Cjss second Issue: cjss second Issue qxd

New Research in Forecasting
Edward Raupp
Batumi International University
Nato Apkhazava,
The University of Georgia
ll institutions in Georgia are changing. Decision-makers in Government
agencies and private organizations need the best available data and analyses
to meet the challenges of changing times. Evidence-based forecasting can help
officials to predict the likely outcomes of their decisions. This paper reports on
work being done by T
HE
G
EORGIA
F
ORECAST
TM
and The University of Georgia Fore-
casting Center to identify and apply modern methods to Georgia’s most press-
ing issues. These methods include statistical techniques, such as time series
and regression, and judgmental adjustments, such as Delphi, surveys of con-
sumers and producers, and scenario scripting. One recent application studied
in this research project, forecasting tourism in Georgia, is an example of how
modern methods may improve decision-making by those who make public
policy, allocate resources, and make business decisions.
Statistical forecasting techniques assume that the near future will look
substantially like the recent past. We can, therefore, use data from recent his-
tory to make predictions about the near future. The longer the horizon, the
weaker the assumption. Two general classes of statistical forecasts are (1)
Causal and (2) Non-Causal. The most common causal model is regression: the
variable to be determined (Y) is related to one or more predictor variables
(Xi). For example, the quantity demanded of a good (Qd) may be estimated
from the price of that good (P) and other factors, such as income, relative
prices of substitute and complementary goods, weather, etc. Time series mod-
els are non-causal. We may not know why a variable of interest rises or falls,
but we can detect patterns from the recent past, such as trend, seasonality,
and cycles.
Judgmental methods assume some degree of knowledge that may not be
reflected in the statistics. These may include, among others, a Delphi panel of
experts, survey of consumers and producers, game theory, and scenario script-
ing. 
Because of the high degree of uncertainty in the Georgian economic,
political, and social environment, the use of one model is unlikely to yield re-
liable forecasts. Therefore, T
HE
G
EORGIA
F
ORECAST
TM
and The University of Geor-
gia Forecasting Center use a combination of methods and continually
evaluate the performance of their forecasts. This paper uses the case of fore-
casting tourism to demonstrate these techniques.
A
28
Caucasus Journal of Social Sciences

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axali kvleva prognozirebis sferoSi
eduard raupi
baTumis saerTaSoriso universiteti
nato afxazava
saqarTvelos universiteti
vela organo garkveul cvlilebas ganicdis saqarTveloSi.
sajaro da kerZo seqtoris pasuxismgebel pirebs sWirdebaT
saukeTesod damuSavebuli da gaanalizebuli informacia, rom
drois gamowvevas Sesabamisad upasuxon. faqtebze dafuZnebuli
prognozi saSualebas miscems pasuxismgebel pirebs, maT mier
miRebuli gadawyvetilebebis savaraudo Sedegebze winaswar
iqonion warmodgena. naSromi exeba 
T
HE
G
EORGIA
F
ORECAST
TM
da
saqarTvelos universitetis prognozirebis centris mier ganx-
orcielebul samuSaos, saqarTveloSi, amJamad aqtualur sak-
iTxebze,  meTodebis gansazRvrisa da gamoyenebis mimarTulebiT.
meTodebSi igulisxmeba statistikuri meqanizmebi da profe-
siuli prognozis regulireba. kvleviT proeqtSi, Seswavlis
erT-erTi uaxlesi mimarTuleba aris turizmis prognozireba
da analizi saqarTveloSi. aRniSnuli sfero saukeTeso maga-
liTia, imisa Tu rogor SeiZleba Tanamedrove meTodebis
gamoyenebam gazardos miRebuli gadawyvetilebebis sisworis
albaToba. statistikuri prognozirebis mixedviT uaxloesi
momavali faqtiurad igive suraTs gvaZlevs, ra suraTic
gvqonda uaxloes warsulSi. Zvel informaciaze dayrdnobiT
SesaZlebelia garkveuli samomavlo gaTvlebis gakeTeba. arse-
bobs statistikuri prognozirebis 2 zogadi klasi: 1) mizezo-
brivi da 2) ara mizezobrivi. yvelaze gavrcelebuli
mizezobrivi modelia regresi, xolo ara mizezobrivi _ drois
RerZis modeli. profesiuli prognozirebis meTodebi statis-
tikis codnis garda sxva saxis codnasac moiTxovs. saqarTve-
los ekonomikaSi, politikasa da sazogadoebaSi arsebuli
gaurkvevlobebis gamo, mxolod erTi modelis gamoyeneba
arasakmarisia obieqturi prognozirebisTvis. Tumca 
T
HE
G
EORGIA
F
ORECAST
TM
da saqarTvelos universitetis prognozirebis cen-
tri ramdenime meTods iyenebs da sistematurad afasebs mis
mier warmoebul saqmianobas. zemoxsenebuli meqanizmebi naS-
romSi ganxilulia  turizmis sferos prognozirebis magal-
iTze.  
y
29
Caucasus Journal of Social Sciences

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All institutions in Georgia are changing. Decision-makers in govern-
ment agencies and private organizations need the best available data and
analyses to meet the challenges of changing times. Evidence-based forecast-
ing can help officials to predict the likely outcomes of their decisions (Arm-
strong, 2006). This paper reports on work being done by T
HE
G
EORGIA
F
ORECAST
TM
and The University of Georgia Forecasting Center to identify and
apply modern methods to Georgia’s most pressing issues. 
These methods include statistical techniques, such as time series and re-
gression, and judgmental techniques, such as Delphi, surveys of consumers
and producers, and scenario scripting. One recent application studied in this
research project, forecasting tourism in Georgia, is an example of how modern
methods may improve decision-making by those who make public policy, al-
locate resources, and make business decisions (Raupp & Apkhazava, 2009).
Makridakis and Hibon (1979) note that, “The ultimate test of any fore-
cast is whether or not it is capable of predicting future events accurately.” Sta-
tistical forecasting techniques assume that the near future will look
substantially like the recent past. We can, therefore, use data from recent his-
tory to make predictions about the near future. The longer the horizon, how-
ever, the weaker the assumption. Two general classes of statistical forecasts
are (1) causal and (2) non-causal. The most common causal model is regres-
sion: the variable to be determined (Y) is related to one or more predictor
variables (Xi). For example, the quantity demanded of a good (Qd) may be es-
timated from the price of that good (P) and other factors, such as income, rel-
ative prices of substitute and complementary goods, weather, etc. Time series
models are non-causal; we may not know why a variable of interest rises or
falls, but we can detect patterns from the recent past, such as trend, season-
ality, and cycles.
Judgmental methods assume some degree of knowledge that may not
be reflected in past data. These may include, among others, a Delphi panel of
experts, survey of consumers and producers, game theory, and scenario
scripting. 
Because of the high degree of uncertainty in the Georgian economic, po-
litical, and social environment, the use of one model is unlikely to yield reli-
able forecasts. Therefore, T
HE
G
EORGIA
F
ORECAST
TM
and The University of Georgia
Forecasting Center use a combination of methods and continually evaluate
the performance of their forecasts. This paper uses the case of forecasting the
tourism sector of the Georgian economy to demonstrate these techniques.
30
Caucasus Journal of Social Sciences

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