Page 8 - Azerbaijan State University of Economics
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THE                      JOURNAL OF ECONOMIC SCIENCES: THEORY AND PRACTICE, V.80, # 2, 2023, pp. 4-13

                    FORECASTING OF ENGEL CURVE COMPONENTS
                    In this section, for the forecasting purposes household income and expenses per capita
                    data are taken from State Statistical Committee of Azerbaijan (table 1). The data given
                    in table 1 match the middle class of population in the country (stratified based on
                    income per capita) and possibly represent the averaged Engel curve throughout the
                    country.

                    Table 1: Household income and expenses per capita
                     №     Years    Income   Expenses    Expenses on food   Expenses on food (%)
                     1     2008     108.9      114.6          65.2                  0.57
                     2     2009     125.0      129.6          68.6                  0.53
                     3     2010     144.2      147.4          71.1                  0.48
                     4     2011     166.0      173.0          82.4                  0.48
                     5     2012     190.9      202.0          87.3                  0.43
                     6     2013     214.7      221.4          91.8                  0.41
                     7     2014     230.0      234.9          95.6                  0.41
                     8     2015     240.5      245.6          99.4                  0.40
                     9     2016     257.8      264.7          107.1                 0.40
                     10    2017     268.4      278.2          117.9                 0.42
                     11    2018     276.0      286.0          119.7                 0.42
                     12    2019     292.6      298.4          123.8                 0.41
                     13    2020     291.4      297.8          129.2                 0.43
                     14    2021     300.6      308.6          134.7                 0.44

                    First,  the  stationarity  of  the  time  series  is  checked  according  to  the  identification
                    phase. Based on the correlogram and graphical analysis and ADF (Augmented Dickey
                    Fuller)  test,  it  is  defined  that  whether  the  time  series  is  non-stationary  or  has  an
                    increasing trend. In the given case the time series data are non-stationary, so based on
                    correlogram  outputs  in  Eviews  program  package  the  ARIMA  models  were
                    constructed up to sixth differences.

                    Consequently the residuals are checked for White Noise using Ljung-Box Q Statistics;
                    AR roots should lie inside a unit circle showing covariance stationarity and MA roots
                    should lie inside a unit circle showing invertibility of ARMA process. Various models
                    constructed with separate values of p, d, and q parameters are selected by the Akakike,
                    Hannan-Quinn,  and  Schwarz  statistics,  whereby  a  smaller  value  identifies  a  better
                    parameterized model.We tested the model estimation parameters with Q-statistics. Where
                    the obtained probability values are greater than 0.05, the residuals are considered white
                    noise. Based on computed covariances, stationarity of the processes is ensured. Below in
                    tables 1-3 the  ARIMA models are given, built on Box-Jenkins methodology.



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