Me 4 Business Forecasting

8/6/2019 Me 4 Business Forecasting

1/28

With Knowledge We ServeWith Knowledge We ServeWith Knowledge We Serve

Business andBusiness and

Economic ForecastingEconomic Forecasting


2/28


Business andBusiness and

Economic ForecastingEconomic Forecasting


3/28


Learning OutcomeLearning Outcome

Able to conduct economic forecast using

time-series and econometric methods


4/28


FORECASTING

Likelihood of future events based on

past and current information


5/28


TYPESOFFORECASTING METHODS

Qualitative Method

Quantitative Method i. Time Series

ii. Casual


6/28


Qualitative Method Expert Opinion

Expert Judgment


7/28


Quantitative Method

Time Series Method

Generatingprocess

Inputs Output

Trend

Smoothing

Decomposition

ARIMA


8/28


Quantitative Method

Causal Method

RelationshipBetween

two or morefactors

Inputs Output

Regression

Econometric


9/28


Choosing a Forecasting Method

Lead time

Time to prepare forecast

Pattern of data

Data requirements

Ease ofunderstanding

Cost

Accuracy


10/28


Time Series Technique


11/28


COMPONENTSOFATIMESERIES

Yt = St x Tt x Ct x Rt

Y= a time series

S= seasonality

T = trend

C= cycle

R= random/irregular


12/28


Decomposition Method

Yt = St x Tt x Ct x Rt

MA = T x C

Y/ MA = S

T = a + bt


13/28


COMPONENT OF A TIME SERIES


14/28


TREND ESTIMATION


15/28


SEASONAL ESTIMATION

Ratio to Trend Method (pg. 170-171)

Dummy Variables (pg. 171-172)


16/28


CYCLICAL ESTIMATION

Sine + Cosine Method to estimate a

wavelength


17/28


TREND AND SEASONAL COMPONENTS


18/28


Forecasting

Palm

Oil production


19/28


Trend Equation

Dependent Variable: PDPO

Method: Least Squares

Date: 02/27/05 Time: 21:21

Sample (adjusted): 1980M01 2004M12

Included observations: 300 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 122.4347 13.91131 8.801090 0.0000

T 3.155101 0.080117 39.38130 0.0000

R-squared 0.838822 Mean dependent var 597.2774

Adjusted R-squared 0.838281 S.D. dependent var 298.8351

S.E. of regression 120.1744 Akaike info criterion 12.42241

Sum squared resid 4303683. Schwarz criterion 12.44710

Log likelihood -1861.361 F-statistic 1550.887

Durbin-Watson stat 0.363132

Prob(

F-statistic) 0.000000


20/28


TREND LINE

- 4 0 0

- 2 0 0

0

2 0 0

4 0 0

6 0 0

0

4 0 0

8 0 0

1 20 0

1 60 0

8 0 8 2 8 4 8 6 8 8 9 0 9 2 9 4 9 6 9 8 0 0 0 2 0 4

e s i d u a l A c tu a l F i t te d


21/28


Seasonal IndexSample: 1980M01 2004M12

Included observations: 300

Ratio to Moving Average

Original Series: PDPO

Scaling

Factors:

1 0.816493

2 0.755568

3 0.864866

4 0.917363

5 0.976460

6 1.000028

7 1.099330

8 1.183448

9 1.280867

10 1.235198

11 1.096170

12 0.927304


22/28


SEASONAL INDEX

0 .

0 .8

0 .9

. 0

1 . 1

1 .

1 .3

8 0 8 8 4 8 6 8 8 9 0 9 9 4 9 6 9 8 0 0 0 2 0 4

O N


23/28


Causal Technique


24/28


ECONOMETRICS Use of economic theory and statistical

tools to analyze economic relations


25/28


ECONOMETRICS Single equation

Yt = 0 + 1Pyt + 2 Pjt + 3 Yt + 4Ad + t

where Y = sales ofY

Py = price of Y

Pj = prices of related goods

Y= disposable income

Ad = advertising expenditure

= error terms


26/28


ECONOMETRICS

System of equations

Supply: Qst = 0 + 1 Pt + 2 Fct + 3 Tect + t

Demand: Qdt = 0 + 1 Pt + 2 Pst + 3 Yt + t

Identity: Qst = Qdt

Where Qs = quantity supplied

Qd = quantity demanded

P = price of the commodity

Fc = cost

Tec = technology variable

Ps = prices of related commodities

Y = income

= error terms


27/28


Forec

ast Acc

uracy

Techniques

Root Mean Square Errors (RMSE)

Theil Inequality Coefficient (U)

t1 t2 t3 (today)

Estimation Period Ex-post forecast Ex-ente forecast


28/28


TeriTeri K ihK ihThank You

Documents

Me 4 Business Forecasting