Interest Rate on Excess Reserves
Why Use This Data Source In Your Models?
Interest rate on excess reserves is used in coordination with the Fed funds rate to encourage bank behavior that supports Federal Reserve targets. This indicates whether things are trending toward recession or inflation.
Interest Rate on Excess Reserves
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Automated Data Profiling
Suggested Treatment:
Grain Transformation:
Source:
Board of Governors of Fed Reserve System
Release:
Selected Interest Rates
Units:
Percent, Not Seasonally Adjusted
Frequency:
Daily, 7-Day
Available Through:
07/28/2021
Suggested Treatment:
The data shows auto correlation and a non-normal distribution. The data should be differenced. While the Order Norm transformation, provides the best normality, the Square Root variable will also perform well.
Grain Transformation:
Data is unable to be distributed by time or geography. The roll up method used is Weighted Average.
Auto Correlation Analysis:
Data shows auto correlation indicating a need for differencing
The ACF indicates 1 order differencing is appropriate.
Following first order differencing, no further differencing is required based on the differenced ACF at lag one of 0.00
Trend Analysis:
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 3.74 p-value = 0.01 indicates that the data is not stationary.
Distribution Analysis:
The Shapiro-Wilk test returned W = 0.76 with a p-value =0.00 indicating the data does not follow a normal distribution.
A skewness score of 1.07 indicates the data are substantially skewed.
Hartigan's dip test score of 0.07 with a p-value of 0.00 inidcates the data is multimodal
Statistics (Pearson P/ df, lower => more normal)
Auto Correlation Function
Auto Correlation Function After Differencing
Partial Auto Correlation Function
Seasonal Impact
Seasonal and Trend Decompostion
Citation:
Board of Governors of the Federal Reserve System (US), Interest Rate on Excess Reserves [IOER], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/IOER, December 16, 2019.