Forecasting and Structural Analysis with Contemporaneous Aggregates of Time Series Data

• Applicant: Professor Dr. Ralf Brüggemann
• Subject Area: Statistics and Econometrics
• Term: from 2012 to 2016
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 216732382

Time series data in economics and social sciences are often constructed by aggregating over cross-sectional units such as individuals, sectors, states or countries. This type of aggregation is referred to as contemporaneous aggregation. Examples that highlight the practical importance include the sectoral aggregation in the construction of quarterly national accounts data, aggregation of different price index subcomponents, the aggregation of micro-based macroeconomic models and the construction of area-wide time series e.g. for the Euro area. Contemporaneous aggregation may change the dynamic properties (e.g. the persistence) of the time series data in a substantial way, such that often more complex time series models are needed for the aggregate. In this project we explore the consequences of contemporaneous aggregation for econometric forecasting and structural analysis. We focus on issues that arise (1) when there are structural changes in the data generating processes of some cross-sectional units and (2) when the time series information is missing for some cross-sectional units. These problems are of immense practical relevance and are likely to bias the information contained in the aggregate series and any econometric analysis based on this data. Alternative aggregation methods and alternatives to aggregation are developed and their usefulness in econometric forecasting and policy analysis models for aggregates is analyzed.

• DFG Programme: Research Grants