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FACTOR MODELS AND TIME‐VARYING PARAMETER FRAMEWORK FOR FORECASTING EXCHANGE RATES AND INFLATION: A SURVEY
Author(s) -
Kavtaradze Lasha,
Mokhtari Manouchehr
Publication year - 2018
Publication title -
journal of economic surveys
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.657
H-Index - 92
eISSN - 1467-6419
pISSN - 0950-0804
DOI - 10.1111/joes.12205
Subject(s) - econometrics , inflation (cosmology) , predictive power , economics , exchange rate , benchmark (surveying) , bayesian probability , context (archaeology) , portfolio , state space representation , factor analysis , dynamic factor , statistics , computer science , mathematics , financial economics , finance , paleontology , philosophy , physics , geodesy , epistemology , algorithm , theoretical physics , biology , geography
Abstract A survey of models used for forecasting exchange rates and inflation reveals that the factor‐based and time‐varying parameter or state space models generate superior forecasts relative to all other models. This survey also finds that models based on Taylor rule and portfolio balance theory have moderate predictive power for forecasting exchange rates. The evidence on the use of Bayesian Model Averaging approach in forecasting exchange rates reveals limited predictive power, but strong support for forecasting inflation. Overall, the evidence overwhelmingly points to the context of the forecasts, relevance of the historical data, data transformation, choice of the benchmark, selected time horizons, sample period and forecast evaluation methods as the crucial elements in selecting forecasting models for exchange rate and inflation.