|
|
|
|
|
|
|||||||||||
|
|
|
|
|||||||||||||
|
|
|
||||||||||||||
|
|
|||||||||||||||
|
|
|
|
|
|
|
|
|||||||||
|
|
|
|
|
||||||||||||
|
|
|
|
|
|
|
|
|||||||||
|
"There are two kinds of forecasters: Those who don't know, and those who don't know they don't know'' John Kenneth Galbraith (Wall Street Journal, January 22, 1993) “There are known
knowns; there are things that we know that we know. We also know there are
known unknowns; that is to say we know there are some things we do not know. But there are also
unknown unknowns, the ones we don't know we don't know” Donald
Rumsfeld Forecasting Science and Theory of Forecasting Forecasting
is a kind of decision making for defining the most expected scenario from
alternatives of future prospects. Although it is not explicitly mentioned,
many of our decisions are forecasting the future of a system, organization,
human behavior etc. Forecasting is one of the most used functions of human
recognition which works unconsciously. We predict arrival time, departure
time, cost of daily consumables and behavior of a friend and so on. However,
there are some popular forecasts which usually have particular media coverage
such as weather forecasts, economic forecasts and the story of fortune
tellers based on a magical crystal ball! The
reputation of forecasting is deteriorated with improper practices and poor
knowledge about the theory of forecasting. There are several common
misconceptions about forecasting and the method of forecasting process.
Therefore, it is strongly needed to illustrate what forecasting is and how it
works. Exordium: Philosophy of Forecasting Forecasting
is not a new topic, and it is frequently discussed with the physical
phenomena. One of the critical questions about the forecasting is
predictability debate. Before approaching to forecast, people need to clarify
whether it is achievable. In the similar circumstance, the difference between
the nature (physics) and human make a strong impact on our perception of
forecasting. Human sciences e.g. economics are subject to the uncertainty of
mankind which requires an extraordinary effort for forecasting. Physical
phenomena are usually based on robust, clear and repetitive rules. One can
easily predict destiny of a ball if it is released from a table: It drops to
floor. We are unconsciously aware of several physical rules including
gravity. However, when it comes to a complex problem such weather
forecasting, there is a huge number of forces and systems working together.
In such cases, we are unable to make a precise and error-free forecast. On
the other hand, the movements of earth, moon, sun and most part of astronomic
motion can be predicted precisely even in seconds of time. Economic
phenomena as the most popular field for forecasters has somewhat different
dynamics. In the core of the problem, we, human, exist, and our nature is
quite complicated. The models of our behavior can change every day. We may
dislike something today which we liked it yesterday. Therefore, most of the
econometric models need to be re-estimated and re-thought frequently. No
model can serve for several years. The use
of forecasts and the state shift are some other critical topics. Forecasts
are usually required for developing broader policies or strategies in micro
level. Based on the forecast, decision makers define a policy to achieve a
desired outcome. Therefore, policies are employed for changing the direction
of a phenomena from the expected (non-intervention) future state to desired
future state. The major function of policy here is shifting between states by
using policy/strategy instruments. As a
result of policy actions (intervention), the nature of phenomena changes, and
now it is not same as estimated at the time of forecasting. If policies are
somewhat successful, forecasted future state will not become a reality. If
someone criticizes the failure of the forecast, that would be ignorance of
the function of a forecast. Unless a forecast is kept exclusive for a person or
a small group of people, future state will always change, and it will be
replaced with a new one. The theory of information asymmetry emphasizes that
if an information is hold by exclusive users, then that will create an
advantage for them. Based on this, we can roughly say that a publicly known
forecast probably fails since decision makers replace their position and
direction. In addition to that, if most of the decision makers are familiar
with the state-of-art forecasting methods, they will probably conclude
similar predictions which in turn causes similar strategies. Finally,
forecasts will fail even in exclusive terms since the method is common and
symmetrical. That is the current situation in the financial markets. Although
forecasts are exclusive, methods are well known, and even many trader
companies employ quants with PhDs from Harvard or MIT to find a less known
way of forecasting. When
human action is in the problem, there is no straight and accurate way of
prediction whether a forecaster runs complex mathematical functions and
simulations with an extraordinary programming talent. Assumptions behind
the classical forecasting methods The
major assumptions behind forecasts are the repetitive nature of history and
the stationary nature of decision makers. The recursive history perspective
is very old topic, and it is one of the main themes of Muqaddimah
(Ibn Khaldun) which is the first written publication of the scientific method
to social sciences and the philosophy of history. In modern times, Peter Turchin extended and mathematically presented the theory,
and even he developed models of historical dynamics. Cliodynamics
is branch of history dealing with the mathematical modeling of historical
fluctuations and metamorphosis. According to the theory, the most part of the
history is a kind of cyclic movement while names, titles or instruments are
changed. For example, civilization has a cyclic behavior and every
civilization movements and developed empires have a life span from birth to
mature and death. The
current business cycle theory also supports the theory of recursive history
and both macro and micro level economic systems are subject to cycles of
upturns and downturns i.e. recovery and recession. However, the vital point
of the theory is the size and schedule of cycles. Based on the recursive
history assumption, every forecaster deals with finding a proper method to
define particulars of coming cycles. For longer periods, forecasters study
long term cycles and for shorter terms they look for short term cycles. Another
critical assumption is the stationarity of decision makers which refers to
the identical reactions to identical impacts (Reader may confuse with the
rationality assumption which is a quite different matter and will be
discussed later). The recursive history assumption somewhat includes the
stationarity of decision makers. History repeats itself since decision makers
behave identically in addition to the identical circumstances. Under these
assumptions, we recognize the historical pattern and replicate it to find
future direction. From
the economic perspective, the utilization of decision making processes is a
common issue and it is known as rationality of decision maker. It is very
similar to the stationarity assumption with a slight difference. In
stationarity assumption, we assume that all economic agents behave
identically even when these selections and decisions are inadequate. If some
decision makers make faulty preferences, we assume that this faulty decision
will be repeated as well. However, rationality assumption is about the
finding the optimum decisions among a number of options in every cases.
Econometric models are usually based on rationality assumption which means
the economic agents of the intended marketplace rationally defined and
defines optimum economic preferences. Therefore, the proposed model is
estimated under the rational circumstances and the future state is expected
to be rationally managed. Based on these assumptions (many assumptions mean
it is probably impractical), history will repeat itself in the rational
people’s world. However, rationality assumption is strongly criticized in the
last few decades and the irrationality concept (also bounded rationality) is
a growing topic in the field. Why micro economic forecasts are usually inferior in
business practice? It is
related with the publicity of forecasts. As it is discussed in Exordium, the asymmetric information
refers to the possibility of arbitration in case of private market
intelligence. If an economic forecast is publicly available, every decision
makers consider this evidence and revise the direction of their investment.
Although macroeconomic and long term forecasts are able to predict cycles
roughly, micro-economic and short-term fluctuations are affected by the short
term position changes of agents. Once a forecast is publicly available, every
agent moves to a new position, and then the decision space of desired future
will dramatically change. From that time, the particulars of marketplace is
not same as it is assumed/estimated in the modelling stage. Although
the model delivers proper predictions, it will never be accurate since it is
common information. Many international organizations (e.g. World Bank, IMF)
publish macro-economic forecasts while it is very difficult to find publicly
available forecasts for industrial markets. Usually we pay charges for them
and we wish that a few people reach to these predictions. Otherwise they are
worthless and “inaccurate” as a result! Methods of Forecasting Forecasting
methods have two major divisions: Quantitative methods and qualitative
methods (i.e. objective vs. subjective). Econometric modeling, time series
analysis, neural networks and other methods of mathematical solutions are
quantitative methods, and they are very useful when there is a repetitive
nature. For example, seasonal time series methods are quite accurate for
prediction of ice-cream sales or the volume of harvest. However, they are
strictly limited to the historical pattern, and it is impossible to embed
pure subjective factors, expectations or political aspects. Therefore,
qualitative methods are utilized distinctly or in addition to the
quantitative methods. Judgmental forecasting is a typical subjective
forecasting method, and it is frequently used for exposing herd behavior and
other psychological trends. Expert guided adjustment of quantitative methods
is an alternative solution to gain advantages of both. Judgmental forecasts
are also limited to biases and heuristics of decision makers. For this
reason, a special care is needed to handle and manage judgmental forecasts. The use
of computer intelligence, neural network models and fuzzy sets for
uncertainty problem is a growing section of forecasting science, and these
methods contributes to the improvement of processes, randomness and
complexity (i.e. chaos systems). The existing literature has several good
applications of computer intelligence which are superior to the conventional
time series analysis and the orthodoxy of econometrics. Fundamentals of Forecasting In a
brief list, we may figure out some fundamental needs and common failures of
forecasting studies: (1) Data control and preparation: Stationarity control | Data transformation if
needed (2) Sampling (Partitioning): Defining the in-sample period for training
(estimation) and the post-sample period for real forecasting test (i.e.
testing period) (3) Benchmark Selection: Conventional methods and other potential competitors of the proposed
forecasting method should be selected as benchmark. Since this step is
subjective, one may intend to select inferior methods to highlight the
proposed one. A computer intelligence method should not only tested against
similar one, but it should also be compared with the conventional time series
methods. (4) Accuracy Metric Selection: There are several accuracy metrics used frequently
while they are quite biased (e.g. MAPE, RMSE). For example, Mean Absolute
Percentage Error (MAPE) is one of the most biased error metrics. Assume that
we have two actual value, 1 and 5, and then our model generates same
predictions for them, 3 and 3, which means same absolute error for both, 2
and 2. MAPE for these forecasts are |1-3|/1=2.0 (200%) and |5-3|/5=0.4 (40%).
Although absolute errors are same, MAPE metrics have a huge difference. If
forecasting model systematically predict less (undervalue), then we will find
it superior. Please be aware of illusion of accuracy gain. Mean Absolute
Scaled Error (MASE) is a relatively better choice for accuracy control. (5) Residual Control: Residuals should be checked against whether a remaining pattern
exists. A figure showing the residual series is usually enough for
illustrating the white noise control. White noise testing procedures (e.g.
serial correlation test) may also clarify whether residuals are really
irregular oscillations. Ethics of
Forecaster Forecasting methods and procedures are usually
subject to expert consultation, and there is a strong potential of unethical
use or presentation. Subjective selections and arbitrary preferences may help
to validate and rationalize the scholar’s work (self-serving bias), then the
outcome would not be useful and practical. By this way, forecasting study
turns to be an entertainment rather than a professional effort. Forecasters are strongly
encouraged to criticize their work and profession if they concern about the
practical meaning of their efforts. |
|
||||||||||||||
|
|
|||||||||||||||
|
|
|||||||||||||||
|
|
|||||||||||||||
|
|
|||||||||||||||
|
|
|||||||||||||||
|
|
|||||||||||||||
|
|
|||||||||||||||
|
|
|||||||||||||||
|
|
|||||||||||||||
|
|
|||||||||||||||
|
|
|||||||||||||||
|
|
|||||||||||||||
|
|
|||||||||||||||
|
|
|||||||||||||||
|
|
|||||||||||||||
|
|
|||||||||||||||
|
|
|||||||||||||||
|
|
|
|
|
|
|
|
|||||||||
Copyright Okan Duru©2014