Financial Analysis

Abstract

With the development of economic and technology, the competition in labor force become severe. In order to providing suggestions to the government and some social entities, this report mainly discusses the factors that contribute to the forecast of unemployment rate. The data used in this project is from Federal Reserve Bank of St. Louis and World Bank. Based on the multiple linear regression, these economic factors include GDP, Income level, Inflation, Exports and Immigration.

Keyword: liner regression, unemployment rate, economic factors

Introduction

The purpose of this project is to analyze the relationship between unemployment rate and factors that we choose in order to find out which factors have significant impact on unemployment rate by developing linear regression model. Factors include GDP, per capita income, civilian labor force, inflation rate, exports, immigration (persons obtaining lawful permanent resident), age dependency ratio and government expenditure on welfare and social service.

Research on unemployment has practical significance. As a common and important indicator of macroeconomic condition, unemployment rate reflects whether the economy is growing or weakening. The project focuses on what unemployment rate actually stands for. Finding out factors that contribute significantly to unemployment rate can help us form a more specific explanation of a change of unemployment rate. Especially when the rate is increasing, the reason of that can lead to a more targeted solution, which has a constructive significance upon policy makers.

There are three key aspects of the report: data exploration, model interpretation and conclusion.

First, through scatter plot and summary statistics, we can explore primary relationship between unemployment rate and factors. Logarithm transform is used for adjustment. Also, we identify unusual points in the data set which may negatively affect the result of regression. Second, we develop a model...