Maximum Likelihood Estimation of Stochastic Frontier Models with Interval Dependent Variables

2016 ◽  
Author(s):  
Wen-Jen Tsay ◽  
Tsu-Tan Fu
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Stochastic Frontier ◽  
Likelihood Estimation ◽  
Stochastic Frontier Models ◽  
Dependent Variables

scholarly journals Analisis Efisiensi Teknis Pada Usahatani Kedelai (Glycine max (L.) Merril) Di Kecamatan Peudada Kabupaten Bireuen, Aceh

Jurnal Agrium ◽  
2017 ◽  
Vol 12 (1) ◽  
Author(s):  
Riza Putri ◽  
Murdani Murdani ◽  
Fadli Fadli
Keyword(s):  
Glycine Max ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Stochastic Frontier ◽  
Likelihood Estimation ◽  
Stochastic Frontier Model ◽  
Snowball Sampling ◽  
Glycine Max L

Penelitian ini bertujuan untuk menganalisa tingkat efisiensi teknis menggunakan factor produksi pada usahatani kedelai di Kecamatan Pedada, Kabupaten Bireun. Pengambilan sampel menggunakan teknik snowball sampling menggunakan model fungsi Stochastic Frontier Model, koefisien regresi dihitung menggunakan metode Maximum Likelihood Estimation (MLE). Hasil penelitian menunjukkan bahwa ukuran lahan and benih merupakan variabel yang sangat mempengaruhi peningkatan produksi kedelai. Rata-rata tingkat efisiensi teknis input yang digunakan adalah 83% dari potensi produksi maksimum yang akan didapatkan. Umur petani merupakan faktor yang berpengaruh nyata terhadap ketidak efisiensian usaha teknis untuk meningkatkan produksi kedelai

Maximum Likelihood Estimation of Models with Beta-Distributed Dependent Variables

2001 ◽  
Vol 9 (4) ◽  
pp. 325-346 ◽  
Author(s):  
Philip Paolino
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Monte Carlo Simulations ◽  
Interest Groups ◽  
Political Science ◽  
Beta Distribution ◽  
Likelihood Estimation ◽  
Dependent Variables ◽  
Key Aspects

Research in political science is often concerned with modeling dependent variables that are proportions. Proportions are relevant in a wide variety of substantive areas, including elections, the bureaucracy, and interest groups. Yet because most researchers rely upon an approach, OLS, that does not recognize key aspects of proportions, the conclusions we reach from normal models may not provide the best understanding of phenomena of interest in these areas. In this paper, I use Monte Carlo simulations to show that maximum likelihood estimation of these data using the beta distribution may provide more accurate and more precise results. I then present empirical analyses illustrating some of these differences.

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