Econometrics Analysis on U.S. Economy

Introduction

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The topic selected shall be Gross Domestic Product. However, due to the complexity of such data and its review the research shall base on the GDP trend for United States. This way aim to establish whether GDP has been instrumental in enabling the US to achieve equitable economic development. In the analysis part the focus is to develop a single-equation linear regression model and eventually test for GLS and non-linear regression models. In econometrics there are a number of ways to estimate a model within a single equation. For instance, the commonly used one is the ordinary least squares approach adopted in estimating linear regressions. In the case of non-linear models it applies mostly where the dependent variable is discrete and such may include logit, probit or tobit. As such a linear regression is simple to analyze, interpret and be scientifically acceptable. However, its limitation is that in most of the real-world phenomena such does not correspond to the assumptions deriving from a linear model. However, in order to make a well substantiated analysis the model estimate was developed as follows:

RGDP_t = β₀ + β₁GS + β₂MS + β₃FDI + ε_t

Where in the model, the parameters are:

RGDP = GDP

GS = Government Spending

MS = Money Supply

FDI = Foreign Direct Investment (Net Outflows)

Thus, the focus shall be to test the model above using a number of regression models to as to ascertain whether GDP in the United States has been of any significance to other development initiatives in the country such as government spending, money supply and foreign direct investment where the latter three are the indicator variables for sustainable development. The following NULL hypotheses are proposed:

H1: GDP has been a significant predictor of government spending in the U.S.

H2: GDP has been a significant predictor of money supply in the U.S.

H3: GDP has been a significant predictor of foreign direct investment in the U.S.

The model shall be estimated and tested based on both linear and non-linear regression parameters. E-Views shall be used to generate all statistical analysis.

Data analysis and evaluation

To start with are the descriptive statistics for the main variables.

Table 1: Descriptive statistics results………………………………………………………………………