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Fast sparse regression and classification

Friedman, Jerome H. - In: International Journal of Forecasting 28 (2012) 3, pp. 722-738

Many present day applications of statistical learning involve large numbers of predictor variables. Often, that number is much larger than the number of cases or observations available for training the learning algorithm. In such situations, traditional methods fail. Recently, new techniques...

Persistent link: https://www.econbiz.de/10010573814

Estimating causal effects of credit decisions

Fahner, Gerald - In: International Journal of Forecasting 28 (2012) 1, pp. 248-260

In principle, making credit decisions under uncertainty can be approached by estimating the potential future outcomes that will result from the various decision alternatives. In practice, estimation difficulties may arise as a result of selection bias and limited historic testing. We review some...

Persistent link: https://www.econbiz.de/10010796144

Improving forecasting in an emerging economy, South Africa: Changing trends, long run restrictions and disaggregation

Aron, Janine; Muellbauer, John - In: International Journal of Forecasting 28 (2012) 2, pp. 456-476

Forecasting inflation is particularly challenging in emerging markets, where trade and monetary policy regimes have shifted and the exchange rate and food prices are highly volatile. This study shows that the information in long-run co-integrated relationships for relative prices in South Africa...

Persistent link: https://www.econbiz.de/10011051397

Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models

Li, Jiahan; Chen, Weiye - In: International Journal of Forecasting 30 (2014) 4, pp. 996-1015

such exercises. In this paper, we investigate a category of LASSO-based approaches and evaluate their predictive abilities … forecasts from LASSO-based models with those from dynamic factor models can reduce the mean square forecast error (MSFE) further … LASSO-based models outperform dynamic factor models in the out-of-sample forecast evaluations. Second, by extracting …

Persistent link: https://www.econbiz.de/10011051419

Forecasting with dimension switching VARs

Koop, Gary - In: International Journal of Forecasting 30 (2014) 2, pp. 280-290

This paper develops methods for VAR forecasting when the researcher is uncertain about which variables enter the VAR, and the dimension of the VAR may be changing over time. It considers the case where there are N variables which might potentially enter a VAR and the researcher is interested...

Persistent link: https://www.econbiz.de/10011051433

Forecasting correlated time series with exponential smoothing models

Corberán-Vallet, Ana; Bermúdez, José D.; Vercher, … - In: International Journal of Forecasting 27 (2011) 2, pp. 252-265

This paper presents the Bayesian analysis of a general multivariate exponential smoothing model that allows us to forecast time series jointly, subject to correlated random disturbances. The general multivariate model, which can be formulated as a seemingly unrelated regression model, includes...

Persistent link: https://www.econbiz.de/10011051453

Combining exponential smoothing forecasts using Akaike weights

Kolassa, Stephan - In: International Journal of Forecasting 27 (2011) 2, pp. 238-251

Simple forecast combinations such as medians and trimmed or winsorized means are known to improve the accuracy of point forecasts, and Akaike’s Information Criterion (AIC) has given rise to so-called Akaike weights, which have been used successfully to combine statistical models for inference...

Persistent link: https://www.econbiz.de/10010577333

Forecasting method selection in a global supply chain

Acar, Yavuz; Gardner, Everette S. - In: International Journal of Forecasting 28 (2012) 4, pp. 842-848

In supply chains, forecasting is an important determinant of operational performance, although there have been few studies that have selected forecasting methods on that basis. This paper is a case study of forecasting method selection for a global manufacturer of lubricants and fuel additives,...

Persistent link: https://www.econbiz.de/10010603364