This study investigates the relevant factors that drive income and wealth inequality in the United States with the aim of facilitating a better understanding of the dynamic relationships between inequality and key macroeconomic variables. This can serve as a prerequisite to the ability of policymakers to restrain the negative externalities associated with increasing inequality and implement measures to reduce the unexpected effects. The thesis consists of five independent papers corresponding to five chapters. As economic growth is a primary goal of every country and widely accepted tool for reducing economic inequality, our study starts with economic growth. The first paper examines the relationship between the U.S. per capita real GDP and income inequality over the period 1917 to 2012. The literature uncovers a complex set of interactions, which depends on the specific research method and sample, between inequality and economic growth and highlights the difficulty of capturing a definitive causal relationship. Inequality either promotes, retards, or does not affect growth. Most existing studies that examine the inequality-growth nexus exclusively utilize time-domain methods. We use wavelet analysis which allows the simultaneous examination of correlation and causality between the two series in both the time and frequency domains. We find robust evidence of positive correlation between the growth and inequality across frequencies. Yet, directions of causality vary across frequencies and evolve with time. In the time-domain, the time-varying nature of long-run causalities implies structural changes in the two series. These findings provide a more thorough picture of the relationship between the U.S. per capita real GDP and inequality measures over time and frequency, suggesting important implications for policy makers. Inflation targeting is a monetary policy where the central bank sets a specific inflation rate as its goal. The federal government spurs economic growth by adding liquidity, credit, and jobs to the economy and inflation stimulate the demand needed to drive economic growth. The second paper investigates the effects of the inflation rate on income inequality to see whether monetary policy and the resulting inflation rate can affect income inequality and improve the well-being of individuals. Our analysis relies on a cross-state panel for the United States over the 1976 to 2007 period to assess the relationship between income inequality and the inflation rate, employing a semiparametric instrument variable (IV) estimator. By using cross-state panel data, we minimize the problems associated with data comparability often encountered in cross-country studies related to income inequality. We find that the relationship depends on the level of the inflation rate. A positive relationship occurs only if the states exceed a threshold level of the inflation rate. Below this value, inflation rate lowers income inequality. The results suggest that a nonlinear relationship exists between income inequality and the inflation rate. The researchers also examine the relationship between income inequality and growth in personal income, since personal income exerts a large effect on consumer consumption, and since consumer spending drives much of the economy. The third paper investigates the causal relationship between personal income and income inequality in a panel data of 48 states for the period of 1929-2012. Although inequality rose almost everywhere between 1980 to present, some states and regions experienced substantially greater increases in inequality than did others. The decentralization allows different state level of policies, however, there is also a cross-state consistency in how those policies respond to the main economic shocks. Since U.S. states are subject to significant spatial effects given their high level of integration, ignoring cross-sectional dependency may lead to substantial bias and size distortions. We employ a causality methodology proposed by Emirmahmutoglu and Kose (2011), as it takes into account possible slope heterogeneity and cross-sectional dependency in a multivariate panel. Evidence of bi-directional causal relationship exists for several inequality measures -- the Atkinson Index, Gini Coefficient, the Relative Mean Deviation, TheiliÌ8℗¿℗ưs entropy Index and Top 10% -- but no evidence of the causal relationship for the Top 1 % measure. Also, this paper finds state-specific causal relationships between personal income and inequality. The level of development of the United States is related to the sophistication of the financial structure which influences the ability to hedge against shocks and to loosen spending constraints. It leads us to investigate if the financial development affects income inequality in the U.S. In the fourth paper, we look into the role of financial development on U.S. state-level income inequality in a panel data of 50 states from 1976 to 2011. To our knowledge, this paper is the first regarding examining the role of financial development on U.S. state-level inequality. We analyze the data using Fixed Effect and Dynamic Fixed Effect regression. We also divide 50 states into two groups-states, with higher inequality measure and states with lower inequality measures than average of the cross-state average of the inequality, to examine the possible nonlinear impact of financial development on income inequality. We find robust results whereby financial development linearly increases income inequality for the 50 states. When we divide 50 states into two separate groups of higher and lower inequality states than the cross-state average inequality, the effect of financial development on income inequality appears non-linear. When financial development improves, the effect increases at an increasing rate for high income inequality states, whereas an inverted U-shaped relationship exists for low-income inequality states.