Econometric Forecast Models Assignment Help

Econometric Forecast Models

Econometric Forecast Models

Forecasting is the process or procedure of making predictions or estimates of the future based on previous and current data and most commonly by investigation of trends.

There are mainly four types of forecasting methods that financial analysts use to predict or estimate future expenses,revenues, and capital costs for a business. While there are a wide range of normally used quantifiable or quantitative budget forecasting tools we focus on the top four methods:

  • Moving average
  • Straight-line
  • Multiple linear regression.
  • Simple linear regression


There are many categories of forecasting methods and major of them are listed below:

  • Average approach
  • Drift method
  • Naïve approach
  • Qualitative vs. quantitative methods
  • Seasonal naïve approach

The error in the forecasting is the difference between the actual value and the forecast value for the corresponding time or period:

Forecasting has applications in an extensive range of fields where approximations of future conditions are valuable. Not everything can be forecasted dependably if the factors that relate to what is being forecast are well-known and well understood and there is a substantial amount of data that can be used very dependable forecasts can often be attained. If this is not the case or if the actual outcome is affected by the forecasts, the dependability of the forecasts can be significantly worse.

There are many areas where forecasting is important some of the areas are listed below:

  • Economic forecasting
  • Earthquake prediction
  • Egain forecasting
  • Supply chain management
  • Product forecasting
  • Sales forecasting


Econometrics means economic measurement; applying statistical techniques to applicable data econometrics discloses the relationships among economic variables. Econometric forecasting models are systems of interactions or relationships between variables such as GNP, inflation, exchange rates and so on. Their equations are then projected from available data, mainly collective time series.

Econometrics Toolbox provides functions or variables for modeling economic data or pieces of information. For time series analysis and modeling, the toolbox provide co-integration analysis, multivariate VARX models, univariate ARIMAX/GARCH composite models with several GARCH variants includes univariate Bayesian linear regression. User can use a range of diagnostics for model selection, including unit root, stationarity, hypothesis tests, and structural change.

This toolbox contains 50 functions that implement or execute econometric estimation procedures or process, 150 support and various utility functions and 20 functions to carry out diagnostics and statistical testing procedures. Besides, around 100 functions covering all the diagnostic and testing procedure as well as econometric estimation methods and many of the utility functions.

Some of the basic features of Econometrics Toolbox are:

  • Distinct models are available for multivariate, discrete-time data, such as VAR and VEC models.
  • The conditional mean and variance models, regression models with ARIMA errors, and Bayesian linear regression models in this toolbox are used for modeling univariate, discrete-time data.
  • User can compare nested models using misspecification tests, such as the likelihood ratio test, Wald’s test, or Lagrange multiplier test.

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