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Econometric analysis studies the quantitative relationship among economic variables. It is a vital
subject that provides managers with essential input that can boost their decision making. Econometrics is a social
science that primarily considers problems from the economic background instead of those from other disciplines. It
deals with high-level data aggregation and employs various techniques in the data analysis process. At Statistics
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A model is a representation of a real-world process. It should contain salient features of the phenomena
being studied. In economics, a model is simply a set of assumptions that describes the behavior of an economy.
Typical econometric models consist of the following:

- Equations describing the behavior. These equations are usually made up of observed variables and disturbances derived from the economic model. Disturbances are random variables.
- A statement on the errors in the observed values of variables
- The probability distribution of disturbances

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**Aims of econometrics**

Our statistics assignment helpers have explained the three common goals of econometrics below:

- Specification and formulation of econometric models

Economic models should be formulated in an empirically testable form. A single economic model can also give forth to several other models. However, the models may differ because of;

The specification of the stochastic structure of the variables

The different choice of the functional form.

The statistical inference of modeling involves estimating models based on the observed set of data and
suitability testing. A statistician can use a variety of estimation procedures to know the numerical values of the
unknown parameters in a model. Also, various formulations of statistical models can assist in choosing a suitable
and appropriate model.

Policymakers can use econometric models for forecasting and decision making. Additionally, by studying the models, they can judge the whole process and re-adjust the relevant economic variables.

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Statistics techniques of measurement are often developed on the basis of controlled experiments. However,
economic phenomena don’t fit into the framework of controlled experiments. As a result, these techniques may not be
suitable because, in real-world experiments, the variables usually change continuously and simultaneously. It is for
this reason that controlled experiments may not be effective.

Econometrics first adapts statistical methods to the problems of economic life before using them. The
adopted techniques are often termed econometric methods. These methods have been adjusted to become appropriate for
measuring stochastic relationships.

**Theoretical econometrics**

It involves the development of effective methods for measuring economic relationships. These economic
relationships cannot be measured using controlled experiments done in the laboratories. Theoretical econometrics
methods are used to analyze non-experimental data.

**Applied econometrics**

It applies econometric methods to specific theories and problems like demand and supply, investment, consumption, production, etc. Applied econometrics analyzes economic phenomena and predicts economic behaviors.

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**Time series data**

Just like the name suggests, time-series data provides information about the numerical values of
variables within a period or overtime. An example of this type of data can be the monthly income constitutes for the
year 2010.

**Cross-section data**

This is data concerning individual entities such as consumers, producers, creditors, etc. at a given
point in time. Examples of cross-section data include information on a family’s income, expenditure on various
commodities by a family, a list of consumers, etc.

**Panel data**

Panel data is a cross-section data from a repeated survey of a single sample during different periods.

**Dummy variable data**

The data for qualitative variables is usually recorded in the form of an indicator function. The values
of the variables reflect only the presence or absence of a characteristic, but not the magnitude of the data. For
example, the variable taste can take values like and dislike. Sex can only be male or female. These values can be
denoted using a dummy variable. In our example above, in sex, ‘1’ can represent male while ‘0’ represents female.
Similarly in taste, ‘1’ can represent like, and ‘0’ dislike

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Our services cater to all topics and sub-divisions of econometrics analysis. Feel free to seek the help of our proficient and adept experts in the following areas:

- Ordinary Least Squares Method.
- Linear Regression.
- Residual Regression.
- Bayesian Statistics.
- T statistic.
- Numerical Optimizati.
- Non-Parametric Regression.
- p values.
- Time Series.
- Matrix Algebra.
- Probability.

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that our clients regularly contact us for assistance. Our experts are highly qualified and experienced in statistics
and econometrics related courses.

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