TY  - BOOK
AU  - Eickmeier,Sandra
AU  - Ng,Tim
TI  - Forecasting national activity using lots of international predictors: an application to New Zealand 
T2  - Discussion paper. Series 1: economic studies
SN  - 9783865585165
U1  - 338.5442 21
PY  - 2009///
CY  - Frankfurt am Main
PB  - Deutsche Bundesbank
KW  - PREVISIONES ECONOMICAS
KW  - NUEVA ZELANDIA
KW  - PREDICCIONES ECONOMICAS
KW  - PRONOSTICOS ECONOMICOS
N1  - Bibliografía: p. 24-26; 1. Introduction -- 2. Related literature -- 3. Methodology: Forecasting setup -- Data-rich methods -- Shrinkage methods -- Factor methods -- Trade-weighting approaches to summarising international data -- Strength and weaknesses of the various approaches -- 4. Data -- 5. Forecasting results, using New Zealand data only -- 6. The relevance of different classes of international data over the sample -- 7. Conclusions
N2  - We look at how large international datasets can improve forecasts of national activity. We use the case of New Zealand, an archetypal small open economy. We apply "data-rich" factor and shrinkage methods to tackle the problem of efficiently handling hundreds of predictor data series from many countries. The methods covered are principal components, targeted predictors, weighted principal components, partial least squares, elastic net and ridge regression. Using these methods, we assess the marginal predictive content of international data for New Zealand GDP growth. We find that exploiting a large number of international predictors can improve forecasts of our target variable, compared to more traditional models based on small datasets. This is in spite of New Zealand survey data capturing a substantial proportion of the predictive information in the international data. The largest forecasting accuracy gains from including international predictors are at longer forecast horizons. The forecasting performance achievable with the data-rich methods differs widely, with shrinkage methods and partial least squares performing best. We also assess the type of international data that contains the most predictive information for New Zealand growth over our sample
UR  - https://econstor.eu/bitstream/10419/27665/1/200911dkp.pdf
ER  -