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The Effect Of Defense Spending On Us Output: A Factor Augmented Vector Autoregression (Favar) Approach

Author

Listed:
  • Rangan Gupta

    (Department of Economics, University of Pretoria)

  • Alain Kabundi

    (Department of Economics and Econometrics, University of Johannesburg)

  • Emmanuel Ziramba

    (Department of Economics, University of South Africa)

Abstract

Empirical evidence on the effect of defense spending on US output is at best mixed. Against this backdrop, this paper assesses the impact of a positive defense spending shock on the growth rate of real GNP using a FAVAR model estimated with 116 variables spanning the quarterly period of 1976:01 to 2005:02. Overall, the results show that a positive shock to the growth rate of the real defense spending translates to a positive and long lasting effect on the growth rate of real GNP, but the effect is significant only for two quarters. In addition, we indicate that the mixed empirical evidence could be a result of small information sets, by showing the sensitivity of the results to sample size using a small-scale VAR typically used in the literature to analyze the effect of defense spending on output. Finally, given that the FAVAR model was found outperform the VAR in forecasting the growth rate of real GNP, we concluded that the FAVAR framework is superior and should be relied upon more for the analysis of the impact of defense spending on US output.

Suggested Citation

  • Rangan Gupta & Alain Kabundi & Emmanuel Ziramba, 2009. "The Effect Of Defense Spending On Us Output: A Factor Augmented Vector Autoregression (Favar) Approach," Working Papers 200911, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:200911
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    1. repec:cup:apsrev:v:98:y:2004:i:02:p:379-389_00 is not listed on IDEAS
    2. Jesús Crespo Cuaresma & Gerhard Reitschuler, 2004. "A non-linear defence-growth nexus? evidence from the US economy," Defence and Peace Economics, Taylor & Francis Journals, vol. 15(1), pages 71-82, February.
    3. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    4. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809, January.
    5. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    6. Jean Boivin & Marc P. Giannoni & Ilian Mihov, 2009. "Sticky Prices and Monetary Policy: Evidence from Disaggregated US Data," American Economic Review, American Economic Association, vol. 99(1), pages 350-384, March.
    7. H. Sonmez Atesoglu, 2002. "Defense Spending Promotes Aggregate Output in the United States--Evidence from Cointegration Analysis," Defence and Peace Economics, Taylor & Francis Journals, vol. 13(1), pages 55-60.
    8. Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.
    9. Lutz Kilian, 1998. "Small-Sample Confidence Intervals For Impulse Response Functions," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 218-230, May.
    10. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    11. Beck, Nathaniel & King, Gary & Zeng, Langche, 2000. "Improving Quantitative Studies of International Conflict: A Conjecture," American Political Science Review, Cambridge University Press, vol. 94(1), pages 21-35, March.
    12. Jurgen Brauer, 2007. "Data, Models, Coefficients: The Case of United States Military Expenditure," Conflict Management and Peace Science, Peace Science Society (International), vol. 24(1), pages 55-64, February.
    13. Ward, Michael D. & Davis, David R., 1992. "Sizing up the Peace Dividend: Economic Growth and Military Spending in the United States, 1948–1996," American Political Science Review, Cambridge University Press, vol. 86(3), pages 748-755, September.
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    Cited by:

    1. Shahbaz, Muhammad & Leitão, Nuno Carlos & Uddin, Gazi Salah & Arouri, Mohamed & Teulon, Frédéric, 2013. "Should Portuguese economy invest in defense spending? A revisit," Economic Modelling, Elsevier, vol. 35(C), pages 805-815.
    2. Takao Fujii & Kazuki Hiraga & Masafumi Kozuka, 2012. "Analyses of Public Investment Shock in Japan: Factor Augmented Vector Autoregressive Approach," Keio/Kyoto Joint Global COE Discussion Paper Series 2012-006, Keio/Kyoto Joint Global COE Program.
    3. Charles Shaaba Saba & Nicholas Ngepah, 2022. "Nexus between defence spending, economic growth and development: evidence from a disaggregated panel data analysis," Economic Change and Restructuring, Springer, vol. 55(1), pages 109-151, February.
    4. Rabnawaz, Ambar & Jafar, Rana Muhammad Sohail, 2015. "Impact of Public Investment on Economic Growth," MPRA Paper 70377, University Library of Munich, Germany.
    5. Muhammad Shahbaz & Talat Afza & Muhammad Shahbaz Shabbir, 2013. "Does Defence Spending Impede Economic Growth? Cointegration And Causality Analysis For Pakistan," Defence and Peace Economics, Taylor & Francis Journals, vol. 24(2), pages 105-120, April.
    6. Saba Charles Shaaba, 2022. "Defence Spending and Economic Growth in South Africa: Evidence from Cointegration and Co-Feature Analysis," Peace Economics, Peace Science, and Public Policy, De Gruyter, vol. 28(1), pages 51-100, February.
    7. Tiwari, Aviral & Shahbaz, Muhammad, 2011. "Does Defence Spending Stimulate Economic Growth in India?," MPRA Paper 30880, University Library of Munich, Germany, revised 18 Apr 2011.
    8. Kollias, Christos & Paleologou, Suzanna-Maria, 2013. "Guns, highways and economic growth in the United States," Economic Modelling, Elsevier, vol. 30(C), pages 449-455.
    9. repec:ipg:wpaper:2014-380 is not listed on IDEAS
    10. Yi-Hua Wu & Chih-Chin Ho & Eric S. Lin, 2017. "Measuring the Impact of Military Spending: How Far Does a DSGE Model Deviate from Reality?," Defence and Peace Economics, Taylor & Francis Journals, vol. 28(5), pages 585-608, September.

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    More about this item

    Keywords

    Defense Spending; Output; FAVAR;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • H41 - Public Economics - - Publicly Provided Goods - - - Public Goods

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