GDP Modelling with Factor Model: an Impact of Nested Data on Forecasting Accuracy
MPRA Paper from University Library of Munich, Germany
Uncertainty associated with an optimal number of macroeconomic variables to be used in factor model is challenging since there is no criteria which states what kind of data should be used, how many variables to employ and does disaggregated data improve factor model’s forecasts. The paper studies an impact of nested macroeconomic data on Latvian GDP forecasting accuracy within factor modelling framework. Nested data means disaggregated data or sub-components of aggregated variables. We employ Stock-Watson factor model in order to estimate factors and to make GDP projections two periods ahead. Root mean square error is employed as the standard tool to measure forecasting accuracy. According to this empirical study we conclude that additional information that contained in disaggregated components of macroeconomic variables could be used to enhance Latvian GDP forecasting accuracy. The efficiency gain improving forecasts is about 0.15-0.20 percentage points of year on year quarterly growth for the forecasting period 1 quarter ahead, but for 2 quarter ahead it’s about half percentage point.
Keywords: Factor model; forecasting; nested data; RMSE. (search for similar items in EconPapers)
JEL-codes: C53 C22 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:30211
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