Random Walk Forecasts of Stationary Processes Have Low Bias

Kurt Lunsford and Kenneth D. West

No 23-18, Working Papers from Federal Reserve Bank of Cleveland

Abstract: We study the use of a zero mean first difference model to forecast the level of a scalar time series that is stationary in levels. Let bias be the average value of a series of forecast errors. Then the bias of forecasts from a misspecified ARMA model for the first difference of the series will tend to be smaller in magnitude than the bias of forecasts from a correctly specified model for the level of the series. Formally, let P be the number of forecasts. Then the bias from the first difference model has expectation zero and a variance that is O(1/P-squared), while the variance of the bias from the levels model is generally O(1/P). With a driftless random walk as our first difference model, we confirm this theoretical result with simulations and empirical work: random walk bias is generally one-tenth to one-half that of an appropriately specified model fit to levels.

Keywords: ARMA Models; Overdifferenced; Prediction; Macroeconomic Time Series; Simulation (search for similar items in EconPapers)
JEL-codes: C22 C53 E37 E47 (search for similar items in EconPapers)
Pages: 20
Date: 2023-08-03
New Economics Papers: this item is included in nep-ecm and nep-ets
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DOI: 10.26509/frbc-wp-202318

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