Differencing versus nondifferencing in factor‐based forecasting

Autor: In Choi, Hanbat Jeong
Rok vydání: 2020
Předmět:
Zdroj: Journal of Applied Econometrics. 35:728-750
ISSN: 1099-1255
0883-7252
DOI: 10.1002/jae.2777
Popis: This paper studies performance of factor‐based forecasts using differenced and nondifferenced data. Approximate variances of forecasting errors from the two forecasts are derived and compared. It is reported that the forecast using nondifferenced data tends to be more accurate than that using differenced data. This paper conducts simulations to compare root mean squared forecasting errors of the two competing forecasts. Simulation results indicate that forecasting using nondifferenced data performs better. The advantage of using nondifferenced data is more pronounced when a forecasting horizon is long and the number of factors is large. This paper applies the two competing forecasting methods to 68 I(1) monthly US macroeconomic variables across a range of forecasting horizons and sampling periods. We also provide detailed forecasting analysis on US inflation and industrial production. We find that forecasts using nondifferenced data tend to outperform those using differenced data.
Databáze: OpenAIRE
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