Skip to main content

Advertisement

Table 3 PANIC analysis of CPI inflation. Spanish provinces. 1955.1–1978.6

From: The economic integration of Spain: a change in the inflation pattern

  No trend specification Trend specification
\( k \) \( {\text{ADF}}_{{\hat{e}}}^{c} (i) \) \( S_{{\hat{e}_{1} }}^{c} (i) \) \( k \) \( {\text{ADF}}_{{\hat{e}}}^{\tau } (i) \) \( S_{{\hat{e}_{1} }}^{\tau } (i) \) \( \frac{{\sigma (\Delta \hat{e}_{it} )}}{{\sigma (\Delta \pi_{it} )}} \) \( \frac{{\sigma (\lambda^{{\prime }}_{i} F_{t} )}}{{\sigma (\hat{e}_{it} )}} \)
Álava 4 −3.969*** 0.103 0 −3.990*** 0.092 0.713 1.808
Albacete 0 −4.315*** 0.282* 0 −4.611*** 0.110* 0.621 2.513
Alicante 5 −4.327*** 0.099 2 −4.582*** 0.067 0.566 2.728
Almería 0 −3.904*** 0.056 0 −4.115*** 0.056 0.604 2.299
Asturias 0 −3.997*** 0.072 0 −4.182*** 0.068 0.704 2.018
Ávila 0 −3.956*** 0.102 0 −4.360*** 0.101* 0.713 2.020
Badajoz 8 −4.871*** 0.053 0 −4.879*** 0.052 0.579 3.554
Balears, Illes 5 −3.026*** 0.386** 5 −2.917** 0.190*** 0.814 1.452
Barcelona 0 −3.631*** 0.092 0 −3.801*** 0.085 0.773 2.223
Bizkaia 1 −4.503*** 0.187 1 −5.116*** 0.072 0.734 2.348
Burgos 1 −2.855*** 0.199 1 −3.317*** 0.047 0.790 2.568
Cáceres 5 −4.361*** 0.284* 5 −4.368*** 0.039 0.713 2.504
Cádiz 2 −4.048*** 0.100 2 −4.089*** 0.077 0.621 2.501
Cantabria 0 −4.841*** 0.113 0 −4.861*** 0.065 0.796 1.747
Castellón 2 −3.493*** 0.403** 2 −3.781*** 0.100* 0.647 1.758
Ciudad Real 0 −3.315*** 0.226 0 −3.823*** 0.064 0.793 1.429
Córdoba 0 −2.263** 0.245* 0 −3.055** 0.062 0.481 2.678
Coruña, A 7 −3.194*** 0.601*** 7 −3.345*** 0.194*** 0.679 2.894
Cuenca 3 −3.709*** 0.193 2 −4.115*** 0.106* 0.476 2.903
Gipuzkoa 6 −4.536*** 0.171 6 −4.670*** 0.043 0.642 2.732
Girona 1 −3.995*** 0.228 1 −4.087*** 0.071 0.826 2.256
Granada 0 −4.153*** 0.117 0 −4.306*** 0.092 0.616 2.984
Guadalajara 1 −4.610*** 0.073 1 −4.615*** 0.056 0.699 2.496
Huelva 0 −4.262*** 0.107 2 −4.252*** 0.063 0.692 2.854
Huesca 4 −3.838*** 0.216 4 −4.414*** 0.114* 0.689 2.441
Jaén 0 −4.883*** 0.068 0 −4.876*** 0.054 0.639 2.371
León 2 −5.574*** 0.142 2 −5.555*** 0.052 0.583 2.646
Lleida 0 −3.462*** 0.134 0 −3.565*** 0.068 0.755 2.075
Lugo 0 −4.357*** 0.043 0 −4.586*** 0.027 0.706 2.334
Madrid 1 −3.878*** 0.467** 1 −4.187*** 0.209*** 0.545 2.426
Málaga 0 −4.878*** 0.276* 0 −4.877*** 0.061 0.642 2.531
Murcia 3 −3.824*** 0.051 0 −4.072*** 0.037 0.478 3.477
Navarra 0 −3.068*** 0.073 0 −3.605*** 0.035 0.659 2.131
Ourense 5 −5.854*** 0.039 5 −5.858*** 0.025 0.745 2.371
Palencia 0 −4.587*** 0.056 0 −4.587*** 0.056 0.581 3.382
Palmas, Las 3 −2.388** 0.755*** 3 −3.459*** 0.072 0.879 1.259
Pontevedra 2 −3.528*** 0.421** 2 −4.335*** 0.026 0.742 2.182
Rioja, La 8 −3.044*** 0.262* 5 −3.015** 0.121* 0.713 1.947
Salamanca 0 −4.158*** 0.079 0 −4.174*** 0.081 0.684 1.924
Santa Cruz de Tenerife 2 −3.187*** 0.475** 2 −4.164*** 0.057 0.947 0.607
Segovia 0 −4.200*** 0.069 0 −4.299*** 0.052 0.641 3.076
Sevilla 0 −4.951*** 0.182 0 −4.991*** 0.066 0.632 2.607
Soria 3 −2.820*** 0.115 3 −2.792** 0.105* 0.725 2.173
Tarragona 3 −4.006*** 0.097 0 −4.002*** 0.096 0.645 3.100
Teruel 1 −3.876*** 0.199 1 −4.062*** 0.147** 0.778 2.225
Toledo 4 −3.443*** 0.704*** 4 −3.980*** 0.062 0.816 1.779
Valencia 0 −5.692*** 0.096 0 −5.747*** 0.049 0.687 1.853
Valladolid 0 −3.199*** 0.107 0 −3.241*** 0.081 0.744 1.807
Zamora 0 −2.091** 0.139 0 −2.738** 0.129** 0.751 1.290
Zaragoza 1 −5.986*** 0.036 1 −6.000*** 0.035 0.581 3.021
Critical values         
  1 % −2.580 0.536   −3.167 0.185   
  5 % −1.950 0.324   −2.577 0.122   
  10 % −1.620 0.235   −2.314 0.098   
Bai and Ng (2004a) pooled statistics
  \( P_{{\hat{e}}}^{c} \) 809.457*** N.A. \( P_{{\hat{e}}}^{\tau } \) 810.675*** N.A.   
  \( Z_{{\hat{e}}}^{c} \) 50.166*** N.A. \( Z_{{\hat{e}}}^{\tau } \) 50.252*** N.A.   
Bai and Ng (2010) pooled statistics
  \( P_{a}^{c} \) −70.372***   \( P_{a}^{\tau } \) −51.347***    
  \( P_{b}^{c} \) −17.300***   \( P_{b}^{\tau } \) −18.592***    
  \( {\text{PMSB}}^{c} \) −4.972***   \( {\text{PMSB}}^{\tau } \) −6.358***    
  1. The augmented autoregressions employed in the ADF analysis select the optimal lag-order with the t-sig criterion of Ng and Perron (1995), setting a maximum lag-order equal to \( p = 4(T/100)^{1/4} \). The stationarity tests are based on 12 lags of the Quadratic spectral kernel. The information criterion BIC 3 has chosen an optimal rank equal to 1. \( P_{{\hat{e}}} \) is distributed as \( \chi_{100}^{2} \), with 1, 5 and 10 % critical values of 135.807, 124.342 and 118.498, respectively. \( Z_{{\hat{e}}} \) is distributed as N(0,1) with 1, 5 and 10 % critical values equal to 2.326, 1.645 and 1.282, respectively. \( P_{a} \), \( P_{b} \) and \( {\text{PMSB}} \) are distributed as N(0,1) with 1, 5 and 10 % critical values of −2.326, −1.645 and −1.282, respectively. ***, ** and * imply rejection of the null hypothesis at 1, 5 and 10 %, respectively
Common factor analysis Statistic Critical values   Statistic Critical values
1 % 5 % 10 % 1 % 5 % 10 %
\( {\text{ADF}}_{{\hat{F}}}^{c} \) −2.783* −3.430 −2.860 −2.570 \( {\text{ADF}}_{{\hat{F}}}^{\tau } \) −3.427** −3.960 −3.410 −3.120
\( S_{{\hat{F}}}^{c} \) 1.185*** 0.743 0.463 0.343 \( S_{{\hat{F}}}^{\tau } \) 0.371*** 0.215 0.149 0.120
  1. The augmented autoregressions employed in the ADF analysis select the optimal lag-order with the t-sig criterion of Ng and Perron (1995), setting a maximum lag-order equal to \( p = 4(T/100)^{1/4} \). The stationarity tests are based on 12 lags of the Quadratic spectral kernel. The information criterion BIC 3 has chosen an optimal rank equal to 1. \( P_{{\hat{e}}} \) is distributed as \( \chi_{100}^{2} \), with 1, 5 and 10 % critical values of 135.807, 124.342 and 118.498, respectively. \( Z_{{\hat{e}}} \) is distributed as N(0,1) with 1, 5 and 10 % critical values equal to 2.326, 1.645 and 1.282, respectively. \( P_{a} \), \( P_{b} \) and \( {\text{PMSB}} \) are distributed as N(0,1) with 1, 5 and 10 % critical values of −2.326, −1.645 and −1.282, respectively. ***, ** and * imply rejection of the null hypothesis at 1, 5 and 10 %, respectively