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Seize the Last Day: Period-End-Point Sampling for Forecasts of Temporally Aggregated Data

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Abstract

Economists often need to forecast temporally aggregated data, such as monthly or quarterly averages. However, when the underlying data is persistent, constructing forecasts with aggregated data is inefficient. We propose a new forecasting method, Period-End-Point Sampling (PEPS), which uses end-of-period data to create point-in-time forecasts for aggregated data. We show that PEPS forecasts rival the accuracy of bottom-up forecasts and substantially outperform forecasts constructed with averaged data. Importantly, the PEPS method allows models to maintain the lower frequency of the forecast target. Real-time forecast applications to monthly nominal 10-year bond yields and the real prices of gasoline and copper find that disaggregated forecasts can outperform the end-of-month no-change forecasts.

Suggested Citation

  • Reinhard Ellwanger, Stephen Snudden, Lenin Arango-Castillo, 2023. "Seize the Last Day: Period-End-Point Sampling for Forecasts of Temporally Aggregated Data," LCERPA Working Papers bm0142, Laurier Centre for Economic Research and Policy Analysis.
  • Handle: RePEc:wlu:lcerpa:bm0142
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    More about this item

    Keywords

    Forecasting and Prediction Methods; Interest Rates; Commodity Prices;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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