A NEW STUDY ON ASYMPTOTIC OPTIMALITY OF LEAST SQUARES MODEL AVERAGING

2020 ◽  
pp. 1-20
Author(s):  
Xinyu Zhang
Keyword(s):  
Least Squares ◽  
Asymptotic Optimality ◽  
Model Averaging ◽  
New Approach ◽  
Large Sample ◽  
Averaging Methods ◽  
General Weight ◽  
Maximum Selection ◽  
Comprehensive Study

In this article, we present a comprehensive study of asymptotic optimality of least squares model averaging methods. The concept of asymptotic optimality is that in a large-sample sense, the method results in the model averaging estimator with the smallest possible prediction loss among all such estimators. In the literature, asymptotic optimality is usually proved under specific weights restriction or using hardly interpretable assumptions. This article provides a new approach to proving asymptotic optimality, in which a general weight set is adopted, and some easily interpretable assumptions are imposed. In particular, we do not impose any assumptions on the maximum selection risk and allow a larger number of regressors than that of existing studies.

scholarly journals Forecast Bitcoin Volatility with Least Squares Model Averaging

Econometrics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 40 ◽  
Author(s):  
Tian Xie
Keyword(s):  
Least Squares ◽  
Regression Models ◽  
Model Averaging ◽  
Realized Volatility ◽  
Model Specification ◽  
Asset Market ◽  
Averaging Methods ◽  
Empirical Results ◽  
Conventional Regression

In this paper, we study forecasting problems of Bitcoin-realized volatility computed on data from the largest crypto exchange—Binance. Given the unique features of the crypto asset market, we find that conventional regression models exhibit strong model specification uncertainty. To circumvent this issue, we suggest using least squares model-averaging methods to model and forecast Bitcoin volatility. The empirical results demonstrate that least squares model-averaging methods in general outperform many other conventional regression models that ignore specification uncertainty.

Recent and Planned Developments of the Program OxCal

Radiocarbon ◽  
2013 ◽  
Vol 55 (2) ◽  
pp. 720-730 ◽  
Author(s):  
Christopher Bronk Ramsey ◽  
Sharen Lee
Keyword(s):  
Statistical Analysis ◽  
Statistical Methods ◽  
Software Package ◽  
Model Averaging ◽  
Data Sets ◽  
Radiocarbon Dates ◽  
New Approach ◽  
New Models ◽  
Multiphase Models ◽  
Deposition Models

OxCal is a widely used software package for the calibration of radiocarbon dates and the statistical analysis of 14C and other chronological information. The program aims to make statistical methods easily available to researchers and students working in a range of different disciplines. This paper will look at the recent and planned developments of the package. The recent additions to the statistical methods are primarily aimed at providing more robust models, in particular through model averaging for deposition models and through different multiphase models. The paper will look at how these new models have been implemented and explore the implications for researchers who might benefit from their use. In addition, a new approach to the evaluation of marine reservoir offsets will be presented. As the quantity and complexity of chronological data increase, it is also important to have efficient methods for the visualization of such extensive data sets and methods for the presentation of spatial and geographical data embedded within planned future versions of OxCal will also be discussed.

scholarly journals Deformation analysis with Total Least Squares

2006 ◽  
Vol 6 (4) ◽  
pp. 663-669 ◽  
Author(s):  
M. Acar ◽  
M. T. Özlüdemir ◽  
O. Akyilmaz ◽  
R. N. Çelik ◽  
T. Ayan
Keyword(s):  
Least Squares ◽  
Total Least Squares ◽  
Transformation Model ◽  
Deformation Analysis ◽  
New Approach ◽  
Main Research ◽  
Research Fields ◽  
Analysis Process ◽  
Alternative Methodology ◽  
Coordinate Changes

Abstract. Deformation analysis is one of the main research fields in geodesy. Deformation analysis process comprises measurement and analysis phases. Measurements can be collected using several techniques. The output of the evaluation of the measurements is mainly point positions. In the deformation analysis phase, the coordinate changes in the point positions are investigated. Several models or approaches can be employed for the analysis. One approach is based on a Helmert or similarity coordinate transformation where the displacements and the respective covariance matrix are transformed into a unique datum. Traditionally a Least Squares (LS) technique is used for the transformation procedure. Another approach that could be introduced as an alternative methodology is the Total Least Squares (TLS) that is considerably a new approach in geodetic applications. In this study, in order to determine point displacements, 3-D coordinate transformations based on the Helmert transformation model were carried out individually by the Least Squares (LS) and the Total Least Squares (TLS), respectively. The data used in this study was collected by GPS technique in a landslide area located nearby Istanbul. The results obtained from these two approaches have been compared.

Initialization of Identification of Fractional Model by Output-Error Technique

2015 ◽  
Vol 11 (2) ◽  
Author(s):  
Abir Khadhraoui ◽  
Khaled Jelassi ◽  
Jean-Claude Trigeassou ◽  
Pierre Melchior
Keyword(s):  
Numerical Simulation ◽  
Least Squares ◽  
Fractional Order ◽  
Fractional Integration ◽  
New Approach ◽  
Fractional Model ◽  
Output Error

A bad initialization of output-error (OE) technique can lead to an inappropriate identification results. In this paper, we introduce a solution to this problem; the basic idea is to estimate the parameters and the fractional order of the noninteger system by a new approach of least-squares (LS) method based on repeated fractional integration to initialize OE technique. It will be shown that LS method offers a good initialization to OE algorithm and leads to acceptable identification results. The performance of the proposed method is shown through numerical simulation examples.

OPTIMAL MULTISTEP VAR FORECAST AVERAGING

2020 ◽  
Vol 36 (6) ◽  
pp. 1099-1126
Author(s):  
Jen-Che Liao ◽  
Wen-Jen Tsay
Keyword(s):  
Infinite Order ◽  
Asymptotic Optimality ◽  
Model Misspecification ◽  
Model Averaging ◽  
Finite Sample ◽  
Forecast Horizon ◽  
Simulation Experiments ◽  
Vector Autoregressive ◽  
One Step ◽  
Mallows Model

This article proposes frequentist multiple-equation least-squares averaging approaches for multistep forecasting with vector autoregressive (VAR) models. The proposed VAR forecast averaging methods are based on the multivariate Mallows model averaging (MMMA) and multivariate leave-h-out cross-validation averaging (MCVAh) criteria (with h denoting the forecast horizon), which are valid for iterative and direct multistep forecast averaging, respectively. Under the framework of stationary VAR processes of infinite order, we provide theoretical justifications by establishing asymptotic unbiasedness and asymptotic optimality of the proposed forecast averaging approaches. Specifically, MMMA exhibits asymptotic optimality for one-step-ahead forecast averaging, whereas for direct multistep forecast averaging, the asymptotically optimal combination weights are determined separately for each forecast horizon based on the MCVAh procedure. To present our methodology, we investigate the finite-sample behavior of the proposed averaging procedures under model misspecification via simulation experiments.

A STUDY OF HIGHER-ORDER DISCONTINUOUS GALERKIN AND QUADRATIC LEAST-SQUARES STABILIZED FINITE ELEMENT COMPUTATIONS FOR ACOUSTICS

2006 ◽  
Vol 14 (01) ◽  
pp. 1-19 ◽  
Author(s):  
ISAAC HARARI ◽  
RADEK TEZAUR ◽  
CHARBEL FARHAT
Keyword(s):  
Least Squares ◽  
Discontinuous Galerkin ◽  
Large Scale ◽  
Dispersion Analysis ◽  
Stability Parameter ◽  
New Approach ◽  
One Dimensional ◽  
Stabilized Finite Element ◽  
Quadratic Interpolation ◽  
The Stability

One-dimensional analyses provide novel definitions of the Galerkin/least-squares stability parameter for quadratic interpolation. A new approach to the dispersion analysis of the Lagrange multiplier approximation in discontinuous Galerkin methods is presented. A series of computations comparing the performance of [Formula: see text] Galerkin and GLS methods with Q-8-2 DGM on large-scale problems shows superior DGM results on analogous meshes, both structured and unstructured. The degradation of the [Formula: see text] GLS stabilization on unstructured meshes may be a consequence of inadequate one-dimensional analysis used to derive the stability parameter.

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