Rolling Regression Analysis of the Pástor-Stambaugh Model: Evidence from Robust Instrumental Variables

Racicot, François-Éric; Rentz, William F.; Kahl, Alfred L.

doi:10.1007/s11294-016-9620-x

Cited by 5 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, we do not know the process of data creation, but we also lack variables. When the output variable is lost, the learning problem becomes unsupervised [8,9]. For unsupervised learning, the direct execution of AIC or BIC cannot reveal the distribution of potential variables and the information contained in potential variables.…”

Section: Cluster Regression Analysis Modelmentioning

confidence: 99%

The Scoring Mechanism of Players after Game Based on Cluster Regression Analysis Model

Jin

Yi²

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Cluster regression analysis model is an effective theory for a reasonable and fair player scoring game. It can roughly predict and evaluate the performance of athletes after the game with limited data and provide scientific predictions for the performance of athletes. The purpose of this research is to achieve the player’s postmatch scoring through the cluster regression model. Through the research and analysis of past ball games, the comparison and experiment of multiple objects based on different regression analysis theories, the following conclusions are drawn. Different regression models have different standard errors, but if the data in other model categories are put into the centroid model expression, the standard error and the error of the original model are within 0.3, which can replace other models for calculation. In the player’s postmatch scoring, although the expert’s prediction of the result is very accurate, within the error range of 1 copy, the player’s postmatch scoring mechanism based on the cluster regression analysis model is more accurate, and the error formula is in the 0.5 range. It is best to switch the data of the regression model twice to compare the scoring mechanism using different regression experiments.

show abstract

Section: Cluster Regression Analysis Modelmentioning

confidence: 99%

The Scoring Mechanism of Players after Game Based on Cluster Regression Analysis Model

Jin

Yi²

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…The FF + IML panel fixed effects of the table may be found in . The FF + IML dynamic may be found in Racicot, Rentz, and Kahl (2017a Relying on OLS estimation, the augmented FF model seems highly significant both in terms of adjusted R 2 and individual t-statistics for the crosssectional estimations 14 . However, when using our GMM d estimator, the results are not quite so appealing.…”

Section: Resultsmentioning

confidence: 96%

Does Illiquidity Matter? An Errors-in-Variables Perspective

Racicot¹,

Rentz²

2019

SAE

Self Cite

View full text Add to dashboard Cite

La iliquidez es bien conocida en la literatura como un importante factor de riesgo a considerar en los modelos financieros de retorno. Sin embargo, no hay mucho consenso sobre la medida que se debe utilizar como proxy para la iliquidez. Nuestras aportaciones a la cuestión se centran principalmente en la medida de Pástor-Stambaugh en el contexto del modelo tri-factorial de Fama-French y, más recientemente, en su nuevo modelo de cinco factores. En este artículo, se discuten las aportaciones anteriormente aludidas desde la perspectiva de errores en las variables. En particular, se proponen nuevos instrumentos robustos que se han desarrollado y aplicado en distintas etapas de nuestra investigación. En este artículo, se llevan a cabo los test de robustez de estos nuevos instrumentos. En general, nuestros nuevos instrumentos, con estimación GMM, muestran que tanto en el marco de sección cruzada o de datos panel como en una regresión recursiva/rolling regression (en comparación con el marco del filtro de Kalman) el factor más significativo es el factor de mercado. Estos resultados podrían interpretarse en la línea de la preocupación Cochrane acerca de un "zoológico de factores".

show abstract

“…In this section, we control for the relevance of our results by running additional estimations based on an instrumental variables method. Specifically, we adopt the GMM d approach developed by Racicot (2015a) and subsequently implemented by, among others, Racicot and Rentz (2015; or Racicot et al (2017;2019). The GMM d estimator is basically a robust instruments variables-based extension of Hansen's generalized method of moments estimator (Hansen 1982) and is characterized by the following equation:…”

Section: Robustness Testsmentioning

confidence: 99%

Asset pricing models with measurement error problems: A new framework with Compact Genetic Algorithms

Diyarbakirlioglu¹,

Desban²,

Jarjir³

2022

Finance

View full text Add to dashboard Cite

We implement a new framework to mitigate the errors-in-variables (EIV) problem in the estimation of asset pricing models. Considering an international data of portfolio stock returns from 1990 to 2021 widely used in empirical studies, we highlight the importance of the estimation method in time-series regressions. We compare the traditional Ordinary-Least Squares (OLS) method to an alternative estimator based on a Compact Genetic Algorithm (CGA) in the case of the CAPM, three-, and five-factor models. Based on intercepts α, betas β M , adjusted R 2 , and the Gibbons, Ross and Shanken (1989) test, we find that the CGA-based method outperforms overall the OLS for the three asset pricing models. In particular, we obtain less statistically significant intercepts, smoother R 2 across different portfolios and lower GRS test statistics. Specifically, in line with Roll's critique (1977) on the unobservability of the market portfolio, we reduce the attenuation bias in market risk premium estimates. Moreover, our results are robust to alternative methods such as Instrumental Variables estimated with Generalized-Method of Moments (GMM). Our findings have several empirical and managerial implications related to the estimation of asset pricing models as well as their interpretation as a popular tool in terms of corporate financial decision-making.

show abstract

Rolling Regression Analysis of the Pástor-Stambaugh Model: Evidence from Robust Instrumental Variables

Cited by 5 publications

References 28 publications

The Scoring Mechanism of Players after Game Based on Cluster Regression Analysis Model

The Scoring Mechanism of Players after Game Based on Cluster Regression Analysis Model

Does Illiquidity Matter? An Errors-in-Variables Perspective

Asset pricing models with measurement error problems: A new framework with Compact Genetic Algorithms

Contact Info

Product

Resources

About