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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 modeling framework. Nested data means disaggregated data or sub-components of aggregated variables. The authors 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.
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