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1. (2002). Also the smoothing parameter ÃŽ is automatically selected, which is also helpful to resolve the bias from measurement error.44 Hence, we adapt Bayesian inference for penalized spline regression.
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5. Appendix A A1 Descriptive Statistics Table A1-1: Descriptive Statistics Mean Standard Deviation Tropical Livestock Units .6203018 .675447 Land (Hectare) .3552366 .4073929 Education Years of Head 1.365327 2.537335 Age of Head 48.25934 15.61703 Male Head(=1) .8029042 .3978405 Number of working age 2.933062 1.73045 Number of children 2.911103 2.01198 Number of oxen .6514964 1.011109 Productive Asset Value 57.57634 383.2702 Transfer incomed 9.277428 44.10967 Oï-farm income(=1) .440765 .4965228 Number of Crop Trees(Coïee, Enset, Eucalypts) 54.59048 222.4116 N 5647 a
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31. Descriptive Statistics is for Parametric Regression in section B3 b All money values are adjusted to 1994 price. c All assets are in terms of per adult equivalent units. d Transfer income includes remittances, gifts, or other transfers.
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33. Fortunately, ERHS has a small attrition rate of about 5% each round. Dercon and Hoddinott (2009) point out that small attrition is likely due to the fact that households cannot obtain land when moving to other areas.
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49. Krivobokova et al. (2009) propose âsimultaneous Bayesian credible bandsâ derived from MCMC simulation output. In this framework, we use truncated line basis with degree 2. Consider the regression model yi = Î0 + Î1x + K k=1 uk(x â Î)2 + + Îi, (5) where Îi are i.i.d.N(0, Ï2 Î ) and Î = (Î0, Î1, u1, ..., uK)T is the vector of regression coeïcients, and Î1 < Î2 < ... < ÎK are ïxed knots. The following priors are assigned to the error variance Ï2 Î and the prior variance Ï2 Î: Ï2 Î â IG(0.001, 0.001) and Ï2 Î â IG(0.001, 0.001).45 The construction of Bayesian credible bands is based on the posterior distribution and the Figure B1-1: Simultaneous Conïdence Bands for Penalized Splines conïdence region IÎ is deïned in terms of the posterior distribution of f = f(x1), ..., F(XN )T ,
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67. We adapt alternative estimation methods utilizing the GNU Software R for statistical computing and graphics using ConfBands (Krivobokova et al., 2009), np (Hayïeld and Racine, 2010), plm (Croissant, 2010), quantreg (Koenker, 2009), and SemiPar (Wand et al., 2005) packages. 3
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35. Poverty Status and the Impact of Formal Credit on Technology Use and Wellbeing among Ethiopian Smallholders. (2010). Winter-Nelson, Alex ; Liverpool-Tasie, Lenis ; Liverpool, Lenis Saweda O., .
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40. “Poor stays poor” - Household asset poverty traps in rural semi-arid India. (2009). Naschold, Felix.
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41. Social Network Capital, Economic Mobility and Poverty Traps. (2008). Chantarat, Sommarat ; Barrett, Christopher.
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44. Social Network Capital, Economic Mobility and Poverty Traps. (2007). Chantarat, Sommarat ; Barrett, Christopher.
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