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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Martins, L. F. & Perron, P. (2016). Improved tests for forecast comparisons in the presence of instabilities. Journal of Time Series Analysis. 37 (5), 650-659
Exportar Referência (IEEE) 
L. F. Martins and P. Perron,  "Improved tests for forecast comparisons in the presence of instabilities", in Journal of Time Series Analysis, vol. 37, no. 5, pp. 650-659, 2016
Exportar BibTeX 
@article{martins2016_1735267564517,
	author = "Martins, L. F. and Perron, P.",
	title = "Improved tests for forecast comparisons in the presence of instabilities",
	journal = "Journal of Time Series Analysis",
	year = "2016",
	volume = "37",
	number = "5",
	doi = "10.1111/jtsa.12179",
	pages = "650-659",
	url = "http://onlinelibrary.wiley.com/doi/10.1111/jtsa.12179/abstract"
}
Exportar RIS 
TY  - JOUR
TI  - Improved tests for forecast comparisons in the presence of instabilities
T2  - Journal of Time Series Analysis
VL  - 37
IS  - 5
AU  - Martins, L. F.
AU  - Perron, P.
PY  - 2016
SP  - 650-659
SN  - 0143-9782
DO  - 10.1111/jtsa.12179
UR  - http://onlinelibrary.wiley.com/doi/10.1111/jtsa.12179/abstract
AB  - Of interest is comparing the out-of-sample forecasting performance of two competing models in the presence of possible instabilities. To that effect, we suggest using simple structural change tests, sup-Wald and UDmax for changes in the mean of the loss differences. It is shown that Giacomini and Rossi (2010) tests have undesirable power properties, power that can be low and non-increasing as the alternative becomes further from the null hypothesis. On the contrary, our statistics are shown to have higher monotonic power, especially the UDmax version. We use their empirical examples to show the practical relevance of the issues raised.
ER  -