Regression results for multiple OLS models (monthly)
The Role of "Leads" in the Dynamic OLS Estimation of Cointegrating Regression Models
... dynamic OLS estimation of cointegrating regression ...dynamic OLS estimation of cointegrating regression ...dynamic OLS estimator without leads substantially outperforms that with leads ...
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The Role of "Leads" in the Dynamic OLS Estimation of Cointegrating Regression Models
... the OLS estimator contains the so-called second-order ...modified OLS estimator proposed by Phillips and Hansen (1990), Park’s (1992) canonical cointegrating regression estimator, and the dynamic ...
8
Bridging logistic and OLS regression
... the results of a logistic regression by estimating the female and male probabilities, keeping the values of the rest of independent variables equal to their average value, and subtracting the ...the ...
7
Bridging logistic and OLS regression
... the results of a logistic regression by estimating the female and male probabilities, keeping the values of the rest of independent variables equal to ...
6
Although ordinary least-squares (OLS) regression
... on regression through the origin ...such models and compares RTO functions among three pop- ular statistical ...convenience, OLS and RTO refer here to linear regressions obtained by least-squares ...
5
Understanding the Results of Multiple Linear Regression: Beyond Standardized Regression Coefficients
... of regression metrics within a single ...linear regression to be discussed here, such as dominance analysis (Azen & Budescu, 2003) and relative weights (Gro¨mping, 2006; Tonidandel, LeBreton, & ...
25
MULTIPLE LINEAR REGRESSION MODELS IN OUTLIER DETECTION
... mean of the remaining values, or the difference between the outlier and the next closest value. Different computer-based approaches have been proposed for detecting outlying data and it cannot be claimed that this is the ...
6
IMPROVED ESTIMATION STRATEGIES IN MULTIVARIATE MULTIPLE REGRESSION MODELS
... a regression model with a non-normal error when uncertain prior information about the value of the intercept parameter is available and can be expressed in the general form of a null hypothesis, H q×p θ p×1 = h ...
140
Testing for Multiple Structural Changes in Cointegrated Regression Models
... for multiple structural changes is relatively ...for multiple structural changes occurring at unknown dates in cointegrated regression ...of models that are relevant in practice. Our ...
42
Shrinkage estimation of regression models with multiple structural changes
... contain multiple breaks in the calendar range from 1921Q2 to 2011Q4, reflecting discrete changes in the way how equities are priced ...main results are summarized in Table ...usual OLS estimation in ...
52
Comparison of Parametric (OLS) and Non-Parametric (THEIL’S) Linear Regression
... linear regression assumptions for the OLS method or not, the analysis results were in favour of ...Bivariate Regression Data via Theil’s ...squares regression method for fitting an ...
6
A study of partial F tests for multiple linear regression models
... Abstract Partial F tests play a central role in model selections in multiple linear regression models. This paper studies the partial F tests from the view point of simultaneous confidence bands. It ...
26
Estimating and Testing Threshold Regression Models with Multiple Threshold Variables
... on models with only one thresh- old variable and have limited applications when two or more threshold variables are ...to models with multiple threshold variables 1 , and no theoreti- cal ...
75
A methodology for the design of experiments in computational intelligence with multiple regression models
... complex regression models to carry out predictive modeling using Machine Learning and well-known regression ...Our results are different for three out of five state-of-the-art simple datasets ...
22
Cluster-Based Estimators For Multiple And Multivariate Linear Regression Models
... alized regression M-estimator reported in Huber and Ronchetti (2011) is an example of robust regression alternative for small percentage of outliers in the ...linear models; while the least median of ...
54
Marketing margins for producers and traders along the value chain were computed and their determinants identified using multiple OLS regression technique
... This study analyzed the value chain of T. indica in Uganda with the motive of identifying opportunities and constraints to its commercialization and domestication. The study traced the movement of T. indica products from ...
12
Forecasting relativistic electron flux using dynamic multiple regression models
... from multiple spatial ...the results of the forecast of plasma parameters at GSO can be used as a boundary condition for assessing the space en- vironment in the region of the radiation belts outside GSO ...
6
Application of Neural Networks and multiple regression models in greenhouse climate estimation
... Based on universal approximation theorem, a neural network with a single hidden layer and with a sufficient large number of neurons can well approximate any arbitrary continuous function (Haykin, 1994). Therefore, the ...
15
Estimation of Nonparametric Multiple Regression Measurement Error Models with Validation Data
... nonparametric multiple regression mea- surement error models when both independent validation data on covariables and primary data on the response variable and surrogate covariables are ...
12
Comparison between multiple linear regression and fuzzy C regression models towards scale of health in ICU
... FCRM models introduced by Harthway and Bezdek are one of the great methods in analyzing a continuous and categorical ...FCRM models are tested on the simulated data. It shows that the FCRM models can ...
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