The unbalanced nested error component regression model
By Badi H. Baltagi, Seuck Heun Song, B. Jung. 2001
137 citations recorded by the source corpus.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/74c107ef614b3cb61893b40e0693c83aad2f504c
How researchers see this paper
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In Economics and Econometrics
- Sophia Rabe-Hesketh
- A. Skrondal
- L. Serven
- Badi H. Baltagi
- Andrew Pickles
- William H. Greene
- Giancarlo Spagnolo
- Humberto López
Citations in the corpus
- Competition Policy and Productivity Growth: An Empirical Assessment (2013, methodology): “First, since our data have a nested structure, as an industry is ’naturally’ nested within a country, we follow Baltagi et al. (2001) and estimate several mixed-models to fit two-way, multilevel effects by maximum likelihood.”
- Evolution of Forecast Disagreement in a Bayesian Learning Model (2008, background): “Batchelor (2007) also noted that forecasters seem to start their forecasting round by adopting a relatively optimistic or pessimistic view of growth, and those who start optimistic one year also start off being optimistic in other years.”
- Estimating Econometric Models with Fixed Effects (2001, background): “Citing Greene (1993), Baltagi (1995) also remarks that the fixed effects logit model as proposed by Chamberlain (1980) is computationally impractical with T > 10.”
- Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects (2005, background): “Recent publications on random effects and multilevel models in economics and econometrics include Antweiler (2001), Baltagi et al. (2001), Beron et al. (1999), Blundell and Windmeijer (1997), Cardoso (2000), Carey (2000), Davis (2002) and Rice and Jones (1997).”
- A normal relationship ? Poverty, growth, and inequality (2006, methodology): “13 See Rabe-Hesketh et al. (2004) on the computational aspects of ML estimation in this context.”
- Hedonic Housing Prices in Paris: An Unbalanced Spatial Lag Pseudo‐Panel Model with Nested Random Effects (2015, background): “While one can try and include as many as possible of the neighborhood characteristics in the regression to capture these effects, most attempts may fall short as many neighborhood characteristics are not observed, as in our case. For the importance of spatial effects in housing studies, the reader c”