دانلود مقاله ISI انگلیسی شماره 45803
عنوان فارسی مقاله

پیش بینی متغیرهای مالی و اقتصاد کلان با استفاده از روش کاهش داده: شواهد تجربی جدید

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
45803 2014 16 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence ☆
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Econometrics, Volume 178, Part 2, January 2014, Pages 352–367

کلمات کلیدی
افزایش - شاخص پراکندگی - خالص الاستیک - پیش بینی - رگرسیون حداقل زاویه - شریان بند غیر منفی - پیش بینی - بررسی واقعیت - رگرسیون ریج
پیش نمایش مقاله
پیش نمایش مقاله پیش بینی متغیرهای مالی و اقتصاد کلان با استفاده از روش کاهش داده: شواهد تجربی جدید

چکیده انگلیسی

In this paper, we empirically assess the predictive accuracy of a large group of models that are specified using principle components and other shrinkage techniques, including Bayesian model averaging and various bagging, boosting, least angle regression and related methods. Our results suggest that model averaging does not dominate other well designed prediction model specification methods, and that using “hybrid” combination factor/shrinkage methods often yields superior predictions. More specifically, when using recursive estimation windows, which dominate other “windowing” approaches, “hybrid” models are mean square forecast error “best” around 1/3 of the time, when used to predict 11 key macroeconomic indicators at various forecast horizons. Baseline linear (factor) models also “win” around 1/3 of the time, as do model averaging methods. Interestingly, these broad findings change noticeably when considering different sub-samples. For example, when used to predict only recessionary periods, “hybrid” models “win” in 7 of 11 cases, when condensing findings across all “windowing” approaches, estimation methods, and models, while model averaging does not “win” in a single case. However, in expansions, and during the 1990s, model averaging wins almost 1/2 of the time. Overall, combination factor/shrinkage methods “win” approximately 1/2 of the time in 4 of 6 different sample periods. Ancillary findings based on our forecasting experiments underscore the advantages of using recursive estimation strategies, and provide new evidence of the usefulness of yield and yield-spread variables in nonlinear prediction model specification.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.