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Aggregate Supply in the United States: Recent Developments and Implications for the Conduct of Monetary Policy

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Abstract

The recent financial crisis and ensuing recession appear to have put the productive capacity of the economy on a lower and shallower trajectory than the one that seemed to be in place prior to 2007. Using a version of an unobserved components model introduced by Fleischman and Roberts, we estimate that potential GDP in late 2014 was about 7 percent below the trajectory it appeared to be on prior to 2007. We argue that a significant portion of the recent damage to the supply side of the economy plausibly was endogenous to the weakness in aggregate demand. Endogeneity of supply with respect to demand provides a strong motivation for a vigorous policy response to a weakening in aggregate demand, and we present optimal-control simulations showing how monetary policy might respond to such endogeneity in the absence of other considerations. We then discuss how other considerations—such as increased risks of financial instability or inflation instability—could cause policymakers to exercise restraint in their response to cyclical weakness.

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Notes

  1. Kienzler and Schmid (2013) and Ikeda and Kurozumi (2014) reach a similar conclusion in the context of dynamic stochastic general equilibrium (DSGE) models with hysteresis dynamics.

  2. See, for example, Congressional Budget Office (2012). Similarly, the European Central Bank (2011) estimates that the financial crisis led to a permanent drop in the level of potential output in the euro area, but argues that the effects on potential growth going forward are more uncertain. In related work, Martin and Wilson (2013) “find that severe recessions have a sustained and sizable impact on the level of output.”

  3. A production-function approach is also used by the CBO, the IMF, the ECB, and the OECD in developing their estimates of potential output. See also Fernald (2012), Basu and Fernald (2009), Clark (1987), and Gordon (2003).

  4. The state-space model that we use has, with some modifications, been embedded in the Federal Reserve’s large-scale econometric model of the U.S. economy, FRB/US See http://www.federalreserve.gov/econresdata/frbus/us-models-package.htm for further details.

  5. Nalewaik (2010) shows that elements from the income side of the national income and product accounts have substantial incremental information content relative to elements from the product side.

  6. From 1990 through the present, Δp t e equals the median projection of inflation over the next 10 years reported quarterly in the Survey of Professional Forecasters; from 1980 through 1989, it equals the average expected rate of inflation 10 years ahead reported in the Hoey survey of financial market participants. Where necessary, both survey measures are adjusted to put them on a PCE price index basis. Prior to 1980, Δp t e is inferred from the low frequency movements in actual inflation.

  7. The estimation period for the state-space model is 1963:Q2 to 2014:Q3, where the latter date is the most recent quarter for which published national account data are available.

  8. The estimated deceleration in trend MFP is noticeable from 2010 on, and presumably reflects the weak readings on actual labor productivity recorded from 2011 through 2014. In the eyes of the state-space model, these weak readings imply slow trend growth in MFP because they occurred while the unemployment rate was falling steadily and growth in capital services was recovering.

  9. See Aaronson and others (2006, 2014) for a discussion of the contribution of demographic trends to the recent decline in the labor force participation rate. For a discussion of the effects of immigration on U.S. population growth, see Frey (2014).

  10. Because the state-space model does not forecast capital deepening (in contrast to, say, trend labor force participation), any real-time projection of potential GDP beyond the current quarter requires some assumption for the future path of capital services. Similar considerations apply to population, which in these real-time calculations is assumed to continue rising at the average pace observed over the previous year.

  11. Comparing potential GDP estimates based on the June 2008 and June 2009 vintages of data to those based on subsequent vintages is somewhat difficult because the measures of real output and income used to calculate potential GDP were rebased from 2000 dollars to 2005 dollars beginning in July 2009. In Figure 4, the 2008 and 2009 real-time estimates of the level of potential GDP are rescaled by a constant multiplicative factor that has the effect of making these vintages’ historical estimates of real GDP from the late 1940s through the late 1990s closely match those published at a later date.

  12. Evidence on this point is presented in the appendix to the working paper version of the paper. As noted there, some analysts judgmentally marked down their supply-side estimates in the wake of the financial crisis on the grounds that such events typically result in persistent supply-side damage. Judgmental assessments of this sort raise the question: Should the specification of a “good” state-space model allow for discrete shifts in parameters and shocks following the onset of a financial crisis? Although the answer to this question is almost certainly “yes” in principle, the practical difficulty of doing so is quite high owing to the rarity of such crises domestically and the uncertainties of calibrations based on international experience.

  13. A somewhat similar phenomenon occurred with the receipt of the 2009 and 2010 vintages of data. Over this period, the unemployment rate rose by more than the state-space model would have predicted given actual GDP growth and prior estimates of potential output growth. In response to these and other surprises, the model revised down its estimate of potential GDP growth while simultaneously revising up its estimate of the natural rate. Such a pattern of revisions helps to reduce the error in Okun’s Law (which is implicitly embedded in the model’s structure) without appreciably increasing the prediction errors in the model’s Phillips curve, whose slope is estimated to be quite flat.

  14. The real-time estimates presented in Figure 4 are consistent with the claim that data revisions are a significant source of uncertainty, in that data revisions in 2009 and 2010 account for a sizable portion of the downward adjustments to the state-space model’s estimates and projections of potential output growth.

  15. Aside from these three variants of the state-space model, we also considered versions in which we dropped the Phillips curve from the model, replaced all the AR(1) growth rate specifications with random walks, and excluded aggregate nonfarm income. As documented in the working paper version of this paper, none of these changes appreciably changed our estimates of potential output.

  16. Dornbusch and Fischer (1978), the first edition of their macro text, describes the expectations-augmented Phillips curve in this manner (pp. 404–405) and specifically references a long-run vertical Phillips curve (p. 410). For the first known presentation of the standard textbook vertical supply curve to the Federal Open Market Committee, see page 25 of the document provided at: http://www.federalreserve.gov/monetarypolicy/files/FOMC19831115material.pdf. To be sure, ideas along these lines had been presented and discussed among Federal Reserve staff for more than a decade, as illustrated by an “accelerationist” version of the Board’s MPS model developed by William Poole (1971) and a paper that Robert Lucas gave at a 1970 conference hosted at the Board (Lucas, 1972).

  17. It is well known that individuals with longer spells of unemployment find it more difficult to become reemployed. In the past, however, researchers have found it difficult to separate the effects of unobserved heterogeneity in the individuals experiencing long spells of unemployment from duration dependence. To address this issue, Kroft, Lange, and Notowidigdo (2013) recently conducted an experiment and found that, all else equal, potential employers were much less likely to call back job applicants with longer spells of unemployment than otherwise identical applicants with shorter spells, evidence that is consistent with duration dependence in unemployment. Although the aggregate implications of this finding are unclear, under some interpretations employers’ aversion to long unemployment spells could result in hysteresis.

  18. See, for example, Comin and Gertler (2006). Barlevy (2007) argues that the procyclicality of R&D reflects externalities that cause firms to undertake more R&D in economic booms than would be optimal. In contrast, Aghion and others (2012) shows that credit constraints can limit the capacity for firms to invest in R&D during recessions if profits—and thus internal funds—are too low to finance such investments directly.

  19. See http://www.bls.gov/mfp/rdtable.pdf.

  20. In theory, the reduced pace of business capital deepening in the United States seen since mid-2008 could be the result of technology shocks that have reduced the marginal return on capital. Arguing against this interpretation, however, is the elevated level of profitability. Alternatively, one might argue that the decline in business investment has been driven at least in part by reduced access to capital associated with permanently tighter underwriting standards and other structural changes in credit markets. Whether the latter phenomenon is best thought of as a technological rather than a demand development is open to debate, however; in any event, the restrictions on credit availability that have emerged since the financial crisis have been more important for households than for businesses (especially large ones). For these reasons, we believe that most of the observed slowdown in business investment is primarily a response to a weak demand environment and heightened uncertainty about the future pace of recovery.

  21. Such drawn-out capital accumulation dynamics are a standard feature of estimated macro models, including the Federal Reserve Board’s FRB/US model and its two DSGE models, EDO and SIGMA.

  22. On the surface, purely statistical methods for extracting trend output, such as the Beveridge-Nelson decomposition or the Hodrick-Prescott filter, might also seem to avoid this issue because they do not condition on any measure of the capital stock. For the reasons discussed earlier, however, such methods have the problem of ascribing to the “trend” any movements in output associated with drawn-out fluctuations in capital services and other inputs, whether or not they are endogenous.

  23. Even if an estimate of potential output generated by a DSGE model is based on the actual business capital stock, comparing that estimate to one based on the production-function approach may be problematic because the model’s measure of capital may differ noticeably from the official government measure. In part, such differences can arise because DSGE models often define business capital to include residential capital and the stock of consumer durable goods, unlike the nonfarm business sector measure used in the state-space analysis discussed earlier. In addition, DSGE models may implicitly use a different methodology for translating the business capital stock into an aggregate flow of capital services. Finally, DSGE models often treat the capital stock as an unobserved variable, an assumption that can result in yet more differences from the official series.

  24. For more information on FRB/US, including equation documentation, sample simulation code and links to research papers employing the model, see http://www.federalreserve.gov/econresdata/frbus/us-models-about.htm.

  25. Other private-sector expectations—most importantly, households’ assessments of future income—are generated using a small-scale VAR model, implying that their forward-looking expectations are based on the average historical behavior of the economy. We believe that this characterization of household beliefs is more realistic than the standard assumption of rational (model-consistent) expectations, which assume a complete understanding of the dynamics of the economy, including how they are altered by the zero lower bound on nominal interest rates. That said, making all private-sector expectations model-consistent would have had no appreciable qualitative effects on the results reported in this paper. For a further discussion of expectational effects in the FRB/US model, see Brayton, and others (1997).

  26. In the standard version of FRB/US, which incorporates a state-space model similar to the one discussed in the first section of the paper, the equivalents to U**, LFPR**, and MFP** are subject to permanent shocks; these shocks are idiosyncratic and unrelated to shortfalls in aggregate demand. Such shocks are not relevant for the analysis considered in this section of the paper, however, and so are suppressed here to simplify the analysis.

  27. The Scandinavian labor markets do appear to have changed permanently after their financial crisis, but these long-run changes plausibly reflected legislative changes to labor laws and other aspects of the social safety net.

  28. The specification of H(.) as well as the coefficients of the three equations have been calibrated to yield endogenous movements in U*, LFPR*, and MFP* that, in the context of the “severe demand shock” scenario discussed below, appear roughly consistent with the experience of the last few years. Arguably, it would have been better to estimate these equations (and the shape of the scaling function) rather than calibrate them. However, given the lack of historical evidence for hysteresis effects in the United States prior to the current episode, and given that our simulations are intended to explore the possible implications of recent events (as opposed to the most likely ones), we doubt that results from any time-series exercise would be particularly illuminating.

  29. These direct shocks are presumed to reflect those effects of a financial crisis that operate through channels not formally accounted for in the model’s structure, such as reduced access to credit as a result of tighter lending standards and persistent balance-sheet problems, increased uncertainty about future household income and corporate earnings, and a general deterioration in consumer and business confidence. In the context of many DSGE models (including the Federal Reserve’s EDO model), the effects of such disruptions are typically captured through an economy-wide risk premium shock intended to provide a theoretical explanation for the correlated downturn in consumption and investment. Nevertheless, like FRB/US, current DSGE models do not really provide a satisfactory accounting of the various linkages between financial markets and the real economy that come into play during a financial crisis.

  30. Specifically, the rule is R(t) =0.85 R(t-1) +0.15 {2+π(t)+0.5 [π(t)−2]+1.0 Y(t)}, where R is the nominal funds rate, π is the four-quarter rate of core PCE inflation, and Y is the output gap. A noninertial version of this rule is discussed in Taylor (1999).

  31. In the baseline, the unemployment rate, PCE inflation, and the nominal federal funds rate are constant at 5.35, 2.0, and 3.75 percent, respectively, consistent with the mid-points of the central tendencies of the longer-run forecasts reported by the FOMC in the September, 2014 Survey of Economic Projections. The results reported in this section are largely insensitive to these baseline assumptions, with the critical exception of nominal interest rates. Because the simulations incorporate the zero lower bound constraint, the baseline setting of the federal funds rate has an important bearing on the ability of monetary policy to offset the financial crisis.

  32. See Brayton, Laubach and Reifschneider (2014), http://www.federalreserve.gov/econresdata/notes/feds-notes/2014/optimal-control-monetary-policy-in-frbus-20141121.html, for a discussion of OC policy responses to recent economic developments and the sensitivity of those responses to alternative assumptions. Also, see Svensson and Tetlow (2005) for a discussion of its use in FOMC briefing documents.

  33. In addition to aiming to keep unemployment near its natural rate and inflation near the FOMC’s 2 percent target, the loss function penalizes quarter-to-quarter movements in the federal funds rate. In reality, such movements would be destabilizing and thus would have adverse effects on financial markets and the broader economy, implying that such movements would be avoided by policymakers because of their effects on the unemployment gap. However, the FRB/US model does not incorporate any mechanism for such volatility to affect financial conditions and real activity through risk premiums or some other channel, so the third term is added to the loss function to prevent unrealistically large quarterly movements in short-term interest rates in the optimal-control simulations.

  34. In our optimal-control analysis, M (the number of quarters in the optimized path of R) is always set to 60 quarters while N (the number of quarters over which the loss function is evaluated) is set to 80 quarters; in addition, the discount factor β is set to 0.99 and the three α loss weights are all set to unity. Increasing the value of either M or N would have relatively little effect on our simulation results, as would modestly changing the discount factor or altering the relative loss weights. Beyond quarter t0+M, when the OC path ends, the federal funds rate is assumed to follow the prescriptions of the inertial policy rule.

  35. Optimal-control strategies of this sort raise issues of time consistency and how policy should be reoptimized in light of previous commitments and incoming data surprises. These questions are beyond the scope of this paper, however, and in the simulations discussed below we assume that policymakers do not reoptimize the trajectory for the path of the funds rate beyond t0.

  36. This latter effect is achieved by artificially cutting the link between business investment and capital services.

  37. As is well known, uncertainty has little or no role to play in optimal-control decision-making when preferences are well described by a quadratic loss function and the economy is linear. (In our analysis, we have sidestepped the fact that the FRB/US model is not strictly linear, especially in the presence of the zero lower bound.) In reality, however, the actual economy sometimes exhibits highly nonlinear dynamics; moreover, policymakers’ concerns are not adequately captured by a simple loss function, and certainty about the specification of the model is a poor approximation. For these and other reasons, uncertainty assuredly plays an important role in policymaker deliberations in the real world.

  38. Of course, if policymakers were instead concerned about minimizing the risk of a future financial crisis, then robust control might argue for a less activist strategy.

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Authors
  1. Dave Reifschneider
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  2. William Wascher
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  3. David Wilcox
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Additional information

Supplementary information accompanies this article on the IMF Economic Review website (www.palgrave-journals.com/imfer).

*Dave Reifschneider is a special advisor to the Board of Governors of the Federal Reserve System. William Wascher is a deputy director and David Wilcox is the director of the Division of Research and Statistics at the Board. The authors thank Hess Chung, Bruce Fallick, Michael Kiley, Jean-Philippe Laforte, Jesper Linde, Jeremy Rudd, and three anonymous reviewers at the IMF for their helpful comments; they also thank John Roberts for providing the code used to estimate the FRB/US state-space model of supply-side conditions and Dennis Mawhirter for helpful research assistance. The analysis and conclusions set forth in this paper are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors.

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Appendix

Appendix

The State Space Model

  1. 1

    Real GDP (per capita, logged)

  2. 2

    Real nonfarm business output (per capita, logged)

  3. 3

    Real nonfarm business income (per capita, logged)

  4. 4

    Workweek, nonfarm business sector (logged)

  5. 5

    Employment, nonfarm business sector (per capita, logged)

  6. 6

    Employment-to-population ratio (logged)

  7. 7

    Labor force participation rate (logged)

  8. 8

    Core PCE inflation

    Note: MA(X, n) denotes the n-quarter moving average of X.

  9. 9

    Business cycle (state variable)

  10. 10

    Nonfarm business output error (state variable)

  11. 11

    Nonfarm business income error (state variable)

  12. 12

    Trend level of the GDP-NFB output wedge (state variable)

  13. 13

    Trend growth rate of the GDP-NFB output wedge (state variable)

  14. 14

    Trend level of multifactor productivity (state variable)

  15. 15

    Trend growth rate of multifactor productivity (state variable)

  16. 16

    Trend NFB workweek (state variable)

  17. 17

    Trend growth rate of the NFB workweek (state variable)

  18. 18

    Trend level of the wedge between household and NFB payroll employment (state variable)

  19. 19

    Trend growth rate of the wedge between household and NFB payroll employment (state variable)

  20. 20

    Trend level of the labor force participation rate (state variable)

  21. 21

    Trend growth rate of the labor force participation rate (state variable)

  22. 22

    Natural rate of employment (state variable)

Exogenous variables

lveoa :

trend energy-output ratio (logged)

lks :

capital services (per capita, logged)

lqualt :

labor quality (logged)

rpe :

PCE energy prices relative to core PCE prices, weighted by energy share of consumer spending

rpm :

non-oil import prices relative to core PCE prices, weighted by import share of domestic spending

wpc :

wage-price controls (1971:q3 to 1974:q1=1, 1974:q2 to 1974:q4=−3.67,=0 otherwise)

d84 :

dummy variable (=1 from 1985:q1 on,=0 otherwise)

epi :

expected long-run inflation (as reported in the Survey of Professional Forecasters from 1990 to the present and in the Hoey survery from 1981 to 1990; prior to 1981 expectations are inferred by a trend extraction procedure using actual inflation)

Table A1

Table A1 Estimation Results for the State-Space Model (Sample period 1963:Q2 to 2014:Q3)
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Reifschneider, D., Wascher, W. & Wilcox, D. Aggregate Supply in the United States: Recent Developments and Implications for the Conduct of Monetary Policy. IMF Econ Rev 63, 71–109 (2015). https://doi.org/10.1057/imfer.2015.1

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