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CPI Inflation Rate

Linear model of CPI Inflation Rate at period t from the other tracked series and the LN_ / squared transforms described on the Regression Models index: Y(t) = Σ ai Xi(t−6).

Integrated series (S&P 500, Dow, Home Price Index, Commercial RE) are fit as ΔY. Quarterly series use one row per quarter.

Terms are added one at a time to minimize training AIC, at most min(5, n/8) predictors, one transform per driver, and only when the term’s p-value is at most 0.1 and |coefficient| is greater than 1.0e-10. A candidate correlated above 0.8 with Y or with an already selected X is skipped.

LN_ and squared predictors are clamped to their training range. Coefficients smaller than 0.001 are shown in scientific notation.

Each section is one quarter in a one-year walk (4 experiments). The newest label is the current quarter (3Q2026). The test window is the prior 6 quarters of Y; the train window is the five years (20 quarters) before that. Independents are two quarters (six months) older than Y. Dates on this page are quarter-end observations.

Charts start in January 2020 and plot published prints only (not carried-forward extra samples). Fitted (train), fitted (test), and forecast are joined at the window boundaries so the line is continuous through the label quarter. The OLS test/forecast is shown when its test MAE beats or matches a last-print (or zero-change) baseline.

An intercept-only heading (for example “Δ SP500 = 40.657”) means no lagged series improved training AIC by at least 2 after the intercept, so the model is the training-mean change (a random walk with drift).

Test MAE is omitted when every print in the test window is a CCAR extra sample rather than a published observation.

3Q2026: CPI Inflation Rate = -2.721 + 3.359 × LN_Nominal GDP growth

Train: 1Q2020 – 4Q2024 (18 observations). Test: 1Q2025 – 2Q2026 (6 observations). Independents are dated two quarters (six months) before each Y period.

Regression for CPI Inflation Rate
Dependent variable (+/- SE):
CPI Inflation Rate
Constant-2.721 (+/- 2.675)
p = 0.325
LN_Nominal GDP growth3.359 (+/- 1.244)
p = 0.016**
Observations18
R20.313
Adjusted R20.270
Residual Std. Error2.809 (df = 16)
F Statistic7.292** (df = 1; 16)
Note:*p<0.1; **p<0.05; ***p<0.01

Test period (1Q2025 – 2Q2026)

MAE1.433
Last-print MAE1.35
RMSE1.902
R2-0.942

Chart forecast uses the last-print baseline (OLS MAE is worse).

R2 impact

Train ΔR2 is the gain when the term was added. Test ΔR2 is the drop if that term is omitted from the frozen coefficients and the reduced model is scored on the test window.

Variable Train ΔR2 Test ΔR2
LN_Nominal GDP growth +0.4463 +19.4000

2Q2026: CPI Inflation Rate = -2.694 + 3.353 × LN_Nominal GDP growth

Train: 4Q2019 – 3Q2024 (18 observations). Test: 4Q2024 – 1Q2026 (6 observations). Independents are dated two quarters (six months) before each Y period.

Regression for CPI Inflation Rate
Dependent variable (+/- SE):
CPI Inflation Rate
Constant-2.694 (+/- 2.644)
p = 0.324
LN_Nominal GDP growth3.353 (+/- 1.233)
p = 0.016**
Observations18
R20.316
Adjusted R20.273
Residual Std. Error2.807 (df = 16)
F Statistic7.394** (df = 1; 16)
Note:*p<0.1; **p<0.05; ***p<0.01

Test period (4Q2024 – 1Q2026)

MAE0.821
Last-print MAE1.133
RMSE0.873
R2-0.726

Chart forecast uses the OLS fit (beats last-print MAE).

R2 impact

Train ΔR2 is the gain when the term was added. Test ΔR2 is the drop if that term is omitted from the frozen coefficients and the reduced model is scored on the test window.

Variable Train ΔR2 Test ΔR2
LN_Nominal GDP growth +0.4395 +70.5976

1Q2026: CPI Inflation Rate = -125.522 − 1.331 × LN_3-month Treasury Yield + 22.504 × LN_Commercial Real Estate Price Index

Train: 3Q2019 – 2Q2024 (18 observations). Test: 3Q2024 – 4Q2025 (6 observations). Independents are dated two quarters (six months) before each Y period.

Regression for CPI Inflation Rate
Dependent variable (+/- SE):
CPI Inflation Rate
Constant-125.522 (+/- 36.721)
p = 0.004***
LN_3-month Treasury Yield-1.331 (+/- 0.299)
p = 0.0005***
LN_Commercial Real Estate Price Index22.504 (+/- 6.397)
p = 0.004***
Observations18
R20.621
Adjusted R20.571
Residual Std. Error1.861 (df = 15)
F Statistic12.292*** (df = 2; 15)
Note:*p<0.1; **p<0.05; ***p<0.01

Test period (3Q2024 – 4Q2025)

MAE1.829
Last-print MAE1.3
RMSE2.086
R2-5.034

Chart forecast uses the last-print baseline (OLS MAE is worse).

R2 impact

Train ΔR2 is the gain when the term was added. Test ΔR2 is the drop if that term is omitted from the frozen coefficients and the reduced model is scored on the test window.

Variable Train ΔR2 Test ΔR2
LN_3-month Treasury Yield +0.3084 -4.4490
LN_Commercial Real Estate Price Index +0.3126 +23491.7010

4Q2025: CPI Inflation Rate = -5.538 − 1.267 × LN_3-month Treasury Yield + 9.470e-5 × Commercial Real Estate Price Index²

Train: 2Q2019 – 1Q2024 (18 observations). Test: 2Q2024 – 3Q2025 (6 observations). Independents are dated two quarters (six months) before each Y period.

Regression for CPI Inflation Rate
Dependent variable (+/- SE):
CPI Inflation Rate
Constant-5.538 (+/- 3.135)
p = 0.098*
LN_3-month Treasury Yield-1.267 (+/- 0.321)
p = 0.002***
Commercial Real Estate Price Index20.0001 (+/- 0.00003)
p = 0.010***
Observations18
R20.565
Adjusted R20.506
Residual Std. Error1.993 (df = 15)
F Statistic9.722*** (df = 2; 15)
Note:*p<0.1; **p<0.05; ***p<0.01

Test period (2Q2024 – 3Q2025)

MAE1.427
Last-print MAE1.4
RMSE1.69
R2-2.958

Chart forecast uses the last-print baseline (OLS MAE is worse).

R2 impact

Train ΔR2 is the gain when the term was added. Test ΔR2 is the drop if that term is omitted from the frozen coefficients and the reduced model is scored on the test window.

Variable Train ΔR2 Test ΔR2
LN_3-month Treasury Yield +0.3117 -2.3195
Commercial Real Estate Price Index² +0.2528 +140.1386