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

Reinhard Ellwanger, Stephen Snudden, Lenin Arango-Castillo ()
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Reinhard Ellwanger, Stephen Snudden, Lenin Arango-Castillo: Wilfrid Laurier University, https://stephensnudden.com/

LCERPA Working Papers from Laurier Centre for Economic Research and Policy Analysis

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.

Keywords: Forecasting and Prediction Methods; Interest Rates; Commodity Prices (search for similar items in EconPapers)
JEL-codes: C1 C53 E47 F37 Q47 (search for similar items in EconPapers)
Date: 2023-12
New Economics Papers: this item is included in nep-ets and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:wlu:lcerpa:bm0142

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