Author: Peter Praetz
Publisher:
ISBN: 9780867461787
Category : Regression analysis
Languages : en
Pages : 19
Book Description
Seasonality, Differencing, Errors-in-variables and Heteroscedasticity in the Linear Regression Model
Author: Peter Praetz
Publisher:
ISBN: 9780867461787
Category : Regression analysis
Languages : en
Pages : 19
Book Description
Publisher:
ISBN: 9780867461787
Category : Regression analysis
Languages : en
Pages : 19
Book Description
Regression Analysis
Author: George C. S. Wang
Publisher: Institute of Business Forec
ISBN: 9780932126504
Category : Business & Economics
Languages : en
Pages : 306
Book Description
Publisher: Institute of Business Forec
ISBN: 9780932126504
Category : Business & Economics
Languages : en
Pages : 306
Book Description
A Note on Errors-in-variables and Inconsistency in Multiple Regression Models
Author: Charles I. Plosser
Publisher:
ISBN:
Category : Regression analysis
Languages : en
Pages : 28
Book Description
Publisher:
ISBN:
Category : Regression analysis
Languages : en
Pages : 28
Book Description
Australian National Bibliography
Fusion of Data Sets in Multivariate Linear Regression with Errors-in-Variables
Author: Albert Satorra
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
We consider the application of normal theory methods to the estimation and testing of a general type of multivariate regression models with errors-in-variables, in the case where various data sets are merged into a single analysis and the observable variables deviate possibly from normality. The various samples to be merged can differ on the set of observable variables available. We show that there is a convenient way to parameterize the model so that, despite the possible non-normality of the data, normal-theory methods yield correct inferences for the parameters of interest and for the goodness-of-fit test. The theory described encompasses both the functional and structural model cases, and can be implemented using standard software for structural equations models, such as LISREL, EQS, LISCOMP, among others. An illustration with Monte Carlo data is presented.
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
We consider the application of normal theory methods to the estimation and testing of a general type of multivariate regression models with errors-in-variables, in the case where various data sets are merged into a single analysis and the observable variables deviate possibly from normality. The various samples to be merged can differ on the set of observable variables available. We show that there is a convenient way to parameterize the model so that, despite the possible non-normality of the data, normal-theory methods yield correct inferences for the parameters of interest and for the goodness-of-fit test. The theory described encompasses both the functional and structural model cases, and can be implemented using standard software for structural equations models, such as LISREL, EQS, LISCOMP, among others. An illustration with Monte Carlo data is presented.
Regression with Dummy Variables
Author: Melissa A. Hardy
Publisher: SAGE Publications, Incorporated
ISBN: 9780803951280
Category : Social Science
Languages : en
Pages : 0
Book Description
It is often necessary for social scientists to study differences in groups, such as gender or race differences in attitudes, buying behavior, or socioeconomic characteristics. When the researcher seeks to estimate group differences through the use of independent variables that are qualitative, dummy variables allow the researcher to represent information about group membership in quantitative terms without imposing unrealistic measurement assumptions on the categorical variables. Beginning with the simplest model, Hardy probes the use of dummy variable regression in increasingly complex specifications, exploring issues such as: interaction, heteroscedasticity, multiple comparisons and significance testing, the use of effects or contrast coding, testing for curvilinearity, and estimating a piecewise linear regression.
Publisher: SAGE Publications, Incorporated
ISBN: 9780803951280
Category : Social Science
Languages : en
Pages : 0
Book Description
It is often necessary for social scientists to study differences in groups, such as gender or race differences in attitudes, buying behavior, or socioeconomic characteristics. When the researcher seeks to estimate group differences through the use of independent variables that are qualitative, dummy variables allow the researcher to represent information about group membership in quantitative terms without imposing unrealistic measurement assumptions on the categorical variables. Beginning with the simplest model, Hardy probes the use of dummy variable regression in increasingly complex specifications, exploring issues such as: interaction, heteroscedasticity, multiple comparisons and significance testing, the use of effects or contrast coding, testing for curvilinearity, and estimating a piecewise linear regression.
Regression Analysis
Author: Jim Frost
Publisher: Statistics By Jim Publishing
ISBN: 9781735431185
Category :
Languages : en
Pages : 352
Book Description
Intuitively understand regression analysis by focusing on concepts and graphs rather than equations and formulas. I use everyday language so you can grasp regression at a deeper level. Progress from a beginner to a skilled practitioner. Learn practical tips for performing your analysis and interpreting the results. Feel confident that you're analyzing your data properly and able to trust your results. Know that you can detect and correct problems that arise. Includes access to free downloadable datasets for the examples. Learn the following: How regression works and when to use it. Selecting the correct type of regression analysis. Specifying the best model. Understanding main effects, interaction effects, and modeling curvature. Interpreting the results. Assessing the fit of the model. Generating predictions and evaluating their precision. Checking the assumptions and resolving issues. Examples of different types of regression analyses.
Publisher: Statistics By Jim Publishing
ISBN: 9781735431185
Category :
Languages : en
Pages : 352
Book Description
Intuitively understand regression analysis by focusing on concepts and graphs rather than equations and formulas. I use everyday language so you can grasp regression at a deeper level. Progress from a beginner to a skilled practitioner. Learn practical tips for performing your analysis and interpreting the results. Feel confident that you're analyzing your data properly and able to trust your results. Know that you can detect and correct problems that arise. Includes access to free downloadable datasets for the examples. Learn the following: How regression works and when to use it. Selecting the correct type of regression analysis. Specifying the best model. Understanding main effects, interaction effects, and modeling curvature. Interpreting the results. Assessing the fit of the model. Generating predictions and evaluating their precision. Checking the assumptions and resolving issues. Examples of different types of regression analyses.
Consistent Estimation in Linear Regression with Errors in Variables
Non-linear Errors-in-variables Regression Models
Author: Gareth Gordon James
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 694
Book Description
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 694
Book Description
Essentials of Econometrics
Author: Damodar N. Gujarati
Publisher: SAGE Publications
ISBN: 1071850423
Category : Business & Economics
Languages : en
Pages : 633
Book Description
Logically organized and accessible, this updated Fifth Edition of Gujaratiā²s classic text provides students with an overview of the basics of econometric theory from ordinal logistic regression to time series.
Publisher: SAGE Publications
ISBN: 1071850423
Category : Business & Economics
Languages : en
Pages : 633
Book Description
Logically organized and accessible, this updated Fifth Edition of Gujaratiā²s classic text provides students with an overview of the basics of econometric theory from ordinal logistic regression to time series.