Statistical downscaling techniques are used to translate global climate scenarios into local impact forecasts. In this study, I propose a new algorithm for downscaling by using a linear mixed-effect state-space model (LMESS). The rationale to use this model in a climate data context is that it allows for both time-varying and fixed relations between dependent and explanatory variables. My findings show the importance of identifying the correct random and fixed effects. I develop a new method for selection based on the state-space formulation with fixed parameters by Chow (1984). I apply the proposed methods to climate data at five different weather stations in the Netherlands. My findings show that the LMESS model is not able to consistently outperform a multivariate linear regression forecast method.

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hdl.handle.net/2105/44789
Econometrie
Erasmus School of Economics

Schot, J.H., & Wel, M. van der. (2018, December 13). Linear Mixed-Effect State-Space Forecasting: Completing the algorithm. Econometrie. Retrieved from http://hdl.handle.net/2105/44789