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Economic policy uncertainty and household inflation uncertainty

  • Carola Conces Binder EMAIL logo
Veröffentlicht/Copyright: 23. Juni 2017

Abstract

How does uncertainty about economic policy translate into uncertainty about macroeconomic outcomes, in particular inflation? New measures of consumer inflation uncertainty are compared to the economic and monetary policy uncertainty indices of [Baker, S., N. Bloom, and S. Davis. 2016. “Measuring Economic Policy Uncertainty.” Quarterly Journal of Economics.]. Economic policy uncertainty is more strongly correlated with uncertainty about shorter-run inflation than with uncertainty about longer-run inflation, while monetary policy uncertainty is more correlated with uncertainty about longer-run than shorter-run inflation. Both economic and monetary policy uncertainty Granger cause inflation uncertainty. Consumer inflation uncertainty can be computed for demographic subgroups. High income and high education consumers have the lowest inflation uncertainty, but their short-run inflation uncertainty is most strongly correlated with policy uncertainty. The long-run inflation uncertainty of the top income quintile is less correlated with policy uncertainty, possibly reflecting stronger anchoring of inflation expectations for this group. Policy uncertainty appears to reflect the expectations of consumers more than professional forecasters or financial markets.

JEL Classification: D83; D84; E31; E52; E58

Appendix A

Tables and Figures

Table 9:

Coefficient of variation and autocorrelation for uncertainty indices.

VariableCoef. of variationAutocorrelation
EPU41.60.76
MPU61.80.60
Short-horizon CIU23.20.87
Long-horizon CIU17.70.71
CF inflation dispersion29.30.76
Inflation risk premium13.80.72
  1. Monthly data beginning in 1985. Economic and monetary policy uncertainty indices from Baker, Bloom, and Davis (2016). Consumer inflation uncertainty indices updated from Binder (2017). Consensus Forecasts inflation dispersion from Wright (2011). Inflation risk premium from Haubrich, Pennacchi, and Ritchken (2011) and the Federal Reserve Bank of Cleveland.

Table 10:

Granger causality tests from bivariate VARs of uncertainty indices.

Response
EPUMPUShort-run CIULong-run CIU
ImpulseEPU336.8***14.2***
MPU5.224.3***17.8***
Short-run CIU5.13.411.8***
Long-run CIU16.1***16.0***4.1
  1. Table shows χ2 statistic from Granger causality Wald test that the row variable Granger causes the column variable. Each test follows a bivariate VAR with three lags of row and column variable. ***Denotes row variable Granger causes column variable with p < 0.01.

Table 11:

Granger causality test from bivariate VARs of first-differenced uncertainty indices.

Response
EPUMPUShort-run CIULong-run CIU
ImpulseEPU1.138.0***9.2**
MPU3.418.1***7.5**
Short-run CIU0.72.67.2*
Long-run CIU6.8*3.62.9
  1. Table shows χ2 statistic from Granger causality Wald test that the row variable Granger causes the column variable. Each test follows a bivariate VAR with three lags of row and column variable, first-differenced. ***denotes row variable Granger causes column variable with p < 0.01, **with p < 0.05, *with p < 0.1.

Figure 5: Orthogonalized impulse response functions.Notes: Recursive VAR with the Cholesky ordering inflation (pi), unemployment (unemp), Economic Policy Uncertainty (EPU), short-horizon consumer inflation uncertainty (CIU_s), long-horizon consumer inflation uncertainty (CIU_l). Three lags of each variable and 303 monthly observations.
Figure 5:

Orthogonalized impulse response functions.

Notes: Recursive VAR with the Cholesky ordering inflation (pi), unemployment (unemp), Economic Policy Uncertainty (EPU), short-horizon consumer inflation uncertainty (CIU_s), long-horizon consumer inflation uncertainty (CIU_l). Three lags of each variable and 303 monthly observations.

Figure 6: Orthogonalized impulse response functions.Notes: Recursive VAR with the Cholesky ordering inflation (pi), unemployment (unemp), Monetary Policy Uncertainty (MPU), short-horizon consumer inflation uncertainty (CIU_s), long-horizon consumer inflation uncertainty (CIU_l). Three lags of each variable and 303 monthly observations.
Figure 6:

Orthogonalized impulse response functions.

Notes: Recursive VAR with the Cholesky ordering inflation (pi), unemployment (unemp), Monetary Policy Uncertainty (MPU), short-horizon consumer inflation uncertainty (CIU_s), long-horizon consumer inflation uncertainty (CIU_l). Three lags of each variable and 303 monthly observations.

Table 12:

Forecast error variance decomposition for multivariate VARs.

Response
EPUShort CIULong CIUInflationUnemp.
a. VAR with EPU, CIU, inflation, and unemployment
Impulse
 EPU0.770.140.070.030.12
 Short-run CIU0.030.750.290.010.23
 Long-run CIU0.050.010.480.050.01
 Inflation0.090.040.140.900.02
 Unemployment0.060.070.010.010.63
Response
MPUShort CIULong CIUInflationUnemp.
b. VAR with MPU, CIU, inflation, and unemployment
Impulse
 MPU0.850.130.070.010.05
 Short-run CIU0.020.740.290.010.25
 Long-run CIU0.050.010.490.050.01
 Inflation0.060.030.130.920.02
 Unemployment0.020.090.020.010.68
  1. Table shows the fraction of variance in the response variable explained by the impulse variable at the 12-month forecast horizon.

Table 13:

Chi squared statistic for test that EPU or MPU Granger causes inflation uncertainty, by income quintile.

Short horizonLong horizon
QuintileEPUMPUEPUMPU
18.8**6.04.88.7**
225.3***23.5***17.4***15.8***
326.9***24.7***13.2***15.4***
431.6***31.6***17.7***19.3***
531.1***20.0***9.3**9.1**
  1. Table shows χ2 statistic from Granger causality Wald test that EPU or MPU Granger causes inflation uncertainty by income quintile and horizon. Each test follows a bivariate VAR with three lags of row and column variable. ***p < 0.01, **p < 0.05, *p < 0.1.

Table 14:

Chi squared statistic for test that inflation uncertainty Granger causes EPU or MPU, by income quintile.

Horizon 1Horizon 5
Income quintileEPUMPUEPUMPU
12.53.44.69.4**
24.24.016.1***22.0***
35.32.73.75.5
46.22.311.2**9.6**
57.5*8.7**14.5***12.5**
  1. Table shows χ2 statistic from Granger causality Wald test that inflation uncertainty Granger causes EPU or MPU by income quintile and horizon. Each test follows a bivariate VAR with three lags of row and column variable. ***p < 0.01, **p < 0.05, *p < 0.1.

Table 15:

Policy uncertainty regressed on consumer and professional forecaster inflation uncertainty.

(1)(2)(3)(4)
EPUMPUEPUMPU
SPF Uncertainty(t)−0.06−0.06−0.23−0.31
(−0.26)(−0.25)(−0.88)(−1.04)
SPF Uncertainty(t−1)0.280.160.01−0.20
(1.45)(0.71)(0.03)(−0.63)
Unemployment0.42−0.220.29−0.43
(1.41)(−0.62)(0.87)(−1.12)
Inflation−0.68−0.10−0.070.62
(−1.13)(−0.14)(−0.09)(0.64)
Squared Inflation0.630.320.23−0.16
(1.04)(0.44)(0.32)(−0.20)
Fed Funds Rate−0.36−0.59−0.44−0.66
(−0.83)(−1.15)(−0.95)(−1.22)
Δ Fed Funds Rate−0.44*−0.51*−0.27−0.30
(−1.97)(−1.92)(−0.95)(−0.94)
t−0.53*−0.60−0.44−0.47
(−1.81)(−1.74)(−1.44)(−1.35)
CIU Short(t)0.550.71
(1.27)(1.41)
CIU Short(t−1)−0.12−0.06
(−0.32)(−0.14)
Observations23232323
R20.5600.3850.6170.492
  1. *p < 0.10, **p < 0.05, ***p < 0.01. Standardized beta coefficients; t statistics in parentheses.

Figure 7: Uncertainty and professional forecaster dispersion.Notes: All series have been normalized by subtracting the mean and dividing by the standard deviation. The consumer inflation uncertainty index at the 1-year horizon is constructed according to the methodology in Binder (2017). The policy uncertainty index data from Baker, Bloom, and Davis (2016) was downloaded at www.policyuncertainty.com. Professional forecaster dispersion is the standard deviation of forecasts from Consensus Forecasters, downloadable in the replication files for Wright (2011) .
Figure 7:

Uncertainty and professional forecaster dispersion.

Notes: All series have been normalized by subtracting the mean and dividing by the standard deviation. The consumer inflation uncertainty index at the 1-year horizon is constructed according to the methodology in Binder (2017). The policy uncertainty index data from Baker, Bloom, and Davis (2016) was downloaded at www.policyuncertainty.com. Professional forecaster dispersion is the standard deviation of forecasts from Consensus Forecasters, downloadable in the replication files for Wright (2011) .

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Published Online: 2017-6-23

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