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# File2-page202 - x . Allows us to graph out calorie...

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Poverty and Undernutrition (ii) Ordinary least squares (OLS) regression (iii) Instrumental variable (IV) regression Non-Parametric Estimates Work with regression of the form: m ( x ) = E p y x P where x is the logarithm of per-capita total household expenditure and y is logarithm of per-capita calorie availability. Smooth local regression technique: for any x (or band of x ) run a weighted linear regression of logarithm of per-capita calorie availability ( y ) on the logarithm of per-capita total household expenditure ( x ). Don’t impose a structure on the error, let the data speak for itself. Non-parametric regression: useful for examining bi-variate relationships, which are potentially non-linear. Look at the shape of relationship – is there Fattening with increasing income. then use average derivative estimators to calculate the slopes within dif- ferent bands of
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Unformatted text preview: x . Allows us to graph out calorie elasticities. Look for two things (i) is there a decline in elasticity with increasing income (ii) are calorie elasticity estimates signicantly different from zero, in par-ticular for the poor. Calculate the condence intervals to check this. Parametric Estimates In addition to expenditure, calorie availability is likely to depend on other factors, e.g., household composition. As children are likely to consume less than adults, we would expect to observe lower calorie availability in households with a greater proportion of children after controlling for household size. Another important determinant of calorie intakes is occupation . Other things being equal, we can expect to observe higher intakes in households, where Development Economics, LSE Summer School 2007 199...
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## This note was uploaded on 12/29/2011 for the course ECO 307 taught by Professor Dublin during the Spring '10 term at SUNY Stony Brook.

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