izvor podataka: poirot

Modeliranje ekonomskog rasta - napredno sekvenciranje i algoritam predviđanja

Modelling Economic Growth - Advanced Sequencing and Forecasting Algorithm

The main objective of the project is to give a final and scientifically based answer to the question whether economic growth can be forecasted and business cycles predicted? A practical objective of the proposed research project is to build an optimal economic growth/cycle model for Croatia using Advanced Growth Sequencing and Forecasting Algorithm (AGSIFAL) that we plan to build. In order to do so, an algorithm (selected from the best of artificial neural network model, structural econometric model, dynamic general equilibrium model, nonlinear econometric model, golden triangle model) for cycle/growth sequencing and forecasting will be developed. Sequencing of the economic growth on a international scale (119 countries) will be performed using business cycle tracer methods and novel golden triangle model developed by Škare (2010). For this purpose, advanced cycle/growth algorithm will be created. Using individual country data as primer, we plan to isolate growth determinants using three dimensional golden triangle model and spectral analysis techniques. Output, inflation and employment form the three dimension of the cycle/growth triangle that will be used for sequencing growth. After a growth sequencing is finished, a best forecasting algorithm is built from the derived growth sequencing bases. Pairing isolated growth determinants help us to rebuild a growth sequence. Using different algorithm models, reconstructed economic growth series is compared to the actual growth series (observed data). Algorithm resulting in the smallest forecast error (RMSE) is selected as optimal to explain economic growth and predict business cycles. EThis conclusion will have important theoretical and policy implications both for the science of economics and economic agents on the market.

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Znanstveno-istraživački projekti





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HRK 600.000,00