October 11, 2023
Every month, our QIS Macro Team provides a complementary analysis of trends and predictions based on RavenPack's Inflation Nowcasting model.
By integrating economic activity, labor market conditions, and financial indicators, RavenPack's nowcasts effectively monitored the core CPI inflation rate for July 2023, which maintained proximity to the June rate. Among sentiment factors, influential predictors included employment, retail sales, exports, inventories, and the consumer price index, with consumer price index sentiment having the most significant impact. In the realm of macroeconomic indicators, jobless claims, surveys on financial conditions, and Cass freight were noteworthy, with jobless claims exerting the largest influence.
The headline CPI remained near the June inflation rate, exhibiting a slight uptick in higher inflation. Notably, consumer price index and interest rate were the key drivers among sentiment data, with jobless claims and interest rate as significant contributors in terms of guidance. Consumer price index guidance had the most pronounced impact. Among standard macro indicators, the right tail of the distribution saw greater influence, primarily attributed to grain transportation cost and manufacturing price surveys.
presents the real-time density prediction of core Consumer Price Index (CPI)
Month-Over-Month (MoM) inflation for July 2023. The chart also includes the actual inflation data released on August 10th, 2023,
corresponding to July.
In July, the month-over-month core CPI remained relatively stable, hovering around 0.15. Our predictive distribution for June displayed a mean of 0.21 and a median of 0.23, while for July, these figures were a mean of 0.15 and a median of 0.16.
One interesting observation is the change in the standard deviation of the predictive distribution. In June, the standard deviation stood at 0.27%, indicating relatively higher variability in the predictions. However, this variability decreased significantly to 0.11% in July, implying a more focused distribution of predicted values around the mean.
On the other hand, we noticed changes in the distribution's shape. Specifically, there was an increase in both skewness and excess kurtosis values in July compared to June. This indicates a departure from the normal distribution, with potential asymmetry and heavier tails in the distribution of predicted values for July.
By utilizing the predictive distribution of inflation, we can gauge the probability associated with different inflation states using the Empirical Cumulative Distribution Function (ECDF). These states are classified as follows: Low (<0%), Medium Low (>0% and <0.16%), Medium High (>0.16% and <0.33%), and High (>0.33%). As depicted in Figure 1's second row, the probabilities of observing inflation falling within these predefined states are presented. The analysis of the Empirical Cumulative Distribution Function (ECDF) indicated a decrease in the probabilities associated with the Low and High states of inflation. Simultaneously, it brought to light an increase in the probabilities of the Medium Low state (with the highest probability) and the Medium High state of inflation for July in contrast to June.
(Refer to the
prior report release for more details
In June, the reported headline CPI registered at 0.18%. The median of the predictive distribution was at 0.21%, displaying a positive skewness. Looking forward to July, the predictive distribution's median projected an inflation rate of 0.24%, but the actual inflation rate materialized at 0.17% (refer to
). This alignment with the inflation target is of significance. Additionally, there was a shift in skewness for July compared to the previous month (from 0.2 to -0.1), along with a decrease in the standard deviation (from 0.15% to 0.14%). Meanwhile, the excess kurtosis remained consistent at 1.3.
For July, the Empirical Cumulative Distribution Function (ECDF) revealed a reduction in the probability of observing positive inflation headline in both the Low and Medium Low states compared to June. Conversely, there was an increase in the probabilities linked to the Medium High and High states of inflation (depicted in Figure 2).
The inflation prediction model used in this study relies on the incorporation of sentiment analytics and standard macroeconomic variables. By estimating the impact of each variable on the predictive distribution of inflation, it becomes possible to assess their influence during periods of high inflation. The estimation is conducted using a sample period spanning from December 2021 (when inflation surged and the Fed announced an interest rate hike) to August 2023.
illustrates the aggregated impact of both sentiment and standard macroeconomic variables for core CPI. Notably, both sentiment and standard macro data exhibit a more pronounced marginal contribution on the left tail of the distribution. Furthermore, during this specific time period, the sentiment data appears to have a more substantial impact on the predicted inflation compared to the standard macroeconomic variables.
During this specific time period, the sentiment analytics that have the greatest impact on the predicted inflation are as follows:
In terms of macroeconomic indicators, the following variables have the greatest impact on the predicted inflation during this time period:
illustrates the combined influence of sentiment and standard macroeconomic variables on headline CPI. Notably, standard macro data exhibit a more pronounced contribution to the right tail of the distribution, while sentiment variables have a stronger impact on both the right tail and the median of the distribution. In comparison to the previous month, we noted an augmented contribution to the right tail, particularly for instances of high inflation, from the standard macroeconomic variables.
In contrast to core CPI, the impact of variables on headline CPI is relatively sparse. The predictors with the highest impact are:
Regarding macroeconomic indicators, the following variables exert the most significant influence on the predicted inflation throughout this period:
Please use your business email. If you don't have one, please email us at firstname.lastname@example.org.
By providing your personal information and submitting your details, you acknowledge that you have read, understood, and agreed to our Privacy Statement and you accept our Terms and Conditions. We will handle your personal information in compliance with our Privacy Statement. You can exercise your rights of access, rectification, erasure, restriction of processing, data portability, and objection by emailing us at email@example.com in accordance with the GDPRs. You also are agreeing to receive occasional updates and communications from RavenPack about resources, events, products, or services that may be of interest to you.
Your request has been recorded and a team member will be in touch soon.