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Asif  Alıyev: Forecasting of Engel Curve Components with the Application of ARIMA Method

                    Kumar & et.al (2008) showed that food consumption deprivation index has very little
                    correlation with the traditional measures of poverty. They raised a question how to
                    combine Engel curves for some other vital goods such as health services, education,
                    water, energy, etc. into a single index of poverty. Almas (2012) estimated households
                    Engel curves for nine different countries, and observed stability of the Engel law
                    across the countries. James & et.al (2012) estimated food elasticities at different levels
                    of  expenditure  indicating  that  as  income  and  overall  food  expenditure  increases,
                    expenditure  for  foods  will  increase  at  a  declining  rate  in  line  with  Engel’s  law.
                    Çağlayan   (2012)  estimated  Engel  curves  for  food and  clothing  in  Turkey using
                    different  econometric  models  indicating  that  the  food  expenditure  is  the  largest
                    expenditure and the portion of this expenditure in the household budget decreases as
                    the  income  increases  that  is  consistent  with  the  Engel's  law.  Pritchett  &  Spivack
                    (2013) introduced a new simple, intuitive appeal and  consistent with the Engel’s Law
                    ratio  measure  for  comparison  of  consumption  possibilities  over  countries    using
                    average  food  shares  and  taking  into  account  purchasing  power  of  currencies  and
                    adjusted currency conversions. Gibson & Kim (2015) revealed that food shares vary
                    with relative prices, but sometimes spatial price survey is not possible, and unit values
                    are  sometimes  used  as  price  proxy.  Önder  (2017)  studied  that  the  consumer
                    consumption  patterns  can  vary  based  on  the  geographical  and  socio-economic
                    structure  differences,  the  survey  time  and  the  specific  country  circumstances.  Li
                    (2021)  empirically  observed  that  richer  consumers  purchasing  a  larger  variety  of
                    products than poor ones. Divergent Engel curve slopes rely on the relative price and
                    transaction  cost  marginally  and  affect  the  distribution  of  variety  gains  and  the
                    measurement of factual welfare. Laborda et. al (2021) proposed the computation of
                    total  household  expenditure in  Household  Budget  Surveys  (Engel  curves) using a
                    generalized  linear  model   estimator,  to  deal  with  the  heteroskedasticity  problem
                    encountered in  the ordinary least squares method.

                    In our approach the following factors  that affect Engel curve estimations were not
                    taken into account:
                       -  national characteristics (mentality, psychology, religious attitudes etc.),
                       -  personal preferences and consumer behavior,
                       -  different income groups behaviors,
                       -  inflation, purchasing power and currency conversions,
                       -  economical, geographical and regional differences.

                    Research  objective  of  the  paper  is  to  forecast  Engel  curve  components  with  the
                    application of ARIMA time series forecasting method. The Engel curve evaluation is
                    recurrently important to get information  about average wellbeing situation  in the
                    country.


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