The Efficacy of Ability Proxies for Estimating the Returns to Schooling: A Factor Model-Based Evaluation

2021 ◽  
Author(s):  
Mohitosh Kejriwal ◽  
Xiaoxiao Li ◽  
Evan Totty
Keyword(s):  
Factor Model ◽  
Returns To Schooling ◽  
Model Based

Exponential ergodicity of an affine two-factor model based on the α-root process

2017 ◽  
Vol 49 (4) ◽  
pp. 1144-1169 ◽  
Author(s):  
Peng Jin ◽  
Jonas Kremer ◽  
Barbara Rüdiger
Keyword(s):  
Parameter Estimation ◽  
Factor Model ◽  
Estimation Problem ◽  
Two Dimensional ◽  
Dimensional Model ◽  
Parameter Estimation Problem ◽  
Exponential Ergodicity ◽  
Model Based ◽  
Defaultable Bonds ◽  
Two Dimensional Model

Abstract We study an affine two-factor model introduced by Barczy et al. (2014). One component of this two-dimensional model is the so-called α-root process, which generalizes the well-known Cox–Ingersoll–Ross process. In the α = 2 case, this two-factor model was used by Chen and Joslin (2012) to price defaultable bonds with stochastic recovery rates. In this paper we prove exponential ergodicity of this two-factor model when α ∈ (1, 2). As a possible application, our result can be used to study the parameter estimation problem of the model.

scholarly journals Mahalanobis Distances on Factor Model Based Estimation

Econometrics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 10
Author(s):  
Deliang Dai
Keyword(s):  
Covariance Matrix ◽  
Mahalanobis Distance ◽  
Factor Model ◽  
Original Sample ◽  
Mahalanobis Distances ◽  
Model Based ◽  
New Type ◽  
New Form ◽  
Contamination Effects

A factor model based covariance matrix is used to build a new form of Mahalanobis distance. The distribution and relative properties of the new Mahalanobis distances are derived. A new type of Mahalanobis distance based on the separated part of the factor model is defined. Contamination effects of outliers detected by the new defined Mahalanobis distances are also investigated. An empirical example indicates that the new proposed separated type of Mahalanobis distances predominate the original sample Mahalanobis distance.

Factor Model Based Clustering Approach for Cardinality Constrained Portfolio Selection

2014 ◽  
Vol 47 (3) ◽  
pp. 10713-10718
Author(s):  
Kening Jiang ◽  
Duan Li ◽  
Jianjun Gao ◽  
Jeffrey Xu YU
Keyword(s):  
Portfolio Selection ◽  
Factor Model ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering Approach

scholarly journals Multi-factor Stock Selection Model Based on Kernel Support Vector Machine

2018 ◽  
Vol 10 (5) ◽  
pp. 9 ◽  
Author(s):  
Ru Zhang ◽  
Zi-ang Lin ◽  
Shaozhen Chen ◽  
Zhixuan Lin ◽  
Xingwei Liang
Keyword(s):  
Support Vector Machine ◽  
Predictive Power ◽  
Factor Model ◽  
Selection Strategy ◽  
Support Vector ◽  
Stock Selection ◽  
Financial Investment ◽  
Model Based ◽  
Kernel Support Vector Machine ◽  
Selection Of

In recent years, the combination of machine learning method and traditional financial investment field has become a hotspot in academic and industry. This paper takes CSI 300 and CSI 500 stocks as the research objects. First, this paper carries out kernel function test and parameter optimization for the kernel support vector machine system, and then predict and optimize the combination of market-neutral stock selection strategy and stock right strategy. The results of the experiment show that the multi-factor model based on SVM has a strong predictive power for the selection of stock, and it has a difference in the predictive power of different nuclear functions.

From types to typological thinking: a reply to Asendorpf

2006 ◽  
Vol 20 (1) ◽  
pp. 49-51 ◽  
Author(s):  
Robert R. McCrae ◽  
Antonio Terracciano ◽  
Paul T. Costa ◽  
Daniel J. Ozer
Keyword(s):  
Individual Differences ◽  
Factor Model ◽  
Five Factor Model ◽  
Factor Analyses ◽  
Model Based ◽  
Personality Research ◽  
Typological Thinking ◽  
Q Sort

We continue to disagree with Asendorpf (2006) on the best way to analyse Q‐sort data and on our priorities for personality research. We believe on statistical grounds that the large first factor found in inverse factor analyses of raw CAQ items tells us much about response norms, but little or nothing about individual differences. These emerge more clearly in analyses of standardised items, which show the familiar dimensions of the Five‐Factor Model. Based on our research on types and the mixed results reported by other researchers, we do not believe that replicable empirical types are likely to be found, and suggest that a more profitable line of research would focus on the heuristics of types and the configural interpretation of traits. Published in 2006 by John Wiley & Sons, Ltd.

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