Persona:
Rodríguez Sánchez, Ainara

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Rodríguez Sánchez
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Ainara
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2021 - 20253
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Mostrando 1 - 3 de 3
  • Miniatura
    Publicación
    Obtaining a threshold for the stewart index and its extension to ridge regression
    (Springer , 2021) Rodríguez Sánchez, Ainara; Salmerón Gómez, Román; García García, Catalina; https://orcid.org/0000-0001-9925-9802
    The linear regression model is widely applied to measure the relationship between a dependent variable and a set of independent variables. When the independent variables are related to each other, it is said that the model presents collinearity. If the relationship is between the intercept and at least one of the independent variables, the collinearity is nonessential, while if the relationship is between the independent variables (excluding the intercept), the collinearity is essential. The Stewart index allows the detection of both types of near multicollinearity. However, to the best of our knowledge, there are no established thresholds for this measure from which to consider that the multicollinearity is worrying. This is the main goal of this paper, which presents a Monte Carlo simulation to relate this measure to the condition number. An additional goal of this paper is to extend the Stewart index for its application after the estimation by ridge regression that is widely applied to estimate model with multicollinearity as an alternative to ordinary least squares (OLS). This extension could be also applied to determine the appropriate value for the ridge factor.
  • Miniatura
    Publicación
    Enlarging of the Sample to Address Multicollinearity
    (Springer, 2025-04-16) Salmerón-Gómez, Román; García-García, Catalina Beatriz; Rodríguez Sánchez, Ainara
    This paper analyzes the impact of sample enlargement on the mitigation of collinearity, concluding that it may mitigate the consequences of collinearity related to statistical analysis but not necessarily the numerical instability. This issue is important in teaching social sciences as it relates to one widely accepted solution for addressing multicollinearity. For a better understanding and illustration of the contribution made by this paper, two empirical examples and two simulations are presented and not highly technical developments are used.
  • Miniatura
    Publicación
    Nelson-Siegel Model and Multicollinearity
    (Springer, 2025-10-01) Rodríguez Sánchez, Ainara
    Nelson-Siegel model is used for important decision making about monetary policy, among others. Numerous researchers are aware of the potential multicollinearity in the Nelson-Siegel model that can lead to unstable estimations and signs contrary to expectations if the model is estimated by ordinary least squares (OLS). Some authors have proposed fixing the shape parameter to avoid multicollinearity problems, but that change can lead to extremely smooth time series. On the other hand, other authors have proposed estimating the Nelson-Siegel model with the ridge regression that is traditionally applied to estimate models with collinearity as an alternative to OLS. For a correct application of the ridge regression, data should be standardized which can make difficult the interpretation of the estimated model. Also, the inference in ridge regression is controversial. Alternatively, this work proposes the application of the raise regression to mitigate multicollinearity in Nelson-Siegel model. This methodology can be applied with the original data and maintains the global characteristics of the original model. The contribution of this paper is illustrated with two different empirical examples for American and European treasuries.