module 3 fore cat ing tech 1

8/2/2019 Module 3 Fore Cat Ing Tech 1

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Module 3: Forecasting Technique 1

Module objectives (Overall objectives): Module 3 is somewhat technique oriented and

strives to identify those concepts that relate to many different forecasting methodologies and

that provide the vocabulary and basis for understanding a wide range of forecasting

techniques.

Unit 3.1: Basic Econometrics 1

a) Objectives: This chapter will seek to review the main ideas and concepts underlying

regression models.

b) Topics: 1) Types of forecasting techniques

2) Simple Regression

i. The correlation coefficientii. The significance of a regression equation

3) Multiple Regression

i. Introduction to multiple linear regression

ii. Selecting Independent Variables and Model Specification

iii. Multicollinearity

iv. Multiple Regression and Forecasting

1)

Types of Forecasting Techniques

Quantitative forecasting can be applied when three conditions exist:

1) Information about the past is available

2) This information can be quantified in the form of numerical data

Forecasting Techniques

Quantitative Qualitative

- Time series

- Causal Method

- Exploratory

- Normative method



3) It can be assumed that some aspects of the past pattern will continue into the future.

Quantitative Forecasting Methods Qualitative Forecasting

MethodsTime Series Forecasting

Method

Regression (causal)

Models

- Do not require data in

the same manner as

quantitative forecasting

methods

- The inputs required

depend on the specific

method and are mainly

the product of intuitive

thinking, judgement

and accumulated

knowledge

- Prediction of the

future is based on

past values of a

variable and/or past

errors

- The objective of this

method is to discover

the pattern in the

historical data seriesand extrapolate that

pattern into the future

- Causal models

assume that the

factor to be

forecasted exhibits a

cause-effect

relationship with one

or more independent

variables.

Eg: sales = f (income, prices,

advertising,competition, etc)

- The purpose of the

causal model is to

discover the form of

that relationship and

use it to forecast

future values of the

dependent variable.

2) Simple Regression

Simple regression will be dealing with one dependent measure (eg: sales) and one

independent measure (eg: advertising expenditure). The objective is to develop an

explanatory model relating these two measures. Figure 2 below shows this

situation.

a) Simple Regression of Y on X

i) one dependent measure (Y)

ii) one independent measure (X)

iii) n observations

Figure 2

Y1

Y2

.

.

.

.

Yn

X1

X2

.

.

.

.

Xn



All regression models are written as equations linking the dependent and

independent variables. For example, Y= 1.5 + 2.5X expresses Y as a function of X

and involves two coefficients (1.5 and 2.5).When this equation is written in its

general form, Y = a + bX, where a and b are the two coefficients.

The Correlation Coefficient

It often occurs that two variables are related to each other, eventhough it might be

incorrect to say that the value of one of the variables depends upon, or is caused

by, changes in the value of the other variables. In any event, a relationship can be

stated by computing the correlation between the two variables. The coefficient of

correlation, r is a relative measure of the (linear) association between these twovariables. When the correlation coefficient is greater than 0, the two variables are

said to be positively correlated, and when it is less than 0, they are said to be

negatively correlated.

Eg: Correlation between X and Y

rxy =

√

The F- test for Overall Significance

Y = α + βX + ε, has slope coefficient β. The F-test allows us to test the significance

of the overall regression model- to be able to answer the statistical question: Is there a

significant relationship between Y and X. F statistic is defined as follows:

F =

=

Where MS = mean square

SS = sum of squares

Df = degree of freedom



Unit 3.2: Basic Econometrics II

a. Objectives: This chapter will seek to review the main ideas and concepts

underlying econometric models, describe the statistical methods used, and finallydiscuss the role of econometric methods as a forecasting tool.

b. Topics: 1) Econometric Models and Forecasting

i. The basis of econometric modelling

ii. Specification and Identification

iii. Development and Application of Econometri Models

iv. Estimation Procedures Used with Econometric Methods

Module 4: Forecasting Technique II

Module Objectives: Module 4 is regarding to a general approach to time series

analysis and strives to introduce various concepts useful in time series analysis (and

forecasting), give definition of some general notation (that proposed by Box and

Jenkins, 1970) for dealing with time series-models and illustrates of how the concepts,

notation, and statistical tools can be combined to aid analysis of a wide variety of time

series.

Unit 4.1: Time Series I


underlying smoothing and decomposition time series methods.

b. Topics: 1) Smoothing methods

i. Averaging Methods

ii. Exponential Smoothing Methods

iii. General Aspects of Smoothing Methods

2) Decomposition Methods

i. Trend Fitting

ii. The Ratio to Moving Averages Classical Decomposition Method

iv. Different Types of Moving Averages

v. The Census II Decomposition Method

Unit 4.2: Time Series II




underlying Autoregressive/ Integrated/ Moving Average (ARIMA) models also

known as the Box-Jenkins method.

b. Topics: 1) The Box- Jenkins Method

i.

Identification1. Stationarity and Nonstationarity

2. Autoregressive Processes

3. Moving Average Processes

4. Mixture: ARMA Processes

5. Mixtures: ARIMA Processes

ii. Estimating the Parameters

iii. Diagnostic Checking

iv. Forecasting with ARIMA Models

module 3 fore cat ing tech 1

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