Forecasting with instabilities: An application to DSGE models with financial frictions

Cardani, Roberta; Paccagnini, Alessia; Villa, Stefania

doi:10.1016/j.jmacro.2019.103133

Cited by 11 publications

(3 citation statements)

References 64 publications

(76 reference statements)

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“…The point forecast is implemented by conducting both static and dynamic analysis, as described in Cardani et al (2019). If the static analysis provides a unique forecast value, the dynamic analysis describes the evolution of the prediction along the time dimension to investigate possible time-varying effects.…”

Section: Forecasting With Dsge Models 20mentioning

confidence: 99%

“…Following Clements and Hendry (1998), Kolasa et al (2012) apply the standard forecast unbiased test to assess if DSGEs are good forecasters in the absolute sense. The accuracy of the dynamic analysis is based on the Fluctuation Test (for some DSGE applications, see: Giacomini and Rossi, 2016;Cardani et al, 2019;Boneva et al, 2019). This test is based on the calculation of RMSEs that are assessed to investigate if the forecasting performance can be influenced by instabilities in the model parameters.…”

Section: Forecasting With Dsge Models 20mentioning

confidence: 99%

“…The forecasting performance is evaluated using rolling or recursive (expanded) prediction windows (for a discussion, see Cardani et al, 2015). DSGEs are usually estimated using revised data, but several studies propose better results estimating the models using real-time data (see, for example, Del Negro and Schorfheide, 2013; Wolters, 2015; Kolasa and Rubaszek, 2015b;Cardani et al, 2019).…”

Section: Forecasting Gdp and Inflation 94mentioning

confidence: 99%

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Forecasting: theory and practice

Petropoulos,

Apiletti,

Assimakopoulos

et al. 2020

Preprint

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Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice.

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Section: Forecasting With Dsge Models 20mentioning

confidence: 99%