Bootstrap multi-step forecasts of non-Gaussian VAR models

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Boosting multi-step autoregressive forecasts

Multi-step forecasts can be produced recursively by iterating a one-step model, or directly using a specific model for each horizon. Choosing between these two strategies is not an easy task since it involves a trade-off between bias and estimation variance over the forecast horizon. Using a nonlinear machine learning model makes the tradeoff even more difficult. To address this issue, we propo...

متن کامل

Boosting multi-step autoregressive forecasts

Multi-step forecasts can be produced recursively by iterating a one-step model, or directly using a specific model for each horizon. Choosing between these two strategies is not an easy task since it involves a trade-off between bias and estimation variance over the forecast horizon. Using a nonlinear machine learning model makes the tradeoff even more difficult. To address this issue, we propo...

متن کامل

Comparison of Forecasts Using the Bootstrap

1 Main Ideas Quantitative accuracy measures serve as gauges of forecast performance. There are many types of forecasts: point or multivariate ((eld), probabilistic or categorical. A wealth of accuracy measures exist for each type of forecast (see Wilks, 1996, chap. 7 for a review of many). Let us suppose we have a set of 24-hour high temperature forecasts for a city and their corresponding obse...

متن کامل

Identifiability of Non-Gaussian Structural VAR Models for Subsampled and Mixed Frequency Time Series

Causal inference in multivariate time series is confounded by subsampling in time between the true causal scale and the observed data sampling rate. In practice, this presents challenges for inferring causal interaction between time series due to differences in sampling rates across time series and generally low sampling rates due to technological limitations. To determine instantaneous and lag...

متن کامل

Non-rigid multi-modal object tracking using Gaussian mixture models

This work presents an approach to visual tracking based on dividing a target into multiple regions, or fragments. The target is represented by a Gaussian mixture model in a joint featurespatial space, with each ellipsoid corresponding to a different fragment. The fragment set and its cardinality are automatically adapted to the image data using an efficient region-growing procedure and updated ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: International Journal of Forecasting

سال: 2015

ISSN: 0169-2070

DOI: 10.1016/j.ijforecast.2014.04.001