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A Monte Carlo Evaluation of Midas and Umidas Models for Mixed-Frequency Forecasting
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This study assesses finite sample forecasting ability of Mixed Data Sampling (MIDAS) models in the framework of a Monte Carlo simulation. Several of the restricted MIDAS specifications are compared to the Unrestricted MIDAS (UMIDAS) model in the context of a controlled mixed-frequency data generating process. Forecast accuracy and efficiency are compared for alternative sample sizes. The results indicate that restricted MIDAS models have consistently higher finite-sample performance than UMIDAS: the Beta MIDAS specification has the best finite-sample performance overall, when the true lag structure is smooth. The results offer good methodological information for the implementation of applied mixed frequency forecasting.
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Title: A Monte Carlo Evaluation of Midas and Umidas Models for Mixed-Frequency Forecasting
Description:
This study assesses finite sample forecasting ability of Mixed Data Sampling (MIDAS) models in the framework of a Monte Carlo simulation.
Several of the restricted MIDAS specifications are compared to the Unrestricted MIDAS (UMIDAS) model in the context of a controlled mixed-frequency data generating process.
Forecast accuracy and efficiency are compared for alternative sample sizes.
The results indicate that restricted MIDAS models have consistently higher finite-sample performance than UMIDAS: the Beta MIDAS specification has the best finite-sample performance overall, when the true lag structure is smooth.
The results offer good methodological information for the implementation of applied mixed frequency forecasting.
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