Bayesian Model Averaging, Ordinary Least Squares and the Price of Gold

2017 ◽  
Author(s):  
Dirk G. Baur ◽  
Brian M. Lucey
Keyword(s):  
Least Squares ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Ordinary Least Squares

scholarly journals Bayesian Model Averaging and Weighted-Average Least Squares: Equivariance, Stability, and Numerical Issues

2011 ◽  
Vol 11 (4) ◽  
pp. 518-544 ◽  
Author(s):  
Giuseppe De Luca ◽  
Jan R. Magnus
Keyword(s):  
Least Squares ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Weighted Average ◽  
Simulated Data ◽  
Linear Regression Models ◽  
Explanatory Variables ◽  
Memory Problems ◽  
Regression Parameters

In this article, we describe the estimation of linear regression models with uncertainty about the choice of the explanatory variables. We introduce the Stata commands bma and wals, which implement, respectively, the exact Bayesian model-averaging estimator and the weighted-average least-squares estimator developed by Magnus, Powell, and Prüfer (2010, Journal of Econometrics 154: 139–153). Unlike standard pretest estimators that are based on some preliminary diagnostic test, these model-averaging estimators provide a coherent way of making inference on the regression parameters of interest by taking into account the uncertainty due to both the estimation and the model selection steps. Special emphasis is given to several practical issues that users are likely to face in applied work: equivariance to certain transformations of the explanatory variables, stability, accuracy, computing speed, and out-of-memory problems. Performances of our bma and wals commands are illustrated using simulated data and empirical applications from the literature on model-averaging estimation.

scholarly journals HỆ THỐNG CHỈ SỐ CẢNH BÁO SỚM KHỦNG HOẢNG TIỀN TỆ VÀ KHỦNG HOẢNG NGÂN HÀNG TẠI VIỆT NAM

2021 ◽  
Vol 36 (06) ◽  
Author(s):  
NGUYỄN THỊ MỸ PHƯỢNG
Keyword(s):  
Least Squares ◽  
Early Warning ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Viet Nam ◽  
Two Stage ◽  
Early Warning Indicators ◽  
Two Stage Least Squares ◽  
Warning Indicators

Dựa trên quan điểm thận trọng, nghiên cứu này tích hợp bốn cách tiếp cận Signal, Logit/Probit, BMA (Bayesian Model Averaging) và 2SLS (Two Stage Least Squares) để phát triển hệ thống các chỉ số cảnh báo sớm (Early Warning Indicators – EWI) về khủng hoảng tiền tệ (KHTT) và khủng hoảng ngân hàng (KHNH)  tại Việt Nam. Kết quả nghiên cứu cho thấy vai trò quan trọng của các chỉ số kinh tế vĩ mô trong cảnh báo sớm KHTT và KHNH tại Việt Nam, đặc biệt là 8 chỉ số, bao gồm chỉ số giá chứng khoán, tỷ giá thực đa phương, xuất khẩu, M2/dự trữ ngoại hối, tiền gửi ngân hàng, dự trữ ngoại hối, số nhân cung tiền M2 và tác động của khủng hoảng tài chính (KHTC) toàn cầu. Thêm vào đó, nghiên cứu cũng tìm thấy bằng chứng về mối quan hệ nhân quả hai chiều giữa KHTT và KHNH tại Việt Nam, đồng thời cung cấp bằng chứng thực nghiệm về tác động của hiện tượng đô la hóa đến khả năng xảy ra KHTT và tác động mạnh mẽ của KHTC toàn cầu đến khả năng xảy ra KHTT và KHNH tại các nền kinh tế mới nổi nhỏ và mở cửa như Việt Nam.

scholarly journals A Regional Blended Precipitation Dataset over Pakistan Based on Regional Selection of Blending Satellite Precipitation Datasets and the Dynamic Weighted Average Least Squares Algorithm

2020 ◽  
Vol 12 (24) ◽  
pp. 4009
Author(s):  
Khalil Ur Rahman ◽  
Songhao Shang
Keyword(s):  
Least Squares ◽  
Bayesian Model ◽  
Arid Regions ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Weighted Average ◽  
Satellite Precipitation ◽  
Post Monsoon ◽  
Skill Scores ◽  
Higher Weights

Substantial uncertainties are associated with satellite precipitation datasets (SPDs), which are further amplified over complex terrain and diverse climate regions. The current study develops a regional blended precipitation dataset (RBPD) over Pakistan from selected SPDs in different regions using a dynamic weighted average least squares (WALS) algorithm from 2007 to 2018 with 0.25° spatial resolution and one-day temporal resolution. Several SPDs, including Global Precipitation Measurement (GPM)-based Integrated Multi-Satellite Retrievals for GPM (IMERG), Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42-v7, Precipitation Estimates from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), ERA-Interim (reanalysis dataset), SM2RAIN-CCI, and SM2RAIN-ASCAT are evaluated to select appropriate blending SPDs in different climate regions. Six statistical indices, including mean bias (MB), mean absolute error (MAE), unbiased root mean square error (ubRMSE), correlation coefficient (R), Kling–Gupta efficiency (KGE), and Theil’s U coefficient, are used to assess the WALS-RBPD performance over 102 rain gauges (RGs) in Pakistan. The results showed that WALS-RBPD had assigned higher weights to IMERG in the glacial, humid, and arid regions, while SM2RAIN-ASCAT had higher weights across the hyper-arid region. The average weights of IMERG (SM2RAIN-ASCAT) are 29.03% (23.90%), 30.12% (24.19%), 31.30% (27.84%), and 27.65% (32.02%) across glacial, humid, arid, and hyper-arid regions, respectively. IMERG dominated monsoon and pre-monsoon seasons with average weights of 34.87% and 31.70%, while SM2RAIN-ASCAT depicted high performance during post-monsoon and winter seasons with average weights of 37.03% and 38.69%, respectively. Spatial scale evaluation of WALS-RPBD resulted in relatively poorer performance at high altitudes (glacial and humid regions), whereas better performance in plain areas (arid and hyper-arid regions). Moreover, temporal scale performance assessment depicted poorer performance during intense precipitation seasons (monsoon and pre-monsoon) as compared with post-monsoon and winter seasons. Skill scores are used to quantify the improvements of WALS-RBPD against previously developed blended precipitation datasets (BPDs) based on WALS (WALS-BPD), dynamic clustered Bayesian model averaging (DCBA-BPD), and dynamic Bayesian model averaging (DBMA-BPD). On the one hand, skill scores show relatively low improvements of WALS-RBPD against WALS-BPD, where maximum improvements are observed in glacial (humid) regions with skill scores of 29.89% (28.69%) in MAE, 27.25% (23.89%) in ubRMSE, and 24.37% (28.95%) in MB. On the other hand, the highest improvements are observed against DBMA-BPD with average improvements across glacial (humid) regions of 39.74% (36.93%), 38.27% (33.06%), and 39.16% (30.47%) in MB, MAE, and ubRMSE, respectively. It is recommended that the development of RBPDs can be a potential alternative for data-scarce regions and areas with complex topography.

Bayesian Model Averaging With Exponentiated Least Squares Loss

2018 ◽  
Vol 64 (5) ◽  
pp. 3331-3345
Author(s):  
Dong Dai ◽  
Lei Han ◽  
Ting Yang ◽  
Tong Zhang
Keyword(s):  
Least Squares ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging

Predicting soil organic carbon percentage from loss-on-ignition using Bayesian Model Averaging

Soil Research ◽  
2009 ◽  
Vol 47 (8) ◽  
pp. 763 ◽  
Author(s):  
Ai Leon ◽  
Roberto Leon Gonzalez
Keyword(s):  
Organic Carbon ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Clay Content ◽  
Ordinary Least Squares ◽  
Soil Survey ◽  
Soil Horizon ◽  
Loss On Ignition ◽  
Organic C

A shortage of data for percentage of organic carbon (C%) makes calculation of soil profile carbon storage difficult. Loss on ignition (LOI) data, which are cheap to obtain and often readily available, can be used to estimate organic C%. This paper simultaneously considers several predictors of organic C%: LOI, parent material, drainage status, type of soil horizon, clay content, and pH. In order to model appropriately the existence of multiple hypotheses and the consequent model uncertainty, a Bayesian Model Averaging (BMA) approach was used. BMA considers all models that result from all possible combinations of explanatory variables. Based on a BMA approach and Scottish Soil Survey data, it was found that the most important factors to predict organic C% were LOI, clay content, a dummy for Countesswells Association (till derived from granite), and a dummy for B horizon soils. The validation analysis showed that prediction accuracy for organic C% was better with the BMA approach than with an ordinary least-squares approach that includes no other predictors apart from LOI (i.e. 22% reduction in horizons A, Ap, and C).

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models
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