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dc.contributor.authorHelliesen, Magnus Kvåle
dc.description.abstractThis paper documents the Python class ModelSolver. ModelSolver lets the user define a model object in terms of equations and endogenous variables. It contains methods to solve the model subject to data (in a Pandas DataFrame), as well as analysing the model using graph theory and network plots. What sets ModelSolver apart from other similar solvers is that it does not require the equations of the model to be written in any particular way, or that the user associates equations with endogenous variables. Most other solvers require that either 1) the model is normalised (i.e., that the model is written in terms of endogenous variables), or 2) that the user explicitly associates equations with endogenous variables. This is non-trivial for models with lots of equations. ModelSolver, however, reads equations in whatever form they may be written, and performs the necessary analyses, without any input from the user other than lists of equations and endogenous variables. ModelSolver was developed to facilitate solving an input-output model for the Norwegian monthly national accounts. 1 It analyses and solves the model’s more than 15,500 equations over more than 30 periods in under a minute on a laptop computer.en_US
dc.publisherStatistisk sentralbyråen_US
dc.rightsNavngivelse-Ikkekommersiell 4.0 Internasjonal*
dc.titleDocumentation of ModelSolver. A Python class for analysing dynamic algebraic modelsen_US
dc.typeWorking paperen_US
dc.rights.holder© Statistisk sentralbyråen_US

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Navngivelse-Ikkekommersiell 4.0 Internasjonal
Med mindre annet er angitt, så er denne innførselen lisensiert som Navngivelse-Ikkekommersiell 4.0 Internasjonal