Bayesian Model and Electricity Consumption: A Two-Stage Approach to Coincident and Leading Indicators
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Using monthly electricity consumption data, we constructed a Bayesian model to calculate coincident and leading economic indicators. The dynamic latent common factor model is used to measure the current conditions of the economy, as revealed by electricity consumption across sectors. However, as aggregation of electricity consumption has a one month time lag, we developed a two-stage Bayesian estimation procedure in which forecasting electricity consumption and extracting the common factor are systematically implemented. We applied our method to the Korean economy and found that our experimental indicators can not only trace but also describe the intricate phases of the Korean business cycle.
Abstract
Using monthly electricity consumption data, we constructed a Bayesian model to calculate coincident and leading economic indicators. The dynamic latent common factor model is used to measure the current conditions of the economy, as revealed by electricity consumption across sectors. However, as aggregation of electricity consumption has a one month time lag, we developed a two-stage Bayesian estimation procedure in which forecasting electricity consumption and extracting the common factor are systematically implemented. We applied our method to the Korean economy and found that our experimental indicators can not only trace but also describe the intricate phases of the Korean business cycle.
참고문헌 (36)
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