Structural Forecasts for the Danish Economy Using The Dynamic-AAGE Model

Traditionally, applied general equilibrium (AGE) models have been used to answer "what if" questions such as: how different would an economy look in a specified year if a policy change, or some other disturbance, had occurred in some preceding year. Typically there has been no emphasis on forecasting how the economy would look in the year of interest in the absence of the shock, or on tracing the economy's adjustment path from the time when the shock occurred to the year of interest. In recent years, the MONASH AGE model of the Australian economy has been used to make realistic forecasts for the economy at a high level of detail over periods of policy relevance (say up to 10 years). The forecasting methodology developed for MONASH has now been applied to Dynamic-AAGE, the dynamic version of the Agricultural Applied General Equilibrium (AAGE) model of the Danish economy. The starting point for the Dynamic-AAGE forecasts is a set of scenarios for the macro economy supplied by a specialist-forecasting agency, currently the Danish Economic Council. Expert forecasts for major agricultural commodities and a detailed scenario on changes in technologies and household preferences are also drawn upon. The role of DynamicAAGE is to forecast a microeconomic picture for the Danish economy that is consistent with the macroeconomic scenarios and other inputs The objective of this paper is to outline the forecasting methodology adopted in DynamicAAGE, and to describe the latest set of forecasts derived from the model. These forecasts have two main purposes. First they provide information to organisations that have to make decisions requiring views about the likely future structure of the economy. Prime examples are multi-industry, businesses concerned with the allocation of their resources, educational and training authorities concerned with anticipating chances in the allocation of the labour force, and the government concerned with the regional development of public infrastructure. The second main purpose of these forecasts is to provide a realistic base case from which to calculate the answers to the traditional "what if" questions.