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Chapter 4
INFLATION AND FORECASTING
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Chapter 4
INFLATION AND FORECASTING
4.0 INTRODUCTION TO INFLATION
In the flat world, macro economic variables such as Inflation
serve as important guidelines/index not only to government and
private institutions but also for a common man. With the increasing
literacy rate, the average common man in India is considering
Inflation as one of the simple indices for making long term and short
term investment and saving decisions. There is hardly any country in
the world that is not afflicted by Inflation. It is on account of this the
phenomenon of inflation is widely attracting the attention of not only
economists but also techno-entrepreneurs.
Indeed there is a plethora of definitions on Inflation. The
layman, however,
understands the phenomenon of sizeable and
rapid increase in general price level of products and services in a
particular economic domain as Inflation. According to Irwing fisher
“Inflation occurs when the supply of money actively bidding goods
and services increases faster than the available supply of goods.
Inflation leads to Inflationary spiral. When prices rise, workers
demand higher wages. Higher wages leads to higher costs. Higher
costs lead to higher prices. Higher prices again lead to higher wages,
higher costs and so on. Thus prices, wages, costs chase each other
leading to hyperinflation.
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Recession is just opposite of inflation and is a state of declining
prices due to reduction of total expenditure of the community and
this occurs when demand is less than supply of goods and services.
According to quantity of money theorists, excessive issue of money
causes inflation. According to demand and supply theorists, it is
caused by total demand exceeding the total supply of goods and
services.
4.1 CAUSES OF INFLATION
The causes of inflation may be grouped under the following
headings:
Increase in demand that may be due to:
(i)
Increase in money supply
(ii)
Increase in disposable incomes
(iii) Increase in public aggregate spending on consumptions
(iv) Excessive speculations and tendency to hoarding
and
profiteering on the part of producers and traders
(v)
Increase in foreign demands and hence exports
(vi) Increase in salaries, wages or dearness allowance
(vii) Increase in population
No corresponding increase in the output of goods and services which
may be due to :
(i)
Deficiency of capital equipment
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(ii)
Scarcity of other complementary factors of production e.g.
skilled
manpower,
raw
materials
or
lack
of
dynamic
entrepreneurs.
(iii) Increase in exports for earning the required foreign exchange
(iv) Decrease in imports due to wars or sanctions on imports
because of an adverse balance of payments.
(v)
Drought, famine or any other natural calamity resulting in
decrease in agricultural production
(vi) Prolonged
industrial
unrest
resulting
in
reduction
of
industrial production
4.2 THEORIES OF INFLATION
Inflation can be caused by a variety of different factors, and for
each factor there are different theoretical explanations. Here we look
at some of these theories about causes of inflation.
Demand-Pull Inflation: Inflation caused by increase in aggregate
demand due to increased private and government spending etc.
Demand-pull inflation happens where there is too much money
chasing too few goods. Excessive growth in demand literally pulls
prices up.
Cost-Push Inflation: If costs rise too fast, companies will need to put
prices up to maintain their margins. The reasons for rise in costs
may be increase in wages, salaries and cost of machines and
materials.
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Mixed Inflation: According to some theorists, inflation is due to both
demand-pull and cost-push. Inflation occurs both due to excess
demand and rising costs. One leads to the other and is intermingled.
Inflation is said to be suppressed inflation, when the
government does not allow the prices to rise with some price
controls. If the government does not attempt to control prices and let
them free to rise, it is called “Open Inflation”. Suppressed Inflation or
the latent inflation involves price controls and rationing. If the
administration is not efficient, price controls lead to certain evils
such as profiteering, hoarding, black-marketing, corruption etc. The
postponed demand due to rationing will burst up when price controls
are lifted, and it creates several other problems. Therefore open
inflation is relatively better than the suppressed inflation.
On the basis of the rate of increase of prices with respect to
time, inflation will be called with different names – creeping inflation,
walking inflation, running inflation, and galloping inflation. When
prices rise very slowly and gradually, it is called Creeping Inflation.
Some economists support this kind of mild dose of inflation and may
be good because it stimulates production.
When creeping inflation gains more and more pace, it is called
walking inflation, then running inflation and then galloping or higher
inflation etc. Stagnation is a different situation where inflation and
recession exists simultaneously. There will be inflation in some
sectors and recession in certain other sectors. Prices may be rising,
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but there is no corresponding increase in the output. Such a
situation is referred as “Stagflation” (Stagnation + Inflation).
A slow and moderate rise in prices is necessary to fight a
depression and this anti depression measures for slow rise in prices
is called reflation. There are different arguments on whether inflation
promotes economic growth or retards it. What ever is the result, it is
required to run along with inflation.
4.3 NATURE OF INFLATION IN A DEVELOPING ECONOMY
Developing countries, in their bid to raise the standards of
living of their people through development plans, have often found
themselves in the grip of inflation. But the nature of inflation in
under developed but developing economies is quite different from
that found in advanced or developed countries. In advanced
countries true inflation starts after the level of full employment is
attained.
But
in
under-developed
countries
like
India
huge
unemployment and inflation exist side by side. In other words, in
evidence long before the level of full-employment is reached. This is
so because the nature of unemployment in under developed
countries during times of depression.
In order to get the economy out of depression, governments in
advanced countries take various steps to increase the level of
investments. The additional investment expenditure leads to an
increase in effective demand depending upon the magnitude of the
multiplier. But this increase in investment and effective demand does
not generate serious inflationary pressures but because of the elastic
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nature of the supply curve of output. Instead increase in investment
and effective demand helps a great deal in removing depression and
unemployment, which are caused by the lack of effective demand.
This is the case of developed economies.
4.4 ANTI INFLATIONARY MEASURES
There is no one unique solution to contain inflation. Antiinflationary measures to control inflation can be classified as under:
(i) Monetary measures: The central bank of the country generally
adopts these measures that include raising the bank rates, sale of
government securities in the open market, varying reserve ratios etc.
The mechanics of monetary policy is as follows:
Raising the bank rates:
The raise in the bank rate will cause
the rise in other market rates of interest. The raise in bank rates
tends to discourage the borrowings by businesspersons and
consumers from banks. The raise in bank rates is in a way a red
signal to the businessman that bad times are ahead. This dampens
their possible plans for additional spending on investment.
Also, an increase in the interest rates will make saving more
lucrative than before. As a result there will be a tendency that people
will save more than before. This will reduce the consumer spending.
On the whole there will be reduction in the amount of aggregate
spending and this results in reduction in the intensity of inflation
pressures.
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Sale of government securities in the open market: Another
important measure to contain the inflation is to resort to the sale of
government securities by the central bank to the public or to the
banks themselves. This results in reduction of the amount of cash
with the banks and this will force them to reduce the supply of credit.
Mostly this measure is intended to reduce the credit-creating capacity
of commercial banks and there by containing the inflation.
Varying Reserve Ratio: Varying reserve ratios may induce the
same effect intended by the sale of government securities. It is
mandatory for the member banks to keep certain minimum amount of
cash with central bank in proportion to the volume of their activities.
The central bank has the power to change this reserve ratio from time
to time. An increase in reserve ratio absorbs the surplus funds of the
banks and thus reduces the credit creating capacity of commercial
banks. This helps in the reduction of the inflationary pressures.
(ii) Fiscal measures: The two important components of fiscal policy
are
government
revenues
and
government
expenditure.
The
government’s fiscal policy can contribute to the control of inflation
either by reducing private spending by increasing the taxes on private
sector or by decreasing the government expenditure, or combining
both the elements. The fiscal measures consists in
(a) Reduction of government expenditure
(b) The rates of the existing taxes should be raised or imposition of
new taxes on products and services, to take away the excess
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buying power from the hands of the consumers and to reduce the
magnitude of the inflationary gaps.
(c) The encouraging the savings and/or introducing compulsory
saving schemes
(d) Public debt management so as to reduce the money supply
(e) Over valuing domestic currency in terms of foreign currencies
(iii) Physical or Non-monetary measures: Some times in addition to
the monetary and fiscal measures, there will also be a necessity to
resort to measures of non-monetary nature. But it should be noted
that such measures are less pragmatic than the monetary and fiscal
measures. Some of the non-monetary measures are:
(a) Increased Production within the country; or increasing imports
and decreasing exports in order to increase the available supply of
goods.
(b) Controlling money wages to keep down costs
(c) Price control and rationing
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4.5 WHY INFLATION ANALYSIS AND FORECASTING?
Typically Inflation is supposed to cause several positive or
negative effects that might influence personal and social welfare. Now
a days, the central banks in the several countries are concerned to
ensure guaranteeing stable prices. For this during last decade
central banks are looking at adopting “Inflation Targeting Regime”. In
this an inflation target or a target range for the inflation rate for an
identified period of time is announced. This inflation target regime
characterizes
framework
the
the
monetary
monetary
policy.
authority
In
with
an
inflation-targeting
strong
commitment
concentrates in containing the inflation within the target range.
Though
these
central
banks
were
quite
successful
in
containing inflation, it was not eliminated completely. In this context,
along with governments, private institutions also are interested in
future inflation to foresee expected changes in the aggregate price
level in order to prevent negative effects of unexpected inflation.
Banks anticipate future inflation that is important in credit
contracts. Labour unions attempts to bring in future inflation during
the wage negotiations so that suffering from unexpected losses in
real wages etc. can be minimized and/or prevented. Last but not
least with the increasing literacy rate and wide exposure to print and
electronic media, a common man tries to anticipate the inflation in
the immediate future to make his monthly earnings, expenditure and
savings style free from the effects of inflation fluctuations.
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From the above discussion, we may conclude that not only
governmental and private institutions but also a literate common
man- directly or indirectly- is in the need of analysing inflation and
forecasting inflation.
4.6 CONVENTIONAL FORECASTING METHODS
There are two basic concepts- econometric and expectationswith respect to forecasting of inflation. In the first one -econometric
approach - forecasts are generated on the basis of actual and
historical empirical data using econometric techniques. According to
this approach, it is believed that some theoretical model explains the
factors that determine inflation. Whenever we have the necessary
data and correct model at hand, it provides a platform to generate
some reasonable forecast.
The second one - expectations approach – is not interested in
what theoretical model is used to predict future inflation. It simply
considers a certain subgroup of people, with an expertise in trends of
inflation. It is believed that these people know enough about the true
factors that determine price level changes, and such subgroup of
people can estimate inflation well on average. Thus it is sufficient to
consider the inflation expectations of the identified subgroup of
people, without focusing on the methods that are used for the
individual forecasts.
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4.7 CLASSIFICATION OF FORECASTING TECHNIQUES
Forecasting
Techniques
Qualitative
Models
Quantitative
Models
Delphi
Method
Time Series
Models
Causal
Models
Sales Force
Method
Moving
averages
Consumer
Panel survey
Exponential
Smoothing
Simple
Regression
Multiple
Regression
Trend
Projection
Box-Jenkins
(ARIMA)
Method
Fig. 4.1: Classification of forecasting techniques
Forecasting
can
be
obtained
using
a
variety
of
techniques/models. These techniques can be discussed under the
following two categories.
1. Qualitative Models
2. Quantitative Models
4.7.1 Qualitative Models
Some of the qualitative methods of forecasting are as follows:
Delphi Method: This method employs a panel of experts in different
locations who independently fill out a series of questionnaires. The
experts are usually not known to each other and their interaction
takes place through a coordinator. However, the results from each
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questionnaire are provided with the next one, so each expert then
can evaluate this group information in adjusting his or her responses
next time. The goal is to reach a relatively narrow spread of
conclusions from most of the experts. The decision makers then
assess this input from the panel of experts to develop the forecast.
Sales Force Composite: This method is often used for sales
forecasting when a company employs a sales force to help generate
sales. It is a bottom-up approach whereby each salesperson provides
an estimate of what sales will be in his or her region. These estimates
are sent up through the corporate chain of command, with
managerial review at each level, to be aggregated into a corporate
sales forecast.
Consumer Panel Survey: This method goes even further than the
preceding
one
in
adopting
a
grass-roots
approach
to
sales
forecasting. It involves surveying customers and potential customers
regarding their future purchasing plans. This input is particularly
helpful for designing new products and then developing the initial
forecasts of their sales.
4.7.2 Quantitative Models
Various Quantitative models used for forecasting come under
two categories.
(a) Time Series Models
Time Series models use time-based data. A time series is a
collection of readings belonging to different time periods, usually
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equally spaced, which may be months, weeks or years, of some
economic variable. Different techniques come under this model are
(i)
Moving Average Method: Rather than using very old data that
may no longer relevant, this method averages the data for only the
last n periods as the forecast for the next period, i.e.
 t -n

Ft    X i  n
 i  t -1 
Where Ft = the forecasted inflation for the time period t
X i = the observed value ( real value) for the i th period
n = number of periods for calculating the value
(ii)
Weighted Average Method: Instead of considering the most
recent moving average only to forecast, weightages will be given to
few past moving averages.
(iii) Exponential Smoothing Method:
This is another time series forecasting technique where the
forecast for the next period is calculated as weighted averages of all
the previous values. It is based on the premise that the most recent
value is the most important for predicting the future value. Also, it
presumes that values prior to the current value are also relevant but
in declining importance as we go back in time. The weights decline
exponentially as we consider the older values.
This method uses the formula
Ft  μX t 1  (1  μ)Ft -1
where  (0< <1) is called the smoothing coefficient. Thus the
forecast is just a weighted sum of the last observation x t-1 and
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preceding forecast Ft-1 for the period just ended. Because of this
recursive relationship between Ft and Ft-1, alternatively Ft can be
expressed as
Ft  μX t 1  μ(1  μ)X t -2  μ(1 - μ) 2 X t 3  ...
In this form, it becomes evident that exponential
smoothing gives the most weight to x t-1 and decreasing weights to
earlier observations. Further more, the first form reveals that the
forecast is simple to calculate because the data prior to period (t-1)
need not be retained; all that is required is x t-1 and the previous
forecast Ft-1.
(iv) Trend projections: In this method a trend line is fitted to give
time series data and then projections are made into future using this
line. For obtaining the trend line, the given historical data is first
plotted on the graph, representing time scale on the X-axis. Then a
line is drawn through these points in such a way that (a) the sum of
deviations above the line is equal to the sum of deviations below the
line so that the sum of deviations is equal to zero, and (b) the sum of
squares of these deviations is minimum. In essence, the trend line is
drawn on the basis of the ‘Principle of least Squares’. Such a line,
like any other straight line is represented by the equation:
Yt  a  bX
Where
Yt = the trend line
A = the Y-axis intercept
B= slope of the trend line
X = independent variable, the time
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Parameters ‘a’ and ‘b’ of the trend line drawn on the principle
of least squares are obtained using the following pair of normal
equations:
 Y  na  b X
 XY  a  X  b X
b
2
 XY - nXY
 X - nX
2
2
a  Y - bX
Here, Y = summation of the values of the dependent variable
X = summation of the values of the independent variable
n = number of data points
X 
X
n
;
Y 
Y
n
(b) Causal Models
Causal forecasting models consider situations where the
variable to be forecasted, called the dependent variable, is related to
some
variable(s)
known
as
independent
variable(s).
Causal
forecasting obtains a forecast of the quantity of interest (the
dependent variable) by relating it directly to one or more other
quantities (the independent variables) that drive the quantity of
interest. The following Table 4.1 shows some examples of the kinds
of situations where causal forecasting some times is used.
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Type of
forecasting
Possible
dependent
variable
No. of sales of an
item
Demand for
Spare parts
Possible
independent
variable
Amount spent
on advertising
Usage of that
vehicle
Sales
volume
Spare parts
of a vehicle
Economic
Inflation
Time period
Trends
Table 4.1: Examples of the kind of situations where causal
forecasting models can be used
Simple Regression Analysis:
Regression analysis is one Causal forecasting model that
establishes a relationship between dependent and independent
variables. Past data establishes relationship between the two
variables.
Simple regression analysis is employed where there is one
independent or explanatory variable. Here an estimate of the
dependent variable is made corresponding to a given value of the
independent variable, by placing the relationship between the two
variables in the form of a regression line. The regression line is
obtained on the basis of the given data, involving paired observations
of the X and Y variables. For this purpose, the given paired data are
plotted on a graph by means of points. The line is obtained on the
basis of the principle of least squares. The regression line
represented by the following equation, which is obviously called the
regression equation:
Y = a +bX
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Where,
Y = the predicted value of the dependent variable
a = y-intercept of the regression line
b= slope of the regression line
X = the independent variable
As in the case of a trend line, the values of ‘a’ and ‘b’ are
obtained as follows:
b
 XY - nXY
 X - nX
2
2
a  Y - bX
X 
X
Y 
Y
n
and
n
Once parameters ‘a’ and ‘b’ of the line are obtained, predictions
of the dependent variable can be made for the given values of the
independent variable, X, in the equation. Parameters ‘b’ is called the
regression coefficient.
Multiple Regression Analysis:
The multiple regression analysis is an extension of the simple
regression analysis and it allows building a model with more than one
independent variable. The issues involved in multiple regression
analysis are many and mathematics involved is rigorous, especially
when more than two variables are involved. We state here the case,
which has two independent variables.
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A two variable regression equation is:
Y  a  b1 X 1  b 2 X 2
The parameters a, b1 and b 2 are obtained by solving the following
three equations simultaneously:
 Y  na  b  X
1
 X Y  a X
1
X
2
1
1
 b2  X2
 b1  X 12  b 2  X 1 X 2
Y  a  X 2  b1  X 1 X 2  b 2  X 22
BOX-JENKINS METHOD:
G.E.P. Box and G.M. Jenkins developed this method. This
method is also called as Autoregressive Integrated Moving Averages
(ARIMA) method. The historical data is used to test the model. This
method requires huge amount of data (a minimum of 50time periods)
related to the past time periods.
This method is iterative in nature. To use this model,
autocorrelations and partial correlations are to be computed and
their patterns are to be examined. An autocorrelation indicates the
correlation between time series values separated by a fixed number
of time periods. The partial autocorrelation is a
conditional
autocorrelation between the original time series and the same time
series moved forward a fixed number of time periods, keeping the
effect of the other lagged times fixed. From the autocorrelations and
partial correlations, one or more models (functional form) can be
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identified and testing the validity of the models can identify the best
one.
4.8 FORECAST ERRORS
Forecast error is the numeric difference of forecasted quantity
and actual quantity. A forecast method yielding large errors is less
desirable than one yielding smaller errors. There are different types
of errors by means of which total forecast error is qualified.
(i) Mean Absolute Deviation (MAD):
MAD = (sum of the absolute value of forecast error) / (number of
periods)
 n

=   Forecast errori  n
 i 1

 n

=   (Forecast  Actual  n
 i 1

where ‘n’ is the number of periods. MAD expresses the
magnitude of error but not the direction of error. This measure of
absolute values is called absolute deviation.
(ii) BIAS
This gives the average of forecast error with regard to direction
and shows tendency consistently to over or under forecast.
BIAS = (Sum of forecast errors for all periods) / Number of
periods
 n

=   Forecast errori  n
 i 1

 n

=   (Forecast  Actual  n
 i 1

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BIAS indicates the directional tendency of forecast errors, over
estimation results in a +ve BIAS value, under estimation results in –
ve BIAS value.
(iii) Tracking Signal (TS):
TS 
BIAS
MAD
Generally lower value of TS is desirable.
Summary: This chapter concentrates on the fundamentals of inflation
and forecasting. Inflation, a macroeconomic variable, represents a
situation in which too much money chasing too few commodities; in
other words rise in prices of commodities. This chapter deals with
causes and control of inflation, theories of inflation, nature of inflation
in a developing economy, and the need for forecasting of inflation.
Forecasting is a technique of estimating the value of an
identified parameter for future time periods. Various forecasting
techniques-qualitative and quantitative; and the forecast errors are
also discussed in this chapter.
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Forecasting
A practicing manager in the dynamic – either economic or
business or production – environment, in which actual demand is not
known with certainty, needs a basis for effective planning and
scheduling of the activities. These managers are required to make
decisions for implementing in future time period. Not only the
practicing managers but also the strategy makers need a rational
basis for formulating the right strategies for future events. But it is a
well known fact that it is difficult to predict the outcome of a future
event with certainty. The managers or policy makers strive to reduce
the amount of uncertainty and arrive at better estimates what are
expected to happen in future. This is the central theme of forecasting.
Forecasting is a process that helps in making effective
decisions by estimating the future event with reduced amount of
uncertainty. Forecasting is a tool that is widely used in several areas
of operations such as marketing, inventory, production planning,
economics, financial planning etc.