Table 2. Intervention models for EPA...

Table 2.

Intervention models for EPA series

Coefficient*/test	Unemployed	p-value	Employed	p-value	Active	p-value
2008 Q4	281.107	6.79E-06	−228.711	0.001	74.708	0.078
2009 Q1	738.085	2.05E-11	−686.885	1.06E-08	20.644	0.711
2009 Q2	712.330	3.94E-07	−788.308	3.11E-07	−82.662	0.114
2009 Q3	482.666	0.001	−665.194	6.09E-05	−173.107	0.001
2009 Q4	384.785	0.011	−534.620	0.001	−146.033	0.009
2010 Q1	338.765	0.016	−420.068	0.007	−92.235	0.031
2010 Q2	308.557	0.005	−328.215	0.007	–	–
2010 Q3	103.359	0.091	−124.560	0.070	–	–
${\hat{ϕ}}_{1}$	0.804	5.85E-21	0.801	3.41E-20	0.447	0.001
${\hat{Φ}}_{1}$	–	–	–	–	−0.163	0.111
${\hat{Φ}}_{2}$	–	–	–	–	−0.669	5.96E-11
${\hat{Θ}}_{1}$	0.547	0.001	0.524	0.001	–	–
${\hat{σ}}_{a}$	78.292	–	87.007	–	60.281	–
HQ information criterion	726.483	–	739.031	–	717.592	–
DH Test (Normality)	0.627	0.731	4.092	0.129	4.120	0.127
LM Test (ARCH(4))	3.389	0.494	2.792	0.593	7.892	0.095
Q(15)	10.857	0.622	28.209	0.008	20.434	0.059

Coefficient*/test	Unemployed	p-value	Employed	p-value	Active	p-value
2008 Q4	281.107	6.79E-06	−228.711	0.001	74.708	0.078
2009 Q1	738.085	2.05E-11	−686.885	1.06E-08	20.644	0.711
2009 Q2	712.330	3.94E-07	−788.308	3.11E-07	−82.662	0.114
2009 Q3	482.666	0.001	−665.194	6.09E-05	−173.107	0.001
2009 Q4	384.785	0.011	−534.620	0.001	−146.033	0.009
2010 Q1	338.765	0.016	−420.068	0.007	−92.235	0.031
2010 Q2	308.557	0.005	−328.215	0.007	–	–
2010 Q3	103.359	0.091	−124.560	0.070	–	–
${\hat{ϕ}}_{1}$	0.804	5.85E-21	0.801	3.41E-20	0.447	0.001
${\hat{Φ}}_{1}$	–	–	–	–	−0.163	0.111
${\hat{Φ}}_{2}$	–	–	–	–	−0.669	5.96E-11
${\hat{Θ}}_{1}$	0.547	0.001	0.524	0.001	–	–
${\hat{σ}}_{a}$	78.292	–	87.007	–	60.281	–
HQ information criterion	726.483	–	739.031	–	717.592	–
DH Test (Normality)	0.627	0.731	4.092	0.129	4.120	0.127
LM Test (ARCH(4))	3.389	0.494	2.792	0.593	7.892	0.095
Q(15)	10.857	0.622	28.209	0.008	20.434	0.059

Notes:

*The entries 2008:4–2010:3 represent impulse-type interventions in the corresponding dates. An impulse is a type of intervention which affects the series at a specific time point and never again. It can be captured by an artificial variable with zeros in all positions except a unit value at the specified date. The corresponding coefficient is an estimate of the difference between the observed and expected values of the series. The error model is an ARIMA(1,1,2) × (0,1,1)₄: (1 − ϕ₁B) (1 − ϕ₁B − ϕ₂B²) ∇₄∇y_t = (1 − Θ₁B⁴) a_t

Source: Authors’ own work

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