title: How to replicate standard Tobit results in R using mhurdle package
body: <p>I'm exploring multiple-hurdle models using the <code>mhurdle</code> package in R. To start, I trying to simply replicate the results of a standard Tobit model run using the <code>tobit()</code> function from the <code>AER</code> package or the <code>censReg()</code> function from the <code>censReg</code> package (both produce the same results). Using sample data from <code>mhurdle</code>, these latter two packages yield the same coefficient estimates for a Tobit regression, but I cannot replicate them using <code>mhurdle()</code>. Here is a basic example. I suspect I'm overlooking something rather simple, but I can't figure it out. Any idea where the misspecification lies between the two models?</p>
  
  <pre><code>library(censReg)
  library(mhurdle)
  
  ### Load sample data from the mhurdle package
  data(Interview) 
  
  ## mhurdle() results for standard Tobit specification
  summary(mhurdle(vacations ~ 0 | linc, data=Interview, dist="n", h2=T))
  
  ## censReg() results for Tobit (same as tobit() from "AER")
  summary(censReg(vacations ~ linc, data=Interview))
  </code></pre>
  
  <p>The <code>mhurdle</code> package produces the following output:</p>
  
  <pre><code>Call:
  mhurdle(formula = vacations ~ 0 | linc, data = Interview, dist = "n", 
      h2 = TRUE, method = "bfgs")
  
  Frequency of 0:  0.848 
  
  Coefficients :
                 Estimate Std. Error  t-value              Pr(&gt;|t|)    
  h2.(Intercept) -8.97938    0.54647 -16.4315 &lt; 0.00000000000000022 ***
  h2.linc         4.98411    0.61031   8.1666  0.000000000000000222 ***
  sd.sd           8.68863    0.18009  48.2463 &lt; 0.00000000000000022 ***
  ---
  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  
  Log-Likelihood: -765.67 on 3 Df
  
  R^2 :
   Coefficient of determination : 0.040817 
   Likelihood ratio index       : NA  
  </code></pre>
  
  <p>The <code>censReg</code> and the <code>AER</code> packages produce the following output:</p>
  
  <pre><code>Call:
  censReg(formula = vacations ~ linc, data = Interview, method = "bfgs")
  
  Observations:
           Total  Left-censored     Uncensored Right-censored 
            1000            848            152              0 
  
  Coefficients:
              Estimate Std. error t value              Pr(&gt; t)    
  (Intercept) -6.33252    0.53559 -11.823 &lt; 0.0000000000000002 ***
  linc         3.51493    0.46583   7.545   0.0000000000000451 ***
  logSigma     1.81278    0.06231  29.091 &lt; 0.0000000000000002 ***
  ---
  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  
  BFGS maximization, 44 iterations
  Return code 0: successful convergence 
  Log-likelihood: -712.5855 on 3 Df
  </code></pre>