8.2 Nonlinear Functions of a Single Independent Variable.8.1 A General Strategy for Modelling Nonlinear Regression Functions.7.6 Analysis of the Test Score Data Set.Model Specification in Theory and in Practice.7.5 Model Specification for Multiple Regression.7.4 Confidence Sets for Multiple Coefficients.

7.3 Joint Hypothesis Testing Using the F-Statistic.7.2 An Application to Test Scores and the Student-Teacher Ratio.7.1 Hypothesis Tests and Confidence Intervals for a Single Coefficient.7 Hypothesis Tests and Confidence Intervals in Multiple Regression.6.5 The Distribution of the OLS Estimators in Multiple Regression.Simulation Study: Imperfect Multicollinearity.

6.4 OLS Assumptions in Multiple Regression.6.3 Measures of Fit in Multiple Regression.6 Regression Models with Multiple Regressors.5.6 Using the t-Statistic in Regression When the Sample Size Is Small.Computation of Heteroskedasticity-Robust Standard Errors.Should We Care About Heteroskedasticity?.A Real-World Example for Heteroskedasticity.5.4 Heteroskedasticity and Homoskedasticity.5.3 Regression when X is a Binary Variable.5.2 Confidence Intervals for Regression Coefficients.5.1 Testing Two-Sided Hypotheses Concerning the Slope Coefficient.5 Hypothesis Tests and Confidence Intervals in the Simple Linear Regression Model.4.5 The Sampling Distribution of the OLS Estimator.Assumption 3: Large Outliers are Unlikely.Assumption 2: Independently and Identically Distributed Data.Assumption 1: The Error Term has Conditional Mean of Zero.4.2 Estimating the Coefficients of the Linear Regression Model.3.7 Scatterplots, Sample Covariance and Sample Correlation.3.6 An Application to the Gender Gap of Earnings.3.5 Comparing Means from Different Populations.3.4 Confidence Intervals for the Population Mean.Hypothesis Testing with a Prespecified Significance Level.Calculating the p-value When the Standard Deviation is Unknown.Sample Variance, Sample Standard Deviation and Standard Error.Calculating the p-Value when the Standard Deviation is Known.3.3 Hypothesis Tests Concerning the Population Mean.Large Sample Approximations to Sampling Distributions.2.2 Random Sampling and the Distribution of Sample Averages.Probability Distributions of Continuous Random Variables.Probability Distributions of Discrete Random Variables.2.1 Random Variables and Probability Distributions.

1.2 A Very Short Introduction to R and RStudio.

metobit Total_fund GDP_capita || region:, ll(0) I tried several combinations of packages and functions but none of that seems to be working.īasically I would like to replicate the following Stata command in Rstudio. I found out that in Stata there is an metobit command that does this and I would like to replicate it in Rstudio. I can't find a way to fit this kind of multilevel mixed-effects Tobit model in Rstudio. Moreover, I would like to apply random effects at the regional level. Hence I would like to account for this peak in the zeros by running a Tobit model. The distribution has a peak in 0, with 312 observations out of 968 that assume value 0. The dependent variable is the amount of transfers received by a region in a given year. My dataset comprises transfers (in euros) to Italian regions from 1989 to 2018 and other several economic and political variables. I am trying to run a multilevel mixed-effects Tobit regression in Rstudio but I can't find a way to do it.

R studio commands regression