Questions tagged [two-step-estimation]

Models in which a complicated function of data is estimated in the first step, and plugged again into another estimation model of primary interest in the second step

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Inverse Mills ratio after OLS

Short version of the question: Is it possible to create a dependent variable in the first step of the Heckman Selection model such that it is possible to obtain the values for the calculation of the ...
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Instrumental variable Tobit in R

I have a data generating process of the form: ...
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Question about inverse in a two-step estimator as a joint GMM-estimators approach

I'm reading Newey & McFadden - Large sample estimation and hypothesis testing (in the Handbook of Econometrics, Volume 4, 1994, page 2178). My model which I'm interested in has some former ...
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Fitting a fixed effect model to the residuals from a mixed effects model

In some statistical analyses (ie genetics), it may makes sense to perform a two-step regression analysis. In this analysis, the dependent variable is regressed against several independent variables. ...
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Heckman sample selection vs. OLS

If the mills ratio of a Heckman selection model (with/without exclusion restriction) is not significant, shall I prefer to estimate my model with OLS instead? Or is it better to use the estimates from ...
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Two step regression using group effects and DAG

Consider the following model $$y_i = \sigma_{c(i)} + \mathbf x_i^\top\beta + u^y_i$$ $$\sigma_{c} = z_c\lambda + \eta_c$$ where for all $i$ $$\mathbb E[u^y_i \lvert x_i] = 0$$ Data is given for a ...
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Omitting a variable in IV estimation

I have an instrumental variable (IV) estimation where I use Z as an instrument for treatment D, to estimate a treatment effect of D on Y. After certain discussion, I find that there might be another ...
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2SLS or IV with a tobit distribution in the first stage

I would like to use a two stage least squares approach (2SLS), where the first stage would benefit from a Tobit specification. I cross posted this on stackoverflow because there might be quite some ...
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Two-Step Procedure to account for multicollinearity

Suppose the estimation equation is $$y=\beta _{0}+\beta _{1}x_{1}+\beta _{2}x_{2}+\varepsilon$$ where $\varepsilon$ is a disturbance and $x_{1}$ and $x_{2}$ are highly correlated regressors. In a ...
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Heckman selection model: probit selection & logit outcome

I have a situation where I think I need to use a Heckman selection model to correct for endogeneity. I am interested in studying the effect of firm's market entry mode on its performance. Factors that ...
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Using random intercepts in a multilevel model as dependent variables in a linear model

I have a mixed model with 3 levels: individual, city, and state, and so I get random intercepts for both cities and states. I understand that since cities are nested in their state, their intercepts ...
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Interaction Term in Fuzzy RD

I'm hoping someone can help me understand the intuition behind the interaction term in a fuzzy RD model. The setup is as follows: $x$ = rating variable with discontinuity at $x = k$ $D$ = dummy=1 if ...
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Goodnes of Fit Measure for Heckman Selection Model

I am working with a two-step heckman selection model. In the first step the selection occurs based on a probit model, in the second step the mean equation is fitted with a linear model where the ...
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Estimation of Covariance Matrix of Two-Sample-Two-Stage-Least-Squares Estimator

My model of interest is given by $Y_1 = X_1\beta + \epsilon_1$ with $Y_1\in\mathbb{R}^{n_1}$ , $X_1\in\mathbb{R}^{n_1}$ , $\beta\in\mathbb{R}$ and $\epsilon_1\in\mathbb{R}^{n_1}$. However, $X_1$ can ...
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tobit two-stage in R

I'm working with a dependent variable $y_i \in [0,1]$. I have a single endogenous explanatory variable $w_i \in [0, 1]$ with corresponding instrument $z_i \in \{0,1\}$. Suppose further that I have a ...
942 views

Regression estimate of a non-negative variable

I have to estimate linear weight $\beta$ for regression $Y \sim \mathbf{X}$, where $Y$ are non-negative samples. If I perform vanilla regression (lets assume ridge regression) it will find $\beta$ ...
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Hausman test fails to reject the H0 (and the instrument is not weak), should I still use the 2SLS based on knowledge that the variable is endogenous?

I have two questions. I am conducting the Hausman test to check the endogeneity of a variable. If the Hausman test fails to reject the null hypothesis, there is no difference between OLS (my reference ...
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heckman two step, a basic question

This is probably a very basic questions, but I cannot find a straight forward answer anywhere. I have a series of data were a selection method (like Heckman two-step) is necessary. Is it a basic ...
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Hausman test for 2SLS vs 3SLS

Can we do a Hausman test for 2SLS vs 3SLS? I know that we can do a BP test for the cross-equation correlation of errors, but what should the null and alternative hypotheses of a Hausman test be?
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First stage of TSLS and the matrix of instruments W

If we assume we have 2 equations and each equation contains the other dependent variable. $y_1 = \beta_0 + \beta_1 y_2 + \beta_2 z_1 + u_1$ $y_2 = \alpha_0 + \alpha_1 y_1 + \alpha_2 z_2 + u_2$ For ...
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Using a regression coefficient as independent variable

Two continuous variables, $Z$ and $X$, are measured at 60 time points during a week for a given person. At the end of the week, the persons value of a variable $Y$ is measured. This is repeated during ...
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How can I derive OLS predicted error term ^ei as a function of ei?

First of all, I'd like to say that any kind of help would be really helpful, whether it's a hint or a good grad/undergrad book. Right now I'm working with Econometric Analysis of Cross Section and ...
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What is the effect of an omitted variable in the first stage of an IV/2SLS

Background Let me start with the fact that I read, this post, this post and this post. These questions deal with omitting exogenous variables from the main equation in the first stage. My question is ...
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Generating variance estimates for Estimated Regression Models (EDV) (vwls)

I am trying to explain the expectation of university graduation among adolescents of a number of countries. My dependent variable is dichotomous, since they formulate their expectation in terms of ‘...
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unified estimation of discrete Markov Model

Background I have a multivariate dataset, say M x N, where M is the number of variables and N is the number of samples. Now, the pattern of dependencies between the M variables changes across the N ...
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Cluster analysis in SPSS

I started learning cluster analysis (using SPSS) and I need some help in a practical problem. Given the following variables: The respondents were asked to indicate the importance of the following ...
1 vote
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How to combine heckman selection and binary endogenous variable in a two-step way?

I want to fit a probit model with a binary endogenous variable and heckman sample selection problem, it's something like ...
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2-stage Heckman instrumental variable estimation

I am working on my thesis. My main regression model is the following: $Y=x_1*{\rm Payment}+x_2*{\rm Country}+x_3*{\rm Industry}...$ All independent variables are dummy / binary variables. In a next ...
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Control function bootstrapped standard errors

Using a control function approach allows to incorporate non-linear first-stage specifications. A problem when doing this approach is that the naive standard errors are too small since they do not ...
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Is there any example of 2-step predictive model?(i.e. classification model coupled with regression models for each subclass)

I have a large dataset with 10,000+ individuals and many many biological features (>5000). And I want to use these features to build a linear model (e.g. elastic net) to predict their clinical ...
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Making predictions for a new dataset using the control function approach

I am using a control function approach with a probit model for the selection equation and a fractional model for the outcome equation. From the selection equation, I calculate the generalized ...
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Estimating Direct Effect with Conditional Models

I was recently considering the following: Suppose we have an experimental set-up where we have collected observations over thousands of locations (S) before and after treatment (T). Further, we have ...
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What is an initial consistent estimator and how do I find one?

When maximizing a likelihood function $L(\psi)$, the gradient-based optimization procedure is generally  \tag{5.1} \hat{\psi}_{r+1} = \hat{\psi}_{r} + \left| I^{*}(\hat{\psi}_{r}) \right|^{-1} D \...
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Two part model - conditional expectation

I am working on a model to predict a continuous target variable $Y$, given a feature set $F$. $Y$ is product of two continuous variables - $A$ and $B$, where $A \epsilon [0, 1]$ and \$B \epsilon (0, \...
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How to perform inference on inverse Mills ratio in Heckit estimation?

I want to estimate log(wages). Most wage estimations suffer from sample selection bias. So I used the two step heckit procedure to correct for it. The problem is that I get an insignificant inverse ...