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Nominal GDP growth

Linear model of Nominal GDP growth 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: Nominal GDP growth = -383.733 + 0.009 × Market Volatility Index² + 35.864 × LN_Dow Total Stock Market Index

Train: 1Q2020 – 4Q2024 (20 observations). Test: 1Q2025 – 2Q2026 (6 observations). Independents are dated two quarters (six months) before each Y period.

Regression for Nominal GDP growth
Dependent variable (+/- SE):
Nominal GDP growth
Constant-383.733 (+/- 126.089)
p = 0.008***
Market Volatility Index20.009 (+/- 0.002)
p = 0.00004***
LN_Dow Total Stock Market Index35.864 (+/- 11.777)
p = 0.008***
Observations20
R20.656
Adjusted R20.615
Residual Std. Error7.340 (df = 17)
F Statistic16.198*** (df = 2; 17)
Note:*p<0.1; **p<0.05; ***p<0.01

Test period (1Q2025 – 2Q2026)

MAE11.973
Last-print MAE2.117
RMSE14.058
R2-59.310

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
Market Volatility Index² +0.4681 -57.8222
LN_Dow Total Stock Market Index +0.1877 +43740.1671

2Q2026: Nominal GDP growth = -256.458 + 0.007 × Market Volatility Index² + 30.931 × LN_SP500 Stock Price Index

Train: 4Q2019 – 3Q2024 (20 observations). Test: 4Q2024 – 1Q2026 (6 observations). Independents are dated two quarters (six months) before each Y period.

Regression for Nominal GDP growth
Dependent variable (+/- SE):
Nominal GDP growth
Constant-256.458 (+/- 102.693)
p = 0.024**
Market Volatility Index20.007 (+/- 0.001)
p = 0.0001***
LN_SP500 Stock Price Index30.931 (+/- 12.377)
p = 0.023**
Observations20
R20.612
Adjusted R20.566
Residual Std. Error7.797 (df = 17)
F Statistic13.401*** (df = 2; 17)
Note:*p<0.1; **p<0.05; ***p<0.01

Test period (4Q2024 – 1Q2026)

MAE8.147
Last-print MAE2.067
RMSE10.58
R2-33.007

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
Market Volatility Index² +0.4693 -33.0056
LN_SP500 Stock Price Index +0.1426 +19452.0496

1Q2026: Nominal GDP growth = -10.843 + 0.549 × Market Volatility Index

Train: 3Q2019 – 2Q2024 (20 observations). Test: 3Q2024 – 4Q2025 (6 observations). Independents are dated two quarters (six months) before each Y period.

Regression for Nominal GDP growth
Dependent variable (+/- SE):
Nominal GDP growth
Constant-10.843 (+/- 4.849)
p = 0.039**
Market Volatility Index0.549 (+/- 0.136)
p = 0.001***
Observations20
R20.475
Adjusted R20.446
Residual Std. Error8.807 (df = 18)
F Statistic16.300*** (df = 1; 18)
Note:*p<0.1; **p<0.05; ***p<0.01

Test period (3Q2024 – 4Q2025)

MAE6.425
Last-print MAE2.15
RMSE7.508
R2-18.768

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
Market Volatility Index +0.4752 +70.1859

4Q2025: Nominal GDP growth = -11.692 + 0.562 × Market Volatility Index

Train: 2Q2019 – 1Q2024 (20 observations). Test: 2Q2024 – 3Q2025 (6 observations). Independents are dated two quarters (six months) before each Y period.

Regression for Nominal GDP growth
Dependent variable (+/- SE):
Nominal GDP growth
Constant-11.692 (+/- 4.962)
p = 0.030**
Market Volatility Index0.562 (+/- 0.137)
p = 0.001***
Observations20
R20.483
Adjusted R20.454
Residual Std. Error8.745 (df = 18)
F Statistic16.810*** (df = 1; 18)
Note:*p<0.1; **p<0.05; ***p<0.01

Test period (2Q2024 – 3Q2025)

MAE5.395
Last-print MAE1.833
RMSE5.708
R2-10.822

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
Market Volatility Index +0.4829 +96.0303