Malnutrition has been a serious problem in developing countries since long and appropriate steps are required to take care of this issue. According to Planning Commission of Pakistan (Vision 2030), about half of the population in the country suffers from absolute to moderate malnutrition, with the most vulnerable being children, women and elderly among the lowest income group. However, a decent society cannot be built on the ruins of hunger, malnutrition and ill health. One important policy question which needs careful consideration is how to improve the nutrient consumption? To highlight this issue, this dissertation provides an in depth analysis of nutrient demand in Pakistan by using micro-level data of 2005-06 and 2010-11 from Pakistan Bureau of Statistics Islamabad. The study tries to probe linkages between nutrient demand with two of its major aspects - income and prices - by employing various modern statistical approaches. Both aspects are studied at country level. The calorie-income relationship has been extensively studied in literature but exploration of macro and micro-nutrient relationship with income is for the most part an undiscovered territory. The present study made an effort to provide background knowledge of this subject in general and with reference to Pakistan in specific. A large heterogeneity exists about the size and significance of nutrientincome relationship in literature. To capture and analyze the heterogeneity in estimates across studies we have used Meta Regression Analysis. The study concludes that overall effect of income is positive and significant on nutrient consumption. In the light of these results, we further quantify empirical relationship between nutrient consumption and income for Pakistan by using parametric (linear and nonlinear specifications), nonparametric and semi-parametric estimation techniques. The parametric linear specification shows the calorie-income elasticity 0.38 and range of 0.28 to 0.65 for rest of macro and micronutrients. Instrumental variable approach provides lower estimates than those of OLS but significant. Similarly the flexible nonlinear parametric approach gives sizeable and significant estimates. Overall, the results of these estimation techniques indicates that, with few exceptions, there is not much difference in the qualitative nature of results that is size and significance of parameter estimates across these models. Our results for Pakistan are in line with World Bank view that increases in income can improve the nutrient consumption. We further examined the impact of food price crises on nutrient demand. For this we used Time Varying Regression model and Quantile Regression method. The results of food price crisis on nutrient demand reveal that rise in food prices have very heterogeneous impact on nutrient demand. Besides the main variable of interest in all estimation techniques, we also used the household level characteristics across estimation techniques. After controlling for household and district level characteristics, we find that main variable of interest remains significant. On the basis of these results, we suggest that efforts should be made for high economic growth in the long run. However, in the short run, income assistance and price mitigating policies for low income groups can improve their nutritional status.