Synchronized Time Series Models for National Accounts Data

We develop a multivariate time series model for the complete national accounts dataset which typically consists of at least nine variables. The dynamic features of the variables are characterized by a set of unobserved components that evolve stochastically over time and may represent trend, cyclical, and seasonal effects. The components can be uniquely associated with a single variable or can be common to multiple variablesThe model treats expenditure and production variables jointly, subject to accounting-identity constraints, ensuring that their gross domestic product aggregates are consistent in both mean and variance by construction. The model constraints can be treated naturally within the Kalman filter. Parameter estimation is based on exact maximum likelihood which is feasible despite the high-dimensional parameter vector. This model-based framework allows for synchronized nowcasting, forecasting, seasonal adjustment, trend and cycle extraction for national accounts variables, including gross domestic product. We show that the statistical precision of signal extraction and forecasting improves when our framework is used instead of univariate and unconstrained model alternatives. When the model includes a common cycle component for all national accounts variables, it can be interpreted as a macroeconomic cycle indicator at current prices. We empirically illustrate our model-based methodology for data from national accounts of Germany, Italy, and the Netherlands, and show its significance for both official statistics and macroeconomic policy-making.