Skip to main content
Latin American Economic Review

Table 4 PANIC analysis of CPI inflation. Spanish Provinces. 1978.7–2014.4

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

5

−4.337***

0.148

5

−5.455***

0.057

0.523

3.665

Albacete

7

−3.058***

0.293*

7

−4.592***

0.109*

0.478

4.393

Alicante

8

−5.769***

0.068

8

−5.884***

0.042

0.425

4.434

Almería

4

−3.722***

0.115

4

−4.311***

0.087

0.607

3.418

Asturias

4

−2.763***

0.091

4

−3.964***

0.063

0.498

2.880

Ávila

1

−5.079***

0.057

0

−5.082***

0.042

0.556

3.218

Badajoz

0

−4.082***

0.073

0

−4.335***

0.062

0.426

4.715

Balears, Illes

5

−7.753***

0.067

3

−8.751***

0.061

0.521

4.964

Barcelona

0

−4.111***

0.420**

1

−4.638***

0.166**

0.538

4.125

Bizkaia

1

−1.824*

0.447**

3

−2.959**

0.159**

0.503

3.543

Burgos

2

−7.806***

0.130

2

−8.060***

0.063

0.529

5.359

Cáceres

1

−3.972***

0.083

2

−4.041***

0.064

0.485

3.707

Cádiz

5

−3.923***

0.068

5

−4.791***

0.068

0.531

5.720

Cantabria

7

−7.205***

0.192

3

−7.666***

0.124**

0.412

4.703

Castellón

2

−6.442***

0.219

2

−6.403***

0.062

0.591

4.561

Ciudad Real

0

−6.191***

0.283*

0

−6.725***

0.089

0.478

4.737

Córdoba

8

−5.243***

0.186

0

−5.127***

0.160**

0.375

4.572

Coruña, A

1

−2.781***

0.122

1

−4.841***

0.078

0.619

2.706

Cuenca

0

−4.685***

0.101

0

−6.507***

0.038

0.346

3.451

Gipuzkoa

2

−3.642***

0.386**

2

−6.034***

0.062

0.616

2.587

Girona

8

−4.414***

0.119

8

−4.848***

0.070

0.426

5.012

Granada

2

−5.377***

0.046

5

−5.370***

0.046

0.476

4.820

Guadalajara

4

−5.199***

0.101

3

−5.132***

0.083

0.455

4.684

Huelva

0

−3.011***

0.073

3

−4.153***

0.046

0.423

4.205

Huesca

8

−4.032***

0.089

8

−5.438***

0.029

0.615

3.869

Jaén

2

−6.002***

0.059

2

−7.693***

0.044

0.559

4.025

León

3

−5.093***

0.268*

3

−5.593***

0.101*

0.418

6.499

Lleida

3

−4.560***

0.088

3

−5.276***

0.089

0.659

3.487

Lugo

4

−9.404***

0.042

4

−9.433***

0.020

0.608

3.319

Madrid

0

−3.535***

0.074

0

−3.926***

0.051

0.431

4.052

Málaga

8

−3.856***

0.087

8

−4.861***

0.075

0.702

3.213

Murcia

3

−6.596***

0.102

3

−6.677***

0.076

0.492

4.704

Navarra

0

−4.801***

0.523**

0

−4.900***

0.318***

0.340

6.899

Ourense

2

−3.564***

0.107

7

−4.056***

0.097

0.776

1.141

Palencia

7

−5.147***

0.157

7

−5.186***

0.115*

0.469

4.851

Palmas, Las

1

−2.821***

0.082

3

−3.881***

0.085

0.792

1.305

Pontevedra

7

−2.664***

0.096

7

−3.117**

0.064

0.624

3.713

Rioja, La

0

−4.420***

0.174

0

−4.708***

0.100*

0.527

4.309

Salamanca

2

−3.539***

0.058

2

−4.897***

0.049

0.542

5.490

Santa Cruz de Tenerife

3

−2.825***

0.043

8

−4.731***

0.043

0.819

1.249

Segovia

0

−3.453***

0.358**

0

−3.815***

0.186***

0.535

3.487

Sevilla

3

−5.055***

0.231

3

−5.054***

0.178**

0.309

7.085

Soria

1

−5.262***

0.078

1

−5.177***

0.082

0.590

5.611

Tarragona

0

−4.923***

0.289*

0

−6.262***

0.101*

0.555

3.805

Teruel

7

−3.801***

0.188

7

−5.345***

0.074

0.534

3.539

Toledo

3

−4.922***

0.029

3

−6.685***

0.029

0.412

4.731

Valencia

1

−4.421***

0.079

1

−5.820***

0.044

0.465

3.675

Valladolid

0

−5.908***

0.106

0

−6.246***

0.065

0.377

7.411

Zamora

0

−6.451***

0.160

0

−6.497***

0.072

0.537

4.501

Zaragoza

1

−7.648***

0.052

0

−8.372***

0.034

0.480

4.665

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

  

\( P_{{\hat{e}}}^{c} \)

835.432***

N.A.

\( P_{{\hat{e}}}^{\tau } \)

895.914***

N.A.

 
  

\( Z_{{\hat{e}}}^{c} \)

52.003***

N.A.

\( Z_{{\hat{e}}}^{\tau } \)

56.280***

N.A.

 

Bai and Ng (2010) pooled statistics

  

\( P_{a}^{c} \)

−8.448***

 

\( P_{a}^{\tau } \)

−15.370***

  
  

\( P_{b}^{c} \)

−5.343***

 

\( P_{b}^{\tau } \)

−8.698***

  
  

\( {\text{PMSB}}^{c} \)

−3.231***

 

\( {\text{PMSB}}^{\tau } \)

−4.722***

  

Common factor analysis

Statistic

Critical values

 

Statistic

Critical values

1 %

5 %

10 %

1 %

5 %

10 %

\( {\text{ADF}}_{{\hat{F}}}^{c} \)

−2.022

−3.430

−2.860

−2.570

\( {\text{ADF}}_{{\hat{F}}}^{\tau } \)

−2.408

−3.960

−3.410

−3.120

\( S_{{\hat{F}}}^{c} \)

3.608***

0.743

0.463

0.343

\( S_{{\hat{F}}}^{\tau } \)

0.859***

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 BIC3 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
Back to article page