Estimating and Forecasting Aggregate Productivity Growth Trends in the US and Germany

Arbeitspapier

This paper addresses the issue of estimating and forecasting productivity growth trends in the US and Germany from the perspective of a business cycle researcher who wants to use the available information in time series of aggregate labor productivity to derive a model for short- and/or long-term forecasts of labour productivity. We will use stability tests and a deterministic model with structural breaks that is estimated using the methods mentioned in Hansen (2001). The methodological approach also draws on Gordon (2003) using a Kalman filter specification. We discuss the implications of unit-root assumptions for long-term forecasts and argue in favor of a near unit-root modelling. That implies a convergence of productivity growth rates in both countries within the next 15 years.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: DIW Discussion Papers ; No. 471

Classification and Topics

• Classification: Wirtschaft ; Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Macroeconomics: Production ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications ; Prices, Business Fluctuations, and Cycles: General (includes Measurement and Data)

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Erber, Georg ; Fritsche, Ulrich
• Event: Veröffentlichung
• (who): Deutsches Institut für Wirtschaftsforschung (DIW)
• (where): Berlin
• (when): 2005

Further information

• Handle: http://hdl.handle.net/10419/18322
• Last update: 10.03.2025, 11:44 AM CET