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Separating the wheat from the chaff: A disaggregate analysis of the effects of public spending in the US

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  • Hafedh Bouakez
  • Denis Larocque
  • Michel Normandin

Abstract

In this paper, we undertake a systematic disaggregate analysis of the effects of government spending on economic activity in the US. The level of disaggregation we consider is the highest available and is unprecedented in the empirical literature. More specifically, public consumption and public investment are decomposed into various subcategories, which are measured at the levels of the federal (defence and non-defence), and state and local governments. For each subcategory, we estimate a structural vector autoregression that identifies public spending shocks through the conditional heteroskedasticity of the structural disturbances, thus relaxing the identifying restrictions commonly used in the literature. Our analysis reveals significant heterogeneity in the effects of public spending shocks on output both across subcategories and government levels. Shocks to spending on durables and structures are found to have the largest and most persistent effect on aggregate output, with a peak multiplier that exceeds 1. Our results also suggest that there is little association between the size of a given category of public expenditures and the magnitude of its effect on output.

Suggested Citation

  • Hafedh Bouakez & Denis Larocque & Michel Normandin, 2018. "Separating the wheat from the chaff: A disaggregate analysis of the effects of public spending in the US," Canadian Journal of Economics, Canadian Economics Association, vol. 51(2), pages 361-390, May.
    • Handle: RePEc:cje:issued:v:51:y:2018:i:2:p:361-390
    DOI: 10.1111/caje.12324
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    2. Shiu‐Sheng Chen, 2023. "A direct approach to Kilian–Lewis style counterfactual analysis in vector autoregression models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(7), pages 1068-1076, November.
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    • 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

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