U.S. Equity Risk Premium Forecasting Dataset (1990–2024): Implied ERP, Realized Returns, and Macro-Financial Predictors

This dataset contains a comprehensive quarterly panel of U.S. equity risk premium measures and macro-financial predictors covering the period 1990Q1–2024Q4. It was constructed to support the analysis in “Structural Shifts in the Forecasting Dynamics of the U.S. Equity Risk Premium: A Regime-Switching and Structural Break Approach, 1990–2024.”

The dataset includes two principal dependent variables:
(1) Implied Equity Risk Premium (ERP_implied) based on Damodaran’s monthly U.S. ERP estimates, sampled at quarter end;
(2) Realized 1-Year Equity Risk Premium (ERP_realized_1y) computed as the difference between S&P 500 total returns and 3-month Treasury bill returns over a four-quarter horizon.

It also provides the key macro-financial predictors widely used in the forecasting literature, including:
– Valuation ratios: Earnings-Price (EP_ratio), Dividend-Price (DP_ratio)
– Interest-rate variables: 3-month Treasury bill rate, 10-year Treasury yield, term spread, and Moody’s Baa–Treasury credit spread
– Macroeconomic indicators: Real GDP growth, CPI inflation, unemployment rate, and the federal funds rate
– Risk and uncertainty indicators: VIX volatility index and the U.S. Economic Policy Uncertainty (EPU) index
– Realized volatility: Annualized standard deviation of monthly S&P 500 total returns over the prior 12 months.

All variables are aligned to quarterly frequency (end-of-quarter levels for stock-market and ERP series; quarterly averages for flow-type indicators such as VIX, EPU, and macroeconomic rates). The dataset is cleaned, merged, and structured to allow direct replication of forecasting regressions, Bai–Perron structural break tests, and two-state Markov-switching models.

This dataset supports empirical research on the time variation, structural instability, and regime dependence of the U.S. equity risk premium. It may be used for forecasting studies, return predictability analysis, macro-financial modeling, and teaching applications in asset pricing and financial economics.