What do we know about estimating government spending multipliers?

초록

Using the DSGE model as the data-generating process (DGP), we assess how three key modeling choices influence government spending multiplier estimates: (1) the econometric method—vector autoregressions (VARs) versus local projections (LPs); (2) the identification strategy for government spending shocks—such as recursive, Blanchard–Perotti (BP), or forecast error (FE) methods; and (3) the variable transformation—log versus Gordon–Krenn (GK). Our results demonstrate that even when using the same data set, these choices can lead to substantially different multiplier estimates. Furthermore, we find that the choice of econometric method should align with the shock identification strategy and targeted estimation horizon. For the short-run, LP method produces the most accurate government spending multipliers when the true shock sequence is known. When there is no strong candidate for the shock, BP-type shocks are preferable, with both VAR and LP methods being more suitable for short-run analysis, while VAR models yield more reliable estimates for long-run horizons. Additionally, using the GK transformation instead of the log transformation reduces the upward bias commonly observed in VAR and LP estimates.

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