Econometric Analysis Assignment Help

Econometric Analysis Assignment Help

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 economics 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 Assignment Experts, we know the trouble students go through when asked to prepare econometric assignments. The concepts are vast and complicated. We heard the cry of students for an excellent econometric analysis homework help service and have responded favorably. Now, you don’t have to stress about your grades or panic about your strict deadlines.  Hire our statistics tutors today and get your homework prepared by top-rated experts.

Econometric Models

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 provides instant help with all assignments related to econometric models. Get our econometrics analysis assignment help at the comfort of your home and receive excellent solutions on time.

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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
  • Different choice of the functional form.


  • Testing and estimation of models

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.

  • Using models

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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Econometrics and statistics

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.

Types of econometrics

  • 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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Various types of data used in econometric analysis

  • 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 of 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 Optimization
·         Non-Parametric Regression ·
·         p values ·
·         Time Series ·
·         Matrix Algebra ·
·         Probability ·

The catalog above is not comprehensive or exhaustive. It only contains some of the popular sub-fields 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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