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on Forecasting |
| By: | Nguyen, BH (Tasmanian School of Business & Economics, University of Tasmania); Zhang, Bo (Business School, Wenzhou University, Wenzhou, Zhejiang Province and Centre for Applied Macroeconomic Analysis (CAMA), Australian National University, Australia) |
| Abstract: | Large Bayesian Vector Autoregressions (BVARs) have been a successful tool in the forecasting literature and most of this work has focused on macroeconomic variables. In this paper, we examine the ability of large BVARs to forecast the real price of crude oil using a large dataset with over 100 variables. We find consistent results that the large BVARs do not beat the BVARs with small and medium sizes for short forecast horizons but offer better forecasts at long horizons. In line with the forecasting macroeconomic literature, we also find that the forecast ability of the large models further improves upon the competing standard BVARs once endowed with flexible error structures. |
| Keywords: | forecasting, non-Gaussian, stochastic volatility, oil prices, big data |
| JEL: | C11 C32 C52 Q41 Q47 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:tas:wpaper:47522&r=for |
| By: | Wang, Xiaohu (Fudan University); Yu, Jun (Singapore Management University); Zhang, Chen (Singapore Management University) |
| Abstract: | This paper examines the performance of alternative forecasting formulaewith the fractional Brownian motion based on a discrete and Önite sample.One formula gives the optimal forecast when a continuous record over theinÖnite past is available. Another formula gives the optimal forecast whena continuous record over the Önite past is available. Alternative discretiza-tion schemes are proposed to approximate these formulae. These alternative discretization schemes are then compared with the conditional expectationof the target variable on the vector of the discrete and Önite sample. It isshown that the conditional expectation delivers more accurate forecasts thanthe discretization-based formulae using both simulated data and daily realizedvolatility (RV) data. Empirical results based on daily RV indicate that theconditional expectation enhances the already-widely known great performanceof fBm in forecasting future RV. |
| Keywords: | Fractional Gaussian noise; Conditional expectation; Anti-persistence; Continuous record; Discrete record; Optimal forecast |
| JEL: | C12 C22 G01 |
| Date: | 2022–10–28 |
| URL: | http://d.repec.org/n?u=RePEc:ris:smuesw:2022_012&r=for |
| By: | Bergeron-Boucher, Marie-Pier; Kjærgaard, Søren |
| Abstract: | Mortality forecasts by age and cause of death are important for more efficient spending on, for example, health care and medical technology. However, there is a reluctance in including the cause of death dimension to the forecast, as forecasts by cause are confronted with many methodological problems. While some of these problems have been addressed in the last two decades, an important remaining issue with forecasts by cause is their inconsistence with all- causes forecasts. This problem relates to how changes in mortality by age and cause interact. So how can we forecast this relation in a coherent manner? To address this problem, we use a model framework based on a Compositional Data Analysis (CoDA) approach which models 1) age and cause simultaneously; 2) cause-of-death distribution within each age group and 3) age-at-death distribution within each cause. We specify multiple models within each of the three frameworks to obtain a better understanding of the age and cause interactions. The results show that forecasting cause-of-death distribution within each age group generally provides the most accurate forecasts and allows for the forecast by cause and for all-cause to be consistent with one another. |
| Date: | 2022–06–28 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:d7hbp&r=for |