Leading indicators
Compound indexes Diffuse indexesCollection of opinions and reviews of goals
Econometric methods
The simplest model
Time series analysis
Разделы презентаций
- Разное
- Английский язык
- Астрономия
- Алгебра
- Биология
- География
- Геометрия
- Детские презентации
- Информатика
- История
- Литература
- Математика
- Медицина
- Менеджмент
- Музыка
- МХК
- Немецкий язык
- ОБЖ
- Обществознание
- Окружающий мир
- Педагогика
- Русский язык
- Технология
- Физика
- Философия
- Химия
- Шаблоны, картинки для презентаций
- Экология
- Экономика
- Юриспруденция
Forecasting
Содержание
- 1. Forecasting
- 2. Successful operations of the companyEffective planningAccurate forecasting
- 3. Forecasting techniques:Mechanical extrapolationBarometric methods
- 4. Mechanical extrapolationForecasting techniquesOriginally extrapolation methods are mechanicaland not closely linked to economic theory
- 5. However, they are widely used by professional
- 6. However, they are widely used by professional
- 7. Mechanical extrapolationThe simplest models:All future values of
- 8. Mechanical extrapolationForecasting techniques:The simplest models: Unchanging modelThe
- 9. The vast majority of all economic, political
- 10. Time series analysis:Time series consist of values
- 11. Why fluctuation is typical for the time
- 12. 1) Trend (Т)Is a long-term increase or
- 13. 3) Cyclic changes (С)Cover periods of several
- 14. Seasonal changes and the method of moving
- 15. Regroup presented data:Time series analysis:Mechanical extrapolationForecasting techniques:Using
- 16. Step 1: Moving average over the four
- 17. Step 5: Make normatization: the average value
- 18. Q1: 316 (для 1989) * 0,99 =
- 19. Designing of trendAs a forecasting method assumes
- 20. ] Y – the observed value of
- 21. Trend estimates are more reliable if they
- 22. Y = 284,382 + 1,632 tYearcentralized moving Average YPeriodtotal
- 23. Скачать презентанцию
Successful operations of the companyEffective planningAccurate forecasting
Слайды и текст этой презентации
Слайд 3Forecasting techniques:
Mechanical extrapolation
Barometric methods
Collection of opinions and reviews of goals
Econometric methods
Слайд 4Mechanical extrapolation
Forecasting techniques
Originally extrapolation methods are mechanical
and not closely linked
to economic theory
Слайд 5However, they are widely used by professional economists who make
forecasting
Because of they are easy to apply and satisfy reasonably
the requirements of the management
Слайд 6However, they are widely used by professional economists who make
forecasting
Because of they are easy to apply and satisfy reasonably
the requirements of the management
Слайд 7Mechanical extrapolation
The simplest models:
All future values of the studied variable
in some way are a function of its present or
recent status^
Forecasting techniques:
] Y – the experimental value of the analyzed variable
Y – the predicted value of the analyzed variable
t – index to distinguish periods
^
Слайд 8Mechanical extrapolation
Forecasting techniques:
The simplest models:
Unchanging model
The predicted value of
the variable for the next period will be equal to
its value in the present periodY t+1 = Y t
^
Proportionaly - changing model
The value of a variable changes from current to next period will be proportional to the value of a variable changes from the previous period to the current period
Y t+1 = Y t + k ∆ Y t
^
Evaluation of k based on retrospective information.
K = 1 is a uniformly changing the model
Слайд 9The vast majority of all economic, political and social decisions
are made based on considered the simplest models
For most short-term
predictions the simplest models are the most easy ways of forecasting, since they are easy to use and requires minimal information for calculatingMechanical extrapolation
Forecasting techniques:
The simplest models:
Слайд 10Time series analysis:
Time series consist of values corresponding to certain
points or periods
Ordered in time indicators: sales, production volume, prices….
Mechanical
extrapolationForecasting techniques:
Слайд 11Why fluctuation is typical for the time series?
Usually there are
four sources of variation in economic time series, :
Trend (T)
Seasonal
changes (S)Cyclic changes (C)
Irregular forces (I)
Time series analysis:
Mechanical extrapolation
Forecasting techniques:
Слайд 121) Trend (Т)
Is a long-term increase or decrease of series
Seasonal
changes (S)
Due to weather conditions and habits appear almost at
the same time of a year (for example, New Year, Easter and other holidays, during which various purchases are made)Time series analysis:
Mechanical extrapolation
Forecasting techniques:
Слайд 133) Cyclic changes (С)
Cover periods of several years, reflect the
level of economic boom or recession
Irregular forces (I)
Strikes, war. Inconsistent
in their effect on individual series, but, nevertheless, be taken into accountTime series analysis:
Mechanical extrapolation
Forecasting techniques:
Слайд 14Seasonal changes and the method of moving average
Moving average is
calculated by summing the values for each period for some
selected period of time and then dividing the resulting amount by the number of periodsSeasonal changes can be taken into account in the forecast using the seasonal index, which can be calculated by the method of moving average
Time series analysis:
Mechanical extrapolation
Forecasting techniques:
Слайд 15Regroup presented data:
Time series analysis:
Mechanical extrapolation
Forecasting techniques:
Using the data presented
in the table, calculate the moving average and define seasonal
indexVolume of sales
quarter
total
Слайд 16Step 1: Moving average over the four periods is calculated
using a consistent set of sales for the 4 quarters
Each
subsequent calculation does not include the first quarter and adds the next quarterStep 2: Centralized moving average for each quarter is calculated as the average of each consecutive pair of 4-period moving averages
Step 3: Seasonal indexes are calculated by dividing the actual volume of sales for the corresponding quarter by centralized moving average for the same period
Step 4: arrange seasonal indexes quarterly
quarter
Year
Sales
4-period
moving
average
centralized
moving
average
Seasonal
index
Слайд 17Step 5: Make normatization: the average value of the four
average seasonal indexes must be equal to 1
Average value is
1.01: adjust seasonal indices up or down, revealing trends and maintaining the average value of the four indexes equal to 10,99 1,38 0,98 0,65
Year
Average Seasonal index
total
Data to calculate Seasonal indexes
Слайд 18Q1: 316 (для 1989) * 0,99 = 312,84 $
Q2: 322
(для 1989) * 1,38 = 444,36 $
Q3: 307 (для 1988)
* 0,98 = 300, 86 $Q4: 311 (для 1988) * 0,65 = 202,15 $
Average Seasonal index
0,99 1,38 0,98 0,65
4-period
moving
average
centralized
moving
average
Seasonal
index
Sales
Step 6: preparation of the forecast for each quarter of the coming year: multiply the last centered moving average for the quarter by its seasonal index
quarter
Year
Слайд 19Designing of trend
As a forecasting method assumes that started change
in the variable will continue in the future
The most widely
used method of trend detection is regression analysis, namely the method of least squaresThe method consists of the selection of a regression line according to the observations so that the squares of their deviations from the regression line were minimal
Time series analysis:
Mechanical extrapolation
Forecasting techniques:
Слайд 20] Y – the observed value of the analyzed variable
Y – the predicted value of the analyzed variable
^
The sum
of the squared deviations between Y and Y is written as:^
^
Regression line is presented by: Y = a + bt, where a and b - parameters of evaluation, t – number of period
^
Taking partial derivatives of D function relative to a and b and equate them with zero, we obtain:
To find the values of the parameters a and b, it is necessary to solve the system of equations
![] Y – the observed value of the analyzed variable Y – the predicted value of the](/img/thumbs/69471c1d3a71bfca43323df5ad1fa329-800x.jpg "Forecasting ] Y – the observed value of the analyzed variable Y")
Слайд 21Trend estimates are more reliable if they are based on
data released from seasonal effects
Seasonal effects are smoothed by a
moving average
