Stochastic Frontier Analysis 2

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  • Опубликовано: 4 ноя 2024

Комментарии • 7

  • @vikubruno
    @vikubruno 8 месяцев назад

    Dear Sir, Thanks for this detailed explanation on how to run sfa on R. I used similar steps from your session to estimate the labour use efficiency in an agro system. The results I got were not very convincing as all the variables were either not significant with very low P-values, a very high mean efficiency value of 0.90, and a gamma value of 0.99. I am a little skeptical about the results since they are not in line with what was expected. Could you guide me on what possible steps I could take or an explanation on what could be the issue? Thanks in advance

    • @joshuaolusegunajetomobi7851
      @joshuaolusegunajetomobi7851  8 месяцев назад

      Check the sigma v coefficient and p value as well as the gamnavar. Inefficiency may not be an issue. Before you conclude, you may wish to consider the type of distribution assumptions for the model. Which one did you use? Also the functional form used may impose further restrictions on your model

  • @bijoydey479
    @bijoydey479 3 года назад

    U have done good Job sir

  • @scholars.home999
    @scholars.home999 4 года назад

    Hi Joshua. Thanks for the amazing video. I am getting an error when I am trying to use the sfa function in R. The system says "could not find function "sfa". I have followed the video step by step and will appreciate your help to overcome this issue. Thanks in Advance

    • @joshuaolusegunajetomobi7851
      @joshuaolusegunajetomobi7851  3 года назад

      sfa function can be found in frontier package. install the package and load it with library(frontier)

  • @sumitbhardwaj8234
    @sumitbhardwaj8234 2 года назад

    Dear sir. The sfa command is not working as it shown to me that number of parameters more than the number of observations. in such case, what can i do?

    • @joshuaolusegunajetomobi7851
      @joshuaolusegunajetomobi7851  2 года назад

      this is a typical case of small sample. If possible, increase your sample size or reduce your number of independent variables