yu, 2011. "Inference and prediction in a multiple-structural-break model," Journal of Econometrics, Elsevier, vol. 163(2), pages 172-185, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:amposc:v:54:y:2010:i:3:p:766-782. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1111/(ISSN)1540-5907 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.