ARIMA modeling for time series analysis in STATA

In the previous article, all possibilities for performing Autoregressive Integrated Moving Average (ARIMA) modeling for the time series GDP were identified as under.

S. No ARIMA
1 (1,1,1)
2 (1,1,2)
3 (1,1,3)
4 (1,1,4)
5 (1,1,5)
6 (1,1,6)
7 (1,2,1)
8 (4,2,1)
9 (9,2,1)

 Table 1: ARIMA models as per ACF and PACF graphs.

Testing ARIMA models in STATA for time series analysis

The present article tests all these ARIMA models and identifies the appropriate one for the process of forecasting time series GDP. To start with testing ARIMA models in STATA:

  1. Click on ‘Statistics’ in ribbon
  2. Click on ‘time series’
  3. Select ‘ARIMA and ARMAX models’ (Figure 1 below)
Figure 1: Path for ARIMA modeling in STATA
Figure 1: Path for ARIMA modeling in STATA

Test 1: ARIMA (1,1,1)

A dialogue box will appear as shown in the figure below. Here fill four important options to carry out ARIMA testing. First select the time series variable fitting ARIMA model. In the present case, the time series variable is GDP. Therefore select ‘gdp’ in the ‘Dependent variable’ option. Second, record the ARIMA model specifications estimated in previous article. Therefore for the first ARIMA model, (1, 1, 1) (Table 1 above), select ‘1’ in ‘Autoregressive order (p)’, ‘1’ in ‘Integrated order (d)’, and ‘1’ in ‘Moving-average order (q)’.

Figure 2: Dialogue box for ARIMA modeling in STATA
Figure 2: Dialogue box for ARIMA modeling in STATA

After selecting the values for ARIMA model specifications, click on ‘Ok’ to proceed for results (Figure 3 below).

Figure 3: Dialogue box for ARIMA modeling in STATA
Figure 3: Dialogue box for ARIMA modeling in STATA

Now ARIMA (1, 1, 1) results will appear, as the figure below shows.

Figure 4: ARIMA (1,1,1) results for time series GDP
Figure 4: ARIMA (1,1,1) results for time series GDP

ARIMA results can be analysed through several components.

Log likelihood: The log likelihood component of ARIMA model should be high, like in the present case. The value of log likelihood (ignoring negative sign) is 554. This is sufficiently high. Compare log likelihood value of different ARIMA models and select the one which has the highest.

Coefficient of AR: The coefficient of AR should be less than 1 and at least 5% level of significance. Here, the coefficient of AR is significant at 5% (0.000) but is close to 1 (0.98967). This suggests that differenced time series GDP may still be non-stationary. Therefore, compare different ARIMA models based on the coefficients of AR and MA, their value (if close to zero) and their significance.

AIC/BIC: The value of ‘AIC’ and ‘BIC’ should be lowest in comparison to other ARIMA models. The value of AIC/BIC is usually the reverse of log likelihood function. Therefore instead of log likelihood, compare different ARIMA models based on the value of AIC/BIC. The ARIMA model with lowest AIC/BIC value will be more appropriate for forecasting.

Similarly, to compare the applicability of ARIMA (1,1,1) calculate next ARIMA model (1,1,2) to compare these two models.

Test 2: ARIMA (1,1,2)

Again filled the values in ARIMA specifications as per (1, 1, 2). After selecting the values for ARIMA model specifications, click on ‘OK’ to proceed for results (Figure 5).

Figure 5: Dialogue box for ARIMA modeling in STATA
Figure 5: Dialogue box for ARIMA modeling in STATA

The figure below shows the results for ARIMA (1,1,2).

Figure 6: ARIMA (1,1,2) results for time series GDP
Figure 6: ARIMA (1,1,2) results for time series GDP

 

ARIMA results as presented in above Figure 6 can be analysed through several components, as below:

Log likelihood: the value of log likelihood (ignoring negative sign) is 552 which is similar to previous ARIMA model (1, 1, 1).

Coefficient of AR: The coefficient of AR and MA are significant but coefficient of AR is insignificant at 5%. This suggests that differenced time series GDP may still be non-stationary. Therefore, similar to previous model, ARIMA (1,1,2) also is not appropriate for forecasting.

AIC/BIC: The value of AIC and BIC is less than previous model but only up to 1 point.  Therefore, no significant difference between ARIMA (1,1,1) and (1,1,2) can be seen. Thus both are inappropriate for forecasting time series GDP.

Test the remaining ARIMA models with different specifications following same procedures (Figures 1, 2 and 3). Then click on ‘OK’ for results.

Comparison of all ARIMA Models

This section presents a comparison of all ARIMA forecasting models mentioned in Table 1. Values of AR and MA coefficients, their significance and values of AIC and BIC are evaluated.

Table 2: Comparison of ARIMA models for time series GDP in STATA
Table 2: Comparison of ARIMA models for time series GDP in STATA

As mentioned previously, the variables of interest in appropriate ARIMA modeling are AR and MA component, AIC/BIC values and significance level. The Table 2 above has been organized as per these variables. Significance level of coefficients is indicated with sign “*”.

To select the best ARIMA model, first identify those models which have AR and MA coefficients as significant as well as lesser than 1. In the table above all the ARIMA models either have AR or MA coefficients close to 0 (indicating non-stationarity) or are insignificant at 5%. However, in case of ARIMA model (9, 2, 1), majority of AR and MA coefficients are lesser than 1 and significant at 5%. Therefore, in terms of coefficient selection, ARIMA model (9, 2, 1) is appropriate.

Second, identify those ARIMA models with minimum value of AIC or BIC. As per the table 2, ARIMA model (1, 2, 1) and ARIMA model (9, 2, 1) are the only ones with lowest AIC/BIC values. However, in ARIMA model (1, 2, 1), the coefficient of MA is almost 1, with insignificance greater than 5%. Therefore, this model cannot be treated for estimating the time series GDP. Therefore, ARIMA (9, 2, 1) is the most appropriate one to estimate the GDP time series.

Thus, ARIMA model (9, 2, 1) is the perfect model exhibiting all the structural trends in GDP data and can be useful for forecasting GDP. The following article explains prediction and forecasting using ARIMA in STATA.

How to build the univariate ARIMA model for time series in STATA?How to predict and forecast using ARIMA in STATA?

Priya Chetty

Partner at Project Guru
Priya is a master in business administration with majors in marketing and finance. She is fluent with data modelling, time series analysis, various regression models, forecasting and interpretation of the data. She has assisted data scientists, corporates, scholars in the field of finance, banking, economics and marketing.
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