Credit Market Conditions and the Bank's Forecast Errors | RDP 2020-01: Credit Spreads, Monetary Policy and the Price Puzzle

RDP 2020-01: Credit Spreads, Monetary Policy and the Price Puzzle 4. Credit Market Conditions and the Bank's Forecast Errors

Benjamin Beckers

January 2020

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The previous section has shown that the cash rate responds strongly to money and credit market conditions. If these conditions ease and credit intermediation and credit supply to the real economy increases, the cash rate increases. Hence, the first condition for the proposed explanation for the price puzzle is fulfilled. However, for this relationship to explain the price puzzle, easy credit market conditions also need to provide additional information about higher future inflation over and above what is captured in the Bank's forecasts for inflation.

I will test whether the financial market indicators used in the previous section systematically explain the Bank's inflation forecast errors. However, to shed light on any differential effects of monetary policy on inflation, output and unemployment as later estimated using the original BT or the new credit spreads-augmented (BT-CS) monetary policy shock series, I will test for efficiency of the Bank's forecasts for all of these three variables, that is

e_{t + h | t} = \hat{α} + \sum_{i = 1}^{4} {\hat{ρ}}_{i} e_{t - i | t - h - i} + Z_{t - 1} \hat{β} + \hat{γ} C S_{t - 1} + {\hat{u}}_{t + h}

where $e_{t + h | t}$ is the forecast error of annualised underlying inflation, the unemployment rate, or annualised real GDP growth. I define the forecast error of the forecast made in period t and realised in period t + h as $e_{t + h | t} = y_{t + h} - y_{t + h | t}^{f c}$ . A positive error for inflation thus means that inflation printed higher than forecast by the Bank. I regress this error on previous errors as available when the forecast was made $(e_{t - i | t - h - i})$ and on each of the financial market indicators separately. I also include past lags of quarterly inflation, output growth and the change in the unemployment rate in the vector Z_{t – 1}. Throughout, I account for the real-time availability of the data as much as possible.^[21]

4.1 Inflation Forecast Errors

In line with the Board's response to domestic money and credit market spreads, I find that both these spreads explain the Bank's forecast errors for inflation to a considerable extent and in the expected direction (Table 4, columns (2), (3) and (6)). Easier domestic credit market conditions (lower spreads) predict inflation to print higher than forecast by the Bank. While the coefficient on the business lending spread is no longer statistically significant when jointly added with the money market spread, the overall explanatory power increases to 20 per cent compared to 12 per cent for the model only including money market spreads. In contrast, the US corporate bond spread and the US VIX provide little predictive information for inflation over and above the Bank's forecasts (columns (4) and (5)). Similarly, the Bank's forecasts also make efficient use of past forecast errors and all available macroeconomic data captured in the controls. Finally, the results are strongest in terms of marginal predictive power for the one-year horizon but also hold to a similar degree for horizons from two quarters onwards (see Table D2).^[22] In conclusion, both conditions explaining why the RR approach delivers the price puzzle are fulfilled.

Table 4: Credit Market Conditions and One-year-ahead Inflation Forecast Errors
1994:Q1–2018:Q4
Predictor	(1)	(2)	(3)	(4)	(5)	(6)
Constant	−0.35	−0.18	0.37	−0.12	−0.27	0.44
$π_{t - 1}$	0.40	0.46	0.34	0.41	0.41	0.39
$Δ u r_{t - 1}$	−0.08	0.00	−0.01	−0.01	−0.08	0.05
$Δ g d p_{t - 1}$	0.11	0.08	0.12	0.09	0.11	0.10
$c s_{t}^{M M}$		−0.84**				−0.65**
$c s_{t}^{L B}$			−0.33*			−0.29
$c s_{t}^{U S B A A}$				−0.09
$c s_{t}^{U S V I X}$					−0.01
Observations	91	91	91	91	91	91
R ²	0.077	0.124**	0.172***	0.089	0.081	0.199***
F-statistic	0.98	1.46	2.12**	1.00	0.90	2.24**
Notes: The regression includes four lags of the dependent variable (not shown); statistical significance of the marginal predictive power (difference in R ²) of credit market indicators relative to the benchmark model (regression (1)) is assessed using a likelihood ratio test; joint significance of all predictors (including AR terms) is assessed using an F-test; see Table 1 for further notes

4.2 Unemployment and GDP Forecast Errors

Since money and credit market conditions provide additional information about future inflation, it is reasonable to expect this to be the case for other variables that the Bank may target. This is particularly the case since the Bank's forecasts for inflation are informed by forecasts for a range of indicators, most importantly the unemployment rate as an indicator of labour market tightness (Ballantyne et al 2019).

However, I find little evidence that credit market indicators provide additional information for the unemployment forecast (Table 5). I show the results for all horizons for the specification including both domestic credit market measures only: as a) these measures predict inflation, and b) these measures are most important for explaining any cash rate changes.^[23] The coefficients on both credit market indicators are insignificant throughout, and with the exception of the two-year horizon they do not improve the model fit significantly. In contrast, there is some evidence that the spread between business lending rates and the money market rate can provide additional predictive information for GDP forecasts over the short- to medium-term horizons (Table 6). However, the coefficients are positive which is surprising and at odds with the findings in the literature (i.e. Gilchrist and Zakrajšek (2012) and López-Salido et al (2017), and – to the extent that credit spreads may capture sentiment or risks around the Bank staff's central forecasts – also Sharpe et al (2017)). Thus, my results suggest that tighter credit conditions are associated with lower inflation (as expected) but higher economic growth than forecast. Reconciling these conflicting findings is difficult and left to future research.^[24]

Table 5: Credit Market Conditions and Unemployment Forecast Errors
1994:Q1–2018:Q4
Predictor	Horizon (in quarters)
Predictor	1	2	4	6	8
Constant	−0.33***	−0.27*	−0.57***	−0.42	−2.08***
$π_{t - 1}$	0.32*	0.27*	0.49*	0.50*	−0.05
$Δ u r_{t - 1}$	−0.34**	−0.34**	−0.43	−0.26	−0.92*
$Δ g d p_{t - 1}$	−0.07	−0.13***	−0.06	−0.08	0.32***
$c s_{t}^{M M}$	0.12	0.04	−0.24	−0.86**	0.33
$c s_{t}^{L B}$	0.01	−0.03	−0.03	−0.06	0.70
Observations	96	95	91	87	70
R ²	0.368	0.187	0.190	0.197	0.264**
Note: See notes for Table 4

Table 6: Credit Market Conditions and GDP Forecast Errors
1994:Q1–2018:Q4
Predictor	Horizon (in quarters)
Predictor	1	2	4	6	8
Constant	−1.26***	−1.61***	−2.12***	−0.88*	2.43***
$π_{t - 1}$	−0.08	0.10	0.10	0.22	0.41
$Δ u r_{t - 1}$	1.07**	1.34**	0.49	0.47	−0.92*
$Δ g d p_{t - 1}$	−0.49**	−0.49***	0.04	−0.07	−0.38*
$c s_{t}^{M M}$	1.44	1.12**	−0.18	−0.54	−1.29
$c s_{t}^{L B}$	−0.13	0.18*	0.87	1.00***	0.11
Observations	96	95	91	87	71
R ²	0.401**	0.242	0.238***	0.206***	0.198
Note: See notes for Table 4

For the following analysis on the role of credit market conditions for resolving the price puzzle, however, both necessary conditions are fulfilled. The Bank raises the cash rate as credit conditions ease, and easier credit conditions predict inflation to print higher than forecast by the Bank. These relationships are also in line with credit supply shocks being key drivers of the business cycle (Gilchrist and Zakrajšek 2012; López-Salido et al 2017), and the Board responding to this information over and above the Bank's forecasts (Evans et al 2015; Adrian and Duarte 2016; Caggiano et al 2018; Caldara and Herbst 2019). As credit conditions have less predictive content for unemployment and GDP over and above the Bank's forecasts, my results are less likely to explain any potential biases in the estimated responses of these variables.

Footnotes

I account for lags in data availability for inflation, unemployment and real GDP but not for revisions. Instead, I use final revised data as available in 2019:Q1 in both Z_{t –1} and when computing the forecast error. [21]

The results are also robust to including the expected cash rate change as a predictor for inflation forecasts. [22]

I find that the US VIX has considerable predictive content for unemployment forecast errors. However, the cash rate does not appear to respond to the US VIX over and above domestic credit spreads, and hence this additional predictive content should not introduce any systematic bias to the estimated unemployment response to cash rate changes. [23]

The positive association between lending spreads and economic activity could reflect increased credit demand to finance investment. Later, as investment comes on line, the productive capacity and aggregate supply increase which may lower prices. However, investment may also lift labour productivity and thereby wages, aggregate demand and inflation. [24]