Explaining the slow U.S. recovery: 2010–2017


This paper argues that the slow U.S. recovery after the 2008–2009 recession was due to sluggish government spending. The analysis uses a structural macroeconometric model. Conditional on government policy, the errors in predicting output for the 2009.4–2017.4 period are within what one would expect historically. Productivity and labor force participation are endogenous variables in the model, and so their behaviors in this period are a consequence of the slow growth rather than a cause.

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  1. 1.

    Some of the comments in the discussion of the FHSW paper argue that more emphasis should have been given to aggregate demand effects in explaining the slow recovery.

  2. 2.

    Users can work with the US model on line or can download the model and related software to work with it on their own computer. If the model is downloaded, it can be modified and reestimated. Many of the results in MM can be duplicated on line. The US model is a subset of a larger multicountry model, the MC model, but for purposes of this paper the US model has been used alone.

  3. 3.

    As noted in Sect. 2, the January 30, 2018, version of the US model is used here. This version uses actual (sometimes preliminary) data through the fourth quarter of 2017. The data thus do not incorporate the NIPA revisions released in July 2018. The revisions were fairly minor, and it is unlikely that the present results would change much if the revised data had been used.

  4. 4.

    There is actually a lot of macro information in Fig. 5, at least from the perspective of the US model. The results in Fair (2004) show that most of the boom in the U.S. economy in the last half of the 1990s was due to the increase in AA—the wealth effect at work. The results in Fair (2005) show that much of the sluggish economy in the 2000.4–2004.3 period was due to the fall in AA. Finally, the results in Fair (2017) show that much of the 2008–2009 recession was due to the huge fall in AA. In this latter case, much of the fall in AA was from the huge decline in housing prices.

  5. 5.

    If the initial estimate of an equation suggests that the error term is serially correlated, the equation is reestimated under the assumption that the error term follows an autoregressive process (usually first order). The structural coefficients in the equation and the autoregressive coefficient or coefficients are jointly estimated (by 2SLS). The \(\hat{u}_t\) error terms are after adjustment for any autoregressive properties, and they are taken to be iid for purposes of the draws.


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Correspondence to Ray C. Fair.

Appendix: Computing standard errors

Appendix: Computing standard errors

There are 25 estimated equations in the US model, but two of these have been dropped for purposes of this paper—the capital gains equation and the Fed rule. This gives 23 equations. The estimation period is 1954.1–2017.4, 256 quarters. For each estimated equation, there are 256 estimated residuals. Let \(\hat{u}_t\) denote the 23-dimension vector of the estimated residuals for quarter t, \(t = 1,\ldots ,256\).Footnote 5

The solution period is 2009.4–2017.4, 33 quarters. The model was solved 2,000 times for this period. Each trial is as follows. First, 33 error vectors are drawn with replacement from the 256 error vectors \(\hat{u}_t\), \(t = 1,\ldots ,256\). These errors are added to the equations, and the model is solved dynamically for the 2009.4–2017.4 period. The predicted values are recorded. This is one trial. This procedure is then repeated 2000 times, which gives 2000 predicted values of each variable. The mean and standard error are then computed for each variable. See MM [Sects. 2.6, 2.7] for more details.

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Fair, R.C. Explaining the slow U.S. recovery: 2010–2017. Bus Econ 53, 184–194 (2018). https://doi.org/10.1057/s11369-018-0095-z

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  • slow recovery
  • productivity
  • labor force participation