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K Sonderhof - One of the best experts on this subject based on the ideXlab platform.
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inteff3 stata module to compute partial effects in a probit or logit model with a triple Dummy Variable interaction term
Statistical Software Components, 2009Co-Authors: Thomas Cornelissen, K SonderhofAbstract:inteff3 computes partial effects in a probit or logit model with a triple Dummy Variable interaction term. These models may be applied when the effect of a binary regressor on a binary dependent Variable is allowed to vary over combinations of two sub-groups. For example, one may be interested in the way a university degree and the presence of children affect the gender difference in labour market participation. To this effect, one may run a probit model of labour market participation including dummies for female, university degree and presence of children, as well as their pairwise and triple interaction terms.
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Partial effects in probit and logit models with a triple Dummy-Variable interaction term
STATA J, 2009Co-Authors: K SonderhofAbstract:In nonlinear regression models, such as probit or logit models, coefficients cannot be interpreted as partial effects. The partial effects are usually nonlinear combinations of all regressors and regression coefficients of the model. We derive the partial effects in such models with a triple Dummy-Variable interaction term. The formulas derived here are implemented in the Stata inteff3 command. The command also applies the delta method to compute the standard errors of the partial effects. We illustrate the use of the command with an empirical application, analyzing how the gender gap in labor-market participation is affected by the presence of children and a university degree. We find that the presence of children increases the gender gap in labor-market participation but that this increase is smaller for more highly educated individuals.
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Marginal effects in the probit model with a triple Dummy Variable interaction term
2008Co-Authors: Thomas Cornelissen, K SonderhofAbstract:In non-linear regression models, such as the probit model, coefficients cannot be interpreted as marginal effects. The marginal effects are usually non-linear combinations of all regressors and regression coefficients of the model. This paper derives the marginal effects in a probit model with a triple Dummy Variable interaction term. A frequent application of this model is the regression-based difference-in-difference-in-differences estimator with a binary outcome Variable. The formulae derived here are implemented in a Stata program called inteff3 which applies the delta method in order to compute also the standard errors of the marginal effects.
Wei Sheng Zeng - One of the best experts on this subject based on the ideXlab platform.
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using linear mixed model and Dummy Variable model approaches to construct compatible single tree biomass equations at different scales a case study for masson pine in southern china
Journal of forest science, 2018Co-Authors: Wei Sheng Zeng, Shou Zheng Tang, Ram P SharmaAbstract:The estimation of forest biomass is important for practical issues and scientific purposes in forestry. The estimation of forest biomass on a large-scale level would be merely possible with the application of generalized single-tree biomass models. The aboveground biomass data on Masson pine ( Pinus massoniana) from nine provinces in southern China were used to develop generalized single-tree biomass models using both linear mixed model and Dummy Variable model methods. An allometric function requiring only diameter at breast height was used as a base model for this purpose. The results showed that the aboveground biomass estimates of individual trees with identical diameters were different among the forest origins (natural and planted) and geographic regions (provinces). The linear mixed model with random effect parameters and Dummy model with site-specific (local) parameters showed better fit and prediction performance than the population average model. The linear mixed model appears more flexible than the Dummy Variable model for the construction of generalized single-tree biomass models or compatible biomass models at different scales. The linear mixed model method can also be applied to develop other types of generalized single-tree models such as basal area growth and volume models.
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using nonlinear mixed model and Dummy Variable model approaches to develop origin based individual tree biomass equations
Trees-structure and Function, 2015Co-Authors: Wei Sheng ZengAbstract:Based on above- and below-ground biomass measurements from 604 and 212 sample trees respectively, aboveground biomass models for different origins didn’t have significant difference while belowground biomass models did. Based on the measurement data of aboveground biomass from 604 sample trees and belowground biomass from 212 sample trees of Chinese fir (Cunninghamia lanceolata) and Masson pine (Pinus massoniana) in southern China, the individual tree above- and below-ground biomass models involving forest origin were developed using nonlinear mixed model and Dummy Variable model approaches, and the effect of forest origin on biomass models was analyzed. The results showed that the aboveground biomass models for different origins had no significant difference, while the belowground biomass models were significantly different; and the belowground biomass estimate of a natural tree was highly greater than that of a planted tree with the same diameter and height. Specially, the belowground biomass estimates of natural trees were nearly 30 % and about 45 % greater than those of planted trees for Chinese fir and Masson pine, respectively. The mean prediction errors of aboveground biomass models and belowground biomass models developed in this study were less than 5 % and 15 %, respectively, which meant the biomass models could be applied to estimate forest biomass of the two species at large scale.
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using the Dummy Variable model approach to construct compatible single tree biomass equations at different scales a case study for masson pine pinus massoniana in southern china
Canadian Journal of Forest Research, 2011Co-Authors: Wei Sheng Zeng, Hui Ru Zhang, Shou Zheng TangAbstract:It is fundamental for monitoring and assessment of national forest biomass to develop generalized single-tree bio- mass models suitable for large-scale forest biomass estimation. However, the compatibility of forest biomass estimates among different scales is a real problem. Based on the aboveground biomass data of Masson pine (Pinus massoniana Lamb.) in southern China, generalized single-tree biomass equations applying to national and regional forest biomass esti- mation were constructed using the Dummy Variable model method, which provided an effective approach to solving the compatibility of forest biomass estimates among different scales. The results show that aboveground biomass estimates of individual trees with the same diameter are different to some extent among the forest origins and geographic regions and that a Dummy model with specific (local) parameters is better than a population-average model. The Dummy Variable model can be applied to develop other generalized compatible models such as tree volume equations.
Dehai Zhao - One of the best experts on this subject based on the ideXlab platform.
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an empirical comparison of two subject specific approaches to dominant heights modeling the Dummy Variable method and the mixed model method
Forest Ecology and Management, 2008Co-Authors: Mingliang Wang, Bruce E Borders, Dehai ZhaoAbstract:Abstract The varying (local) parameter(s) in site index models can be treated as fixed or random. Two primary subject-specific approaches to height modeling, the Dummy Variable method (fixed individual effects) and the mixed model method (random individual effects), were compared using Chapman–Richards type models fitted to second-rotation loblolly pine (Pinus taeda L.) data from a designed experiment. For height prediction of new growth series, tested on our validation subset data, the mixed model provides a new (local) parameter prediction method (termed as mixed predictor), which generally performed better than the traditional method of recovering local parameters (the least squares (LS) predictor we used). However, using the LS predictor, both the Dummy Variable estimation method and mixed model estimation showed almost identical prediction results. With multiple pairs of height–age measurements, no big difference was found in empirical site index prediction between the LS and mixed predictor. Theoretically, one main advantage of the mixed model approach is the ability of its mixed predictor to predict several local parameters using a single height–age pair. However, our empirical results failed to support this point.
Thomas Cornelissen - One of the best experts on this subject based on the ideXlab platform.
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inteff3 stata module to compute partial effects in a probit or logit model with a triple Dummy Variable interaction term
Statistical Software Components, 2009Co-Authors: Thomas Cornelissen, K SonderhofAbstract:inteff3 computes partial effects in a probit or logit model with a triple Dummy Variable interaction term. These models may be applied when the effect of a binary regressor on a binary dependent Variable is allowed to vary over combinations of two sub-groups. For example, one may be interested in the way a university degree and the presence of children affect the gender difference in labour market participation. To this effect, one may run a probit model of labour market participation including dummies for female, university degree and presence of children, as well as their pairwise and triple interaction terms.
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Marginal effects in the probit model with a triple Dummy Variable interaction term
2008Co-Authors: Thomas Cornelissen, K SonderhofAbstract:In non-linear regression models, such as the probit model, coefficients cannot be interpreted as marginal effects. The marginal effects are usually non-linear combinations of all regressors and regression coefficients of the model. This paper derives the marginal effects in a probit model with a triple Dummy Variable interaction term. A frequent application of this model is the regression-based difference-in-difference-in-differences estimator with a binary outcome Variable. The formulae derived here are implemented in a Stata program called inteff3 which applies the delta method in order to compute also the standard errors of the marginal effects.
Erwin Diewert - One of the best experts on this subject based on the ideXlab platform.
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Adjacent Period Dummy Variable Hedonic Regressions and Bilateral Index Number Theory
2010Co-Authors: Erwin DiewertAbstract:The paper addresses whether hedonic regressions should be weighted (by either quantities sold or expenditures on the model) or not. To make some progress on the issue of what weights to choose, the paper uses the time Dummy Variable hedonic regression model applied to only two periods. In this framework, the time Dummy becomes a measure of price change between the two periods and a transformation of the estimated Dummy Variable can be regarded as a generalized bilateral index number formula. The axiomatic properties of this measure of price change are examined for various alternative weighted schemes. If the models in the two periods are exactly the same, the hedonic regression measure of price change reduces to a bilateral index number formula.(This abstract was borrowed from another version of this item.)
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weighted country product Dummy Variable regressions and index number formulae
Review of Income and Wealth, 2005Co-Authors: Erwin DiewertAbstract:The article considers a very simple type of hedonic regression model where the only characteristic of a commodity is the commodity itself. This regression model is known as the country product Dummy method for calculating country price parities in the context of making international comparisons. The paper considers only the two country or two period case and introduces value or quantity weights into the regression. The resulting measures of overall price change between the two countries or time periods are compared to traditional bilateral index number formulae. It is shown how the Geary Khamis, Walsh and Tornqvist price indexes can be obtained as special cases of this framework.
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Adjacent Period Dummy Variable Hedonic Regressions and Bilateral Index Number Theory
Annales d'Économie et de Statistique, 2005Co-Authors: Erwin DiewertAbstract:The paper addresses whether hedonic regressions should be weighted (by either quantities sold or expenditures on the model) or not. To make some progress on the issue of what weights to choose, the paper uses the time Dummy Variable hedonic regression model applied to only two periods. In this framework, the time Dummy becomes a measure of price change between the two periods and a transformation of the estimated Dummy Variable can be regarded as a generalized bilateral index number formula. The axiomatic properties of this measure of price change are examined for various alternative weighted schemes. If the models in the two periods are exactly the same, the hedonic regression measure of price change reduces to a bilateral index number formula.
