Download Price interaction in aquaculture and capture fisheries

PRICE INTERACTION BETWEEN
AQUACULTURE AND FISHERY
AN ECONOMETRIC ANALYSIS OF
SEABREAM AND SEABASS IN ITALIAN MARKETS
by
Roberta Brigante
(IREPA)
and
Audun Lem
(FAO)
XIII EAFE CONFERENCE
SALERNO
18-20
APRIL
2001
The designations employed and the presentation of the material in
this publication do not imply the expression of any opinion
whatsoever on the part of the Food and Agriculture Organization of
the United Nations concerning the legal status of any country,
territory, city or area or of its authorities, or concerning the
delimitation of its frontiers or boundaries.
The designations “developed” and “developing” economies
are intended for statistical convenience and do not necessarily
express a judgement about the stage reached by a particular country,
country territory or area in the development process.
The views expressed herein are those of the authors and do
not necessarily represent those of the Food and Agriculture
Organization of the United Nations nor of their affiliated
organization(s).
- iii -
ABSTRACT
This paper analyses the impact of aquaculture in the Mediterranean on the market
for fish from capture fisheries, based on two species – seabream and seabass. The
interaction between aquaculture and capture fish prices is investigated in order to
verify a possible stabilization effect on the market as a result of increased supply.
Econometrics models, unit roots and co-integration are applied to test the
linkages between prices of farmed and captured products, using quarterly data
from January 1991 to December 1998.
The analysis indicates that captured and farmed species are not substitutes
for each other, and there is no link between the prices. A long-run change in
farmed species price has had no impact on the long-run price of captured species
prices.
- v -
CONTENTS
ABSTRACT
iii
INTRODUCTION
1
SECTION 1 – ANALYSIS OF THE SECTOR
3
World Fisheries and Aquaculture in 1998
3
SECTION 2 – METHODOLOGICAL ANALYSIS
5
SECTION 3 – THE DATA
7
SECTION 4 – THE ECONOMETRIC MODEL
9
THE SEABASS CASE
ADL (4,4) Model estimate
Analysis of the dynamic structure of the ADL (4,4) model:
Redundant Variable test
Reduced Model estimate
Restrictions of the reduced model
Distributed Lag Model Estimate
Co-integration Analysis between LC and LA
Error Correction Model estimate
Granger Causality Test
Comments and conclusions on the Seabass case
10
10
THE SEABREAM CASE
ADL (4,4) Model estimate
Analysis of the dynamic structure of the ADL (4,4) model:
Redundant variable test
Reduced Model estimate
Restrictions on the reduced model
Co-integration analysis
First Difference Model estimate
Granger Causality Test
Paired Granger Causality Tests
Comments and conclusions on the Seabream case
23
23
12
12
15
16
18
19
22
22
25
25
28
28
28
31
31
31
SECTION 5 – CONCLUSIONS
32
BIBLIOGRAPHY
34
FOREWORD
The initiative to this paper was taken by Audun Lem, FAO who also supplied much of the data
and supervised the actual work. The analysis was carried out by Roberta Brigante, IREPA under
the scientific supervision of Professor Massimo Spagnolo, IREPA..
Price interaction between aquaculture and fishery
1
INTRODUCTION
This report is the result of a study on the impact of aquaculture on capture fishing, using an
empirical and econometric approach.
The research focuses on two lines of enquiry: first, to understand whether, and, if so, how,
the increase in supply of product from aquaculture in the last decade could be interpreted as a
substitution process of the cultured product for the captured one; and, second, to verify whether
the increased supply could have a stabilizing effect on the market.
The authors try to answer these questions using a model to explain the dynamics of
interaction between the time series of prices of captured and farmed species. In particular, the
time series of prices are examined in order to establish if they are co-integrated. If a co-integration
relationship exists, then a long-run relationship and a set of short-run adjustment parameters could
also be found.
The analysis is based on the Italian market during the period 1991 to 1998, examining the
time series of prices of two species, seabass and seabream, where supply came both from capture
and farm fisheries. The Italian market is used because of the ample consumption of the two
species and because it absorbs about 70% of the production of seabass and seabream in the
Mediterranean. Studying the Italian case seemed therefore appropriate.
The report has five main sections.

The first section illustrates briefly the world state of fishing and aquaculture. The
purpose is to show the changing structure of fish production. Overexploitation of marine
resources – the principal cause of impoverishment of fish stocks – is affecting the
capture level, which in 1998 again showed a decrease. In contrast, aquaculture is in
continuous growth and production represents nearly a quarter of total world fish
production.

In the second section, the methodology used for the empirical analyses is presented.

In the third section, the data used for the analyses are presented.

In the fourth section, the results of econometric analyses are presented.

In the fifth section, conclusions are drawn and discussed.
2
Introduction
Price interaction between aquaculture and fishery
3
SECTION 1
ANALYSIS OF THE SECTOR
WORLD FISHERIES AND AQUACULTURE IN 1998
In 1998, world fish production was 116.9 million tonnes (t), showing a slight decrease from 1997
(see Table 1).
Table 1. World fish production, 1990-1998
1990
1991
1992
Production
1993
1994
1995
1996
1997
1998
million tonnes
1998
%
Var.(1)
98-97
(%)
mvr(2)
90-98
Capture
85.5
84.4
85.3
86.5
91.4
91.6
93.2
93.3
87.9
75.2
-5.8
0.4
Aquaculture
13.1
13.7
15.5
17.9
20.8
24.5
26.8
28.8
29.0
24.8
+0.7
10.6
Total
98.5
98.1
100.7
104.4
122.2
116.0
119.9
122.1
116.9
100.0
-4.3
2.2
Notes: (1) Percentage change between 1997 and 1998. (2) mvr = marginal variation rate
Source: FAO
Around 75% of the 1998
production came from capture fisheries,
with aquaculture in continuous growth.
Capture and aquaculture production trends (t106)
(Source: Based on FAO data.
World production in 1998 was
4.3% less than 1997, but aquaculture
production
increased
(Figure 1).
Aquaculture is reliably forecast to
continue to grow, to stabilize 2010
around the 35 million t/yr level.
In contrast, capture fisheries
production in 1998 was only about
87.9 million t, in comparison to the
93.3 million t in 1997.
The decreasing trend in capture
fisheries is due, broadly, to the excessive
fishing effort that is one of the primary causes of overexploitation and even extinction of some
species. A lot of species are, in fact, completely exploited (some 44% of the stock), overexploited
or in danger of extinction: this implies production crises, forced inactivity periods, and notable
variations in capture quantities from one year to the next (causing serious fluctuations in prices).
For this reason, FAO has elaborated the Code of Conduct for Responsible Fisheries, that
points out behavioural principles for respecting and safeguarding aquatic life, focusing attention
on ecosystem problems and biodiversity.
In this scenario, aquaculture could provide the means to satisfy the growing demand for fish
in the face of a decreasing supply from the wild. From 1990 to 1997, in fact, world per caput
consumption grew from 13.3 kg to 15.9 kg, and this trend can be expected to continue in the next
few years (Table 2 and Figure 2).
Section 1 – Analysis of the sector
4
Table 2. World consumption of fish
products per caput (kg/head)
Year
Per caput
consumption
(kg/head)
1990
13.3
1991
12.6
1992
13.0
1993
13.6
1994
14.1
1995
15.1
1996
15.4
1997
15.9
Source: FAO data
Trend in per caput consumption of fish products from 1990 to 1997
(kg/head) (Source: Based on FAO data)
.
Price interaction between aquaculture and fishery
5
SECTION 2
METHODOLOGICAL ANALYSIS
The method of analysis used in order to identify the appropriate model was the one proposed by
the London School of Economics (LSE), generally referred to as “from general to particular.”
This procedure, traditionally advocated by LSE, is developed in the opposite direction to classical
econometrics.
In the first phase, you assume a general model with a lot of lags in the endogenous
variabiliy as well as in the exogenous variability. The general model can be formulated as
follows:
z
m
z
yt   ai yt i    k ,i xk ,t i   t
i 1
[1]
k 1 i  0
In the second phase, you test the correct specification of the general model, if the
parameters are significant and stable. At this point you delete from the general model the notsignificant parameters and estimate the resultant model.
On the parameters of the resultant model, the LSE method suggests imposing nine
restrictions. In this way you have nine particular models, each of which subtends a specific
economic behaviour.
For example, if z=1 and m=1, the general model [1] becomes an Autoregressive Lags
Model (ADL) (1,1) model:
y  ay t 1   1 xt   2 xt 1   t
[2]
Imposing the nine restrictions on the parameters of the model obtained [2], you have the
following nine models:
Type of model
Restriction
1) static regression
a =  = 0
2) autoregressive model
 =  = 0
3) tendency indicator model
a =  = 0
4) growth rates model
a=1,  = -
5) distributed lag model
a= 0
6) partial adjustments model
 = 0
7) restriction to common factor model
 = - a 
8) error correction model (ECM)
K = (1 + 2)  (1- a)
9) deferred model
1 = 0
6
Section 2 – Methodological analysis
The most interesting model of these is the ECM model, coming from the ADL (1,1) model,
namely:
yt = 1 xt + (a-1)(y-cx)t-1 +et
where:
yt = yt - yt-1
xt=xt - xt-1;
(y-cx)t-1 is the ECM term that represents the deviation from the long-run equilibrium;
c is the co-integration coefficient.
In this model, variations of yt depend from variations of the dependent variable yxt as well
as from disequilibrium (y-cx)t-1 in the previous period. Thus far, this model is the most complete
because it gives information about both short and long runs.
The parameter 1 represents the short-run reaction; the parameter (a-1) represents the
reaction of the dependent variable to the deviations from the static equilibrium.
Engle and Granger (1987) showed that you can estimate an ECM model only if the
variables are co-integrated. If a co-integration relationship does not exist, there is no long run
equilibrium curve. In this case, the model that you can estimate is a first differences model.
Price interaction between aquaculture and fishery
7
SECTION 3
THE DATA
The models are estimated on the basis of time series of quarterly prices for captured and farmed
seabass and seabream, collected in Italy from 1991 to 1998.
Captured seabass and seabream prices time series are the result of monitoring of the
operators of the sector, collected by IREPA.
Farmed seabass and seabream prices time series are collected by FAO (GLOBEFISH)
Captured seabass prices time series show a constant pattern over the years, with oscillations
in the annual averages depending on the course of captures, in constant diminution since 1995. Of
note is the jump each year in December because of Christmas festivities.
Captured seabream prices time series show hard oscillations in the annual averages
connected to the pattern of captures, in constant diminution since 1994. Note also a jump similar
to that of seabass in December because of Christmas festivities.
Farmed seabass and seabream prices time series show a negative trend. This is due to the
expansion of Italian aquaculture supply, as well as to imports from Greece at extremely
competitive prices.
Some of these parameters are illustrated in the following figures, namely supply of captured
and farmed seabass (Figure 3) and seabream (Figure 4) in Italy; and monthly prices for captured
and farmed seabass (Figures 5 and 6) and seabream (Figures 7 and 8) in Italy.
Figure 4. Seabream – trends in capture and
aquaculture production in Italy, 1991-1998
(Source: FAO data)
Seabass – trends in capture and aquaculture
production in Italy, 1991-1998
(Source: FAO data)
6000
6000
5000
5000
4000
4000
3000
3000
2000
2000
1000
91
1000
92
93
94
QSC
.
95
96
QSA
97
98
0
91
92
93
94
QOC
95
96
QOA
97
98
Section 3 – Data
8
Captured seabass: Trend of monthly prices in Italy,
1991-1998 (Source: IREPA)
52000
Farmed seabass: Trend of monthly prices in Italy,
1991-1998 (Source: IREPA)
24000
22000
48000
20000
44000
18000
40000
16000
36000
14000
12000
32000
91
92
93
94
95
96
97
91
98
92
93
94
95
96
97
98
PSA
PSC
Captured Seabream: Trend of monthly prices in
Italy – 1991-98 (Source: IREPA)
44000
Farmed Seabream: Trend of monthly prices in
Italy – 1991-98 (Source: FAO)
24000
22000
40000
20000
18000
36000
16000
32000
14000
12000
28000
10000
24000
8000
91
92
93
94
95
POC
96
97
98
91
92
93
94
95
POA
96
97
98
Price interaction between aquaculture and fishery
9
SECTION 4
THE ECONOMETRIC MODEL
This analysis evaluates the impact of aquaculture on fisheries in terms of prices. The data are
quarterly captured (C) and farmed (A) seabass and seabream prices time series collected in Italy
from 1991 to 1998. The analysis uses the logarithmic specification of prices time series (LC and
LA) because it accomodates the elasticity of the model.
The variables of the general model are:

The dependent variable is LC.

The independent variables are:
–
the dependent variable LC, four times lagged;
–
the variable LA;
–
the variable LA, four times lagged;
–
three dummy variables – D1, D2, D3 – to accommodate the seasonal component;
–
one dummy variable – DU95 or DU94 – to consider exceptional values, such as a
jump in capture quantity in 1995 for seabass and in 1994 for seabream; and
– the deterministic trend T.
In this model the author applied the London School of Economics (LSE) method: “from the
general to the particular.” It implies the following steps:
(i)
Estimate the general model and perform a diagnostic test on the parameters.
(ii)
Delete from the general model the not-significant parameters and estimate the
resultant model.
(iii)
On the parameters of the resultant model, impose the nine restrictions1 suggested with
the LSE method in order to identify the appropriate model.
1
.
For the nine restrictions, see Section 2.
Section 4 – The econometric model
10
THE SEABASS CASE
ADL (4,4) Model estimate
The ADL (4,4) Model is the following:
LC = 0 + 1LC(-1) + 2LC(-2) + 3LC(-3) + 4LC(-4) + 5LA + 6LA(-1) +
7LA(-2) + 8LA(-3) + 9LA(-4) + 10T + 11D2 + 12D3 + 13D4 + 14DU95
LS // Dependent variable is LC
Sample: 1992:1 to 1998:4
No. of observations: 28 after adjusting endpoints
Variable
Coefficient
SE
T-statistic
10.81816
1.245899
8.683012
0.0000
LC(-1)
-0.066298
0.136506
-0.485681
0.6353
LC(-2)
0.034547
0.153295
0.225365
0.8252
LC(-3)
-0.161272
0.145304
-1.109891
0.2872
LC(-4)
C
Probability
-0.184459
0.112594
-1.638267
0.1253
LA
0.058115
0.038667
1.502985
0.1567
LA(-1)
0.042316
0.042441
0.997041
0.3369
LA(-2)
0.107670
0.041425
2.599127
0.0220
LA(-3)
0.082267
0.041033
2.004876
0.0663
LA(-4)
0.073076
0.040565
1.801455
0.0949
T
0.009456
0.000915
10.32987
0.0000
D2
0.034853
0.009978
3.493140
0.0040
D3
0.043271
0.012884
3.358601
0.0051
D4
0.050324
0.011222
4.484355
0.0006
-0.040752
0.004981
-8.180987
0.0000
DU95
R-squared
0.979524
Mean dependent var
10.57359
Adjusted R-squared
0.957473
S.D. dependent var
0.034663
S.E. of regression
0.007148
Log likelihood
109.3562
Sum squared residual
0.000664
F-statistic
44.42031
Durbin-Watson stat
2.046943
Prob(F-statistic)
0.000000
Breusch-Godfrey Serial Correlation LM Test (4 lags)
F-statistic
0.406406
Probability
0.799810
ARCH heteroscedasticity test (4 lags)
F-statistic
0.839499
Probability
0.517113
The goodness of fit can be tested by the following graphs:
10.65
10.60
0.015
0.010
10.55
0.005
10.50
0.010
10.45
0.005
0.000
-0.005
0.000
-0.010
-0.005
-0.010
-0.015
92
93
94
Residual
95
96
Actual
97
Fitted
98
96:1
96:3
97:1
97:3
Recursive Residuals
98:1
98:3
± 2 S.E.
Price interaction between aquaculture and fishery
11
1.5
15
10
1.0
5
0.5
0
-5
0.0
-10
-0.5
-15
96:1
96:3
97:1
CUSUM
97:3
98:1
5% Significance
98:3
96:1
96:3
97:1
CUSUM of Squares
97:3
98:1
98:3
5% Significance
Section 4 – The econometric model
12
The ADL (4,4) model gives a good interpolation of the data, in terms of R 2 (0.979524) and
adjusted R2 (0.957473). This means the variation of LC is explained at 97.98% from the
regression. Based on the T-statistic only the constant, the parameter LA(-2), as well as the trend
and the dummy variables are significant at 5%. This means that the variation of the dependent
variable LC could be determined from: the farmed prices at time t-2 (LA(-2)); from the
deterministic trend; as well as from the seasonal component and from exceptional values, like a
jump in the captured quantities.
The stochastic components are not autocorrelated. That means the residual components for
different periods (in this case, quarters) are not correlated.
There is no heteroscedasticity in the model.
Based on the recursive residuals test, the model results are stable, confirmed by Cusum and
the Cusum of Squares test.
Analysis of the dynamic structure of the ADL (4,4) model: Redundant Variable test
This test can help you determine whether a subset of variables in an equation have zero
coefficients and might thus be deleted from the equation.
Redundant variables: LC(-1), LC(-2), LC(-3) and LC(-4)
F-statistic = 3.506462; Probability = 0.037573
 The variables are significant.
Redundant variables: LA, LA(-1), LA(-2), LA(-3) and LA(-4).
F-statistic = 19.76253; Probability = 0.000012
 The variables are significant.
Redundant variables D2, D3 and D4
F-statistic = 9.073950; Probability = 0.001671
 The variables are significant.
Redundant variables: LC(-4) and LA(-4)
F-statistic = 2.460941; Probability = 0.1240062
 The variables are not significant.
Redundant variables: LC(-3) and LA(-3)
F-statistic = 2.060766; Probability = 0.166957
 The variables are not significant.
Redundant variables: LC(-2) and LA(-2)
F-statistic = 3.649222; Probability = 0.055224
 The variables are significant.
Redundant variables: LC(-1) and LA(-1)
F-statistic = 0.507506; Probability = 0.613443
 The variables are not significant.
Based on these results we can delete from the ADL (4,4) model the not-significant variables
and estimate the reduced model.
Reduced Model estimate
The reduced model is:
LC = 0 + 1LC(-2) + 2LA + 3LA(-2) + 4T + 5D2 + 6D3 + 7D4 + 8DU95
LS // Dependent variable is LC
Price interaction between aquaculture and fishery
13
Sample: 1991:3 to 1998:4
No. of observations: 30, after adjusting endpoints
Variable
Coefficient
C
SEr
T-statistic
Probability
7.803955
1.170543
6.666951
0.0000
-0.040780
0.136988
-0.297690
0.7689
LA
0.095512
0.046915
2.035856
0.0546
LA(-2)
0.215167
0.043197
4.981092
0.0001
T
0.007928
0.001078
7.357522
0.0000
D2
0.039113
0.007454
5.247308
0.0000
D3
0.044127
0.011199
3.940162
0.0007
0.049559
0.006666
7.434682
0.0000
-0.035383
0.006440
-5.494616
0.0000
LC(-2)
D4
DU95
R-squared
0.924231
Mean dependent var
10.57559
Adjusted R-squared
0.895367
S.D. dependent var
0.034296
SE of regression
0.011094
Log likelihood
97.82278
Sum squared
residual
0.002585
F-statistic
32.01975
Durbin-Watson stat.
1.600424
Probability (F-statistic)
0.000000
Breusch-Godfrey Serial Correlation LM Test (4 lags)
F-statistic
2.376307
F-statistic
2.103784
Probability
0.092846
White heteroscedasticity test
Probability
0.077845
ARCH heteroscedasticity test (4 lags):
F-statistic
2.155201
Probability
10.65
0.03
10.60
0.02
10.55
0.109553
0.01
0.03
10.50
0.02
0.01
0.00
10.45
-0.01
0.00
-0.01
-0.02
-0.02
-0.03
-0.03
92
93
94
Residual
95
96
Actual
97
98
95:3
Fitted
96:1
96:3
97:1
97:3
Recursive Residuals
98:1
98:3
± 2 S.E.
1.6
15
10
1.2
5
0.8
0
0.4
-5
0.0
-10
-15
-0.4
95:3
96:1
96:3
CUSUM
97:1
97:3
98:1
5% Significance
98:3
95:3
96:1
96:3
97:1
CUSUM of Squares
97:3
98:1
98:3
5% Significance
14
Section 4 – The econometric model
Price interaction between aquaculture and fishery
15
The reduced model also gives a good interpolation of data, in terms of R2 (0.924231) and
adjusted R2 (0.895367). This means that the variation of LC derives 92.42% from the regression.
These results are inferior in comparison to those from the model ADL (4,4), indicating that the
ADL (4,4) reduced model involves a worsening of the regression. Based on T-statistics, only the
constant, the parameter LA(-2), and the trend and the dummy variables are significant at 5%. This
means that the variation of the dependent variable LC could be determined from: the farmed
prices at time t-2 (LA(-2)); from the deterministic trend; as well as from the seasonal component
and from exceptional events like an increase in the capture quantities.
The stochastic components are not autocorrelated. That means the residual components
relative to different periods (in this case, quarters) are not correlated.
There is no heteroscedasticity in the model.
Based on the recursive residuals test, the model is unstable; based on Cusum and the Cusum
of Squares test, it is stable.
Restrictions of the reduced model
The nine restrictions are tested by the Wald test.
The Wald test deals with hypotheses involving restrictions on the coefficients of the
explanatory variables. The restrictions may be linear or non-linear, and two or more restrictions
may be tested jointly. Output from the Wald test depends on the linearity of the restriction. For
linear restrictions, the output is an F-statistic and a chi-squared statistic with associated p-values.
Accepting the Null hypothesis of the test implies the possibility of estimating the resultant
model.
(i)
Wald test for the static model
F-statistic = 13.71840; Probability = 0.000155
Chi-square = 27.43680; Probability = 0.000001
H 0 : 1   3  0

H A : 1 ,  3 ,  0
(ii)
Wald test for the AR 1 model
F-statistic = 26.38602; Probability = 0.000002
Chi-square = 52.77205; Probability = 0.000000
H 0 :  2   3  0

H A :  2 ,  3 ,  0
(iii)
Wald test for the tendency indicator model
F-statistic = 2.162065; Probability = 0.140020
Chi-square = 4.324129; Probability = 0.115087
H 0 : 1   2  0

H A : 1 ,  2  0
(iv)
Wald test for the growth rates model
F-statistic = 31.27552; Probability = 0.000001
Chi-square = 62.55105; Probability = 0.000000
 H 0 : 1  1,  3    2

 H A : 1  1,  3    2
(v)
Wald test for the distributed lags model
F-statistic = 0.088620; Probability = 0.768865
Chi-square = 0.088620; Probability = 0.765939
H 0 : 1  0

H A : 1  0
(vi)
Wald test for the partial adjustments model
F-statistic = 24.81128; Probability = 0.000063
Chi-square = 24.81128; Probability = 0.000001
H 0 :  3  0

H A :  3  0
(vii)
Wald test for the restriction to common factor model
F-statistic = 25.77946; Probability = 0.000050
Chi-square = 25.77946; Probability = 0.000000
H 0 :  3   1 *  2

H A :  3   1 *  2
(viii)
Wald test for the deferred model
F-statistic = 4.144712; Probability = 0.054575
Chi-square = 4.144712; Probability = 0.041765
H 0 :  2  0

H A :  2  0
Section 4 – The econometric model
16
Based on these results, we accept the Null hypothesis of the test only for case (v), the
distributed lag model. That implies the possibility of estimating the resultant model.
Distributed Lag Model Estimate
The model is the following:
LC = 0 + 1LA(-2) + 2T + 3D2 + 4D3 + 5D4 + 6DU95
LS // Dependent variable is LC
Sample: 1991:3 to 1998:4
No. of observations: 30, after adjusting endpoints
Variable
Coefficient
SE
T-statistic
C
7.771893
0.294993
26.34606
0.0000
LA(-2)
0.270152
0.029308
9.217677
0.0000
T
0.007155
0.000664
10.78206
0.0000
D2
0.047476
0.006389
7.431041
0.0000
D3
0.059942
0.006380
9.395743
0.0000
0.055470
0.006125
9.056476
0.0000
-0.030688
0.004364
-7.032540
0.0000
D4
DU95
Probability
R-squared
0.908629
Mean dependent var
10.57559
Adjusted R-squared
0.884793
S.D. dependent var
0.034296
SE of regression
0.011641
F-statistic
38.12029
Sum squared residual
0.003117
Probability (F-statistic)
0.000000
Log likelihood
95.01426
Durbin-Watson stat
1.645804
Breusch-Godfrey Serial Correlation LM Test (4 lags)
F-statistic
2.580668
Probability
0.070413
White heteroscedasticity test
F-statistic
1.720894
Probability
0.151939
ARCH heteroscedasticity test (4 lags)
F-statistic
4.296370
Probability
0.010745
The goodness of fit can be tested by the following graphs:
10.65
0.04
10.60
0.03
10.55
0.02
10.50
0.01
0.03
0.02
0.01
10.45
0.00
0.00
-0.01
-0.01
-0.02
-0.02
-0.03
-0.03
92
93
94
Residual
95
96
Actual
97
Fitted
98
95:3
96:1
96:3
97:1
97:3
Recursive Residuals
98:1
98:3
± 2 S.E.
Price interaction between aquaculture and fishery
17
1.6
15
10
1.2
5
0.8
0
0.4
-5
0.0
-10
-0.4
-15
95:3
96:1
96:3
CUSUM
97:1
97:3
98:1
5% Significance
98:3
95:3
96:1
96:3
97:1
CUSUM of Squares
97:3
98:1
98:3
5% Significance
Section 4 – The econometric model
18
The model also gives a good interpolation of data, in terms of R2 (0.908629) and adjusted
R (0.884793). This means that the variation of LC derives 90.86% from the regression. These
results are inferior to those from the reduced model, so the reduction of the reduced model in the
distributed lag model involves a worsening of the regression. Based on T-statistics, only the
constant, the parameter LA(-2), as well as the trend and the dummy variables are significant at
5%. This means that the variation of the dependent variable LC could be determined from: the
farmed prices at time t-2 (LA(-2)); from the deterministic trend; as well as from the seasonal
component and from exceptional events like a increase in the captured quantities.
2
The stochastic components are not autocorrelated. That means the residual components
relative to different periods (in this case, quarters) are not correlated.
There is no heteroscedasticity in the model.
Based on a recursive residuals test, the model results are unstable, and similarly based on a
Cusum test. Based on the Cusum of Squares test, the results are stable.
Co-integration Analysis between LC and LA
A group of non-stationary time series data is co-integrated if there is a linear combination of them
that is stationary, i.e. the combination does not have a stochastic trend. The linear combination is
called the Co-integrating Equation. Its normal interpretation is as a long-run equilibrium
relationship.
A non-stationary time series can often be made stationary if it is differenced once or several
times. If a time series is non-stationary but its first difference is stationary, it is said to have a unit
root and to be integrated of order one (I(1)). A time series that has to be differenced d times
before it becomes stationary is integrated of order d (I(1)).
The order of a time series can be tested by an Augmented Dickey Fuller (ADF) test. The
ADF test involves running a regression of the first difference of the series against the series
lagged once, lagged difference terms, and, optionally, a constant and a time trend.
The test for a unit root is a test on the coefficient of the lagged dependent variable in the
regression. If the coefficient is significantly different from zero, the hypothesis that y contains a
unit root is rejected and the hypothesis is accepted that y is stationary rather than integrated.
The output of the ADF test consists of the T-statistic of the Coefficient of the lagged test
variable and critical values for the test of a zero coefficient.
If the Dickey-Fuller T-statistic is smaller (in absolute value) than the reported critical
values, you cannot reject the hypothesis of non-stationarity and the existence of a unit root. You
would conclude that your series may not be stationary. You may then wish to test whether the
series is I(1) (integrated of order one) or integrated of a higher order. A series is I(1) if its first
difference does not contain a unit root. You can repeat the ADF test on the first difference of your
series to test the hypothesis of integration of order 1 against higher orders.
You can repeat the test on second differences if you find that the first difference may be
non-stationary.
ADF test on LC
ADF Test Statistic = -3.322718; 1% Critical Value* = -3.6576
Since ADF Test Statistic > % Critical Value*, then LC is integrated.
ADF test on LC
ADF Test Statistic = -8.820744; 1% Critical Value* = -2.6423
Since ADF Test Statistic < 1% Critical Value*, then LC is not integrated, LC is I(1).
*
MacKinnon critical values for rejection of hypothesis of a unit root.
Price interaction between aquaculture and fishery
19
ADF test on LA
ADF Test Statistic = -1.050449; 1% Critical Value* = -2.6395
Since ADF Test Statistic > 1% Critical Value*, then LA is integrated.
ADF test on the LA
ADF Test Statistic = -6.421043; 1% Critical Value* = -2.6423
Since ADF Test Statistic < 1% Critical Value*, then LA is not integrated, LA is I(1).
ADF test on the RESID series (the RESID series represents the residuals of the static regression
of LC on LA)
ADF Test Statistic = -3.326980; 1% Critical Value* = -2.6395
Since ADF Test Statistic < 1% Critical Value*, then RESID is not integrated.
the RESID is a linear combination between LC and LA.
That means that RESID is I(0) and implies that LC and LA are co-integrated.
Error Correction Model estimate
Since LC and LA are co-integrated, we can estimate the ECM, using the following model:
LC = 0 + 1LA + 2ECM(-1) + 3T + 4D2 + 5D3 + 6D4 + 7DU95
LS // dependent variable is LC
Sample: 1991:2 to 1998:4
No. of observations: 31, after adjusting endpoints
Variable
C
Coefficient
SE
T-Statistic
Probability
-0.058882
0.014016
-4.201170
0.0003
0.074322
0.065475
1.135125
0.2680
-0.097751
0.252552
-0.387053
0.7023
T
0.000534
0.000747
0.714531
0.4821
D2
0.084059
0.011174
7.522743
0.0000
D3
0.054003
0.010335
5.225375
0.0000
LA
ECM(-1)
D4
0.069282
0.009596
7.219618
0.0000
DU95
-0.002286
0.006805
-0.335955
0.7400
R-squared
0.817426
Mean dependent var
0.002693
Adjusted R-squared
0.761859
S.D. dependent var
0.037023
S.E. of regression
0.018067
F-statistic
14.71086
Sum squared resid
0.007508
Prob(F-statistic)
Log likelihood
85.06289
Durbin-Watson stat
2.168804
0.000000
Breusch-Godfrey Serial Correlation LM Test (4 lags)
F-statistic
0.645325
Probability
0.636912
White heteroscedasticity test
F-statistic
2.069333
F-statistic
0.151918
Probability
0.079810
ARCH heteroscedasticity test (4 lags)
Probability
0.960128
Ramsey RESET Test (4 lags)
F-statistic
1.001378
Probability
0.431187
Ramsey RESET Test (3 lags)
F-statistic
0.019303
F-statistic
0.027934
Probability
0.996232
Ramsey RESET Test (2 lags)
Probability
0.972489
Section 4 – The econometric model
20
The goodness of fit can be tested by the following graphs:
0.10
0.06
0.05
0.04
0.00
-0.05
0.04
-0.10
0.02
0.02
0.00
-0.15
0.00
-0.02
-0.02
-0.04
-0.04
-0.06
-0.08
-0.06
92
93
94
95
Residual
96
Actual
97
98
95:3
Fitted
96:1
96:3
97:1
97:3
Recursive Residuals
98:1
98:3
± 2 S.E.
1.6
15
10
1.2
5
0.8
0
0.4
-5
0.0
-10
-0.4
-15
95:3
96:1
96:3
CUSUM
97:1
97:3
98:1
5% Significance
98:3
95:3
96:1
96:3
97:1
CUSUM of Squares
97:3
98:1
98:3
5% Significance
Price interaction between aquaculture and fishery
21
Section 4 – The econometric model
22
The model gives a good interpolation of data, in terms of R2 (0.817426) and adjusted R2
(0.761859). This means that the variation of LC derives 81.74% from the regression. These
results are inferior to those from the ADL (4,4) model. Thus the reduction of the reduced model
in the distributed lag model involves a worsening of the regression.
Base on the statistic T alone, the constant and the dummies variables are significant. This
means that the variation of the dependent variable LC could be determined only from the
seasonality. The variable LA is not significant and that means that the short-run adjustment
parameter has no influence on the variation of the dependent variable. The variable ECM(-1) is
also not significant, so the long-run adjustment parameter has no influence on the variation of the
dependent variable.
The stochastic components are not autocorrelated. That means the residual components
relative to different periods (in this case, quarters) are not correlated.
There is no heteroscedasticity in the model.
Based on the Ramsey Reset test, the model is correctly specified.
On the base of the recursive residuals test, the model results are unstable; similarly from
Cusum and Cusum of Squares test.
Granger Causality Test
The Granger approach to the question of whether X causes Y is to see how much of the current Y
can be explained by past values of Y, and then to see whether adding lagged values of X can
improve the explanation. Y is said to be Granger-caused by X if X helps in the prediction of Y, or
equivalently if the coefficients of the lagged Xs are statistically significant.
Pairwise Granger Causality Tests
Sample: 1991:1 to 1998:4
Lags: 4
Null Hypothesis
Observations
[1]
LA does not Granger Cause LC
28
[2]
LC does not Granger Cause LA
F-Statistic
Probability
0.61456
0.65737
1.54295
0.23005
[1] Since F-statistic = 0.61456 < F0.058,19 = 2.48, then LA does not Granger Cause LC
[2] Since F-statistic = 1.54295 < F0.058,19 = 2.48, then LC does not Granger Cause LA
Comments and conclusions on the Seabass case
Any of the estimated models gives satisfactory results. Not all the parameters of the estimated
models are significant and in some case the models are not stable and correctly specified.
Although there is a co-integration relationship between the LC and LA series, the ECM model
does not furnish satisfactory results: the short-run adjustment parameter, LA, has no influence
on the variation of the dependent variable. The ECM(-1) variable is also not significant, so the
lung-run adjustment parameter has no influence on the variation of the dependent variable.
These results mean that the two different product forms – captured seabass and farmed
seabass – are not substitutes for each other and there is no link between their prices. Therefore a
long-run change in price of one product has no impact on the long-run price of the other product.
The Granger causality test confirms this results. In both cases, in fact, we accept the null
hypothesis: LA does not Granger Cause LC in case [1] and LC does not Granger Cause LA in
case [2].
Price interaction between aquaculture and fishery
23
THE SEABREAM CASE
ADL (4,4) Model estimate
The ADL (4,4) Model is the following:
LC = 0 + 1LC(-1) + 2LC(-2) + 3LC(-3) + 4LC(-4) + 5LA + 6LA(-1) + 7LA(-2) +
8LA(-3) + 9LA(-4) + 10T + 11D2T + 12D3T + 13D4T + 14DU94
LS // dependent variable is LC
Sample: 1992:1 to 1998:4
No. of observations: 28, after adjusting endpoints
Variable
Coefficient
SE
T-Statistic
Probability
C
2.267910
3.768348
0.601831
0.5576
LC(-1)
0.098210
0.139733
0.702842
0.4945
LC(-2)
0.193248
0.144248
1.339694
0.2033
LC(-3)
-0.241864
0.145356
-1.663942
0.1200
LC(-4)
0.267353
0.164226
1.627961
0.1275
LA
0.278185
0.110766
2.511457
0.0260
LA(-1)
-0.008705
0.124759
-0.069776
0.9454
LA(-2)
-0.100967
0.128454
-0.786015
0.4459
LA(-3)
-0.046655
0.123526
-0.377692
0.7118
LA(-4)
0.353730
0.111651
3.168173
0.0074
T
0.013865
0.003310
4.189143
0.0011
D2T
-0.004188
0.002089
-2.005172
0.0662
D3T
-0.002600
0.001791
-1.451676
0.1703
D4T
0.001823
0.001747
1.043843
0.3156
-0.084154
0.033863
-2.485116
0.0273
DU94
R-squared
0.922137
Mean dependent var.
10.28433
Adjusted R-squared
0.838284
S.D. dependent var.
0.065112
SE of regression
0.026184
Sum squared resid.
0.008913
Log likelihood
73.00417
F-statistic
10.99708
Durbin-Watson stat.
1.743384
Probability (F-statistic)
0.000052
Breusch-Godfrey Serial Correlation LM Test (4 lags)
F-statistic
0.828567
Probability
0.539346
ARCH Test ( 4 lags)
F-statistic
2.379440
Probability
0.088057
The goodness of fit can be tested by the following graphs:
10.5
0.10
10.4
10.3
0.04
10.2
0.02
10.1
0.05
0.00
0.00
-0.05
-0.02
-0.04
-0.06
-0.10
92
93
94
Residual
95
96
Actual
97
98
Fitted
96:1
96:3
97:1
97:3
Recursive Residuals
98:1
98:3
± 2 S.E.
Section 4 – The econometric model
24
1.5
15
10
1.0
5
0.5
0
-5
0.0
-10
-0.5
-15
96:1
96:3
97:1
CUSUM
97:3
98:1
5% Significance
98:3
96:1
96:3
97:1
CUSUM of Squares
97:3
98:1
98:3
5% Significance
Price interaction between aquaculture and fishery
25
The ADL (4,4) model gives a good interpolation of data, is in terms of R2 (0.922137) and
adjusted R2 (0.838284). This means the variation of LP0C is explained at 92.21% from the
regression. Base on the statistic T, the parameters LA and LC(-4), as well as the trend and the
dummy variable DU94, are significant at 5%. This means that the variation of the dependent
variable LC could be determined from: the farmed prices at time t and at time t-4 (LA and LA(4)); from the deterministic trend; as well as from the seasonal component and from exceptional
events like an increase in the captured quantities.
The stochastic components are not autocorrelated. That means the residual components
relative to different periods (in this case, quarters) are not correlated.
There is no heteroscedasticity in the model.
Based on the recursive residuals test, the model is stable, confirmed by the Cusum test.
Based on Cusum of Squares test results, it is unstable.
Analysis of the dynamic structure of the ADL (4,4) model: Redundant variable test
This test can help you determine whether a subset of variables in an equation have zero
coefficients and could thus be deleted from the equation.
Redundant variables: LC(-1), LC(-2), LC(-3) and LC(-4)
F-statistic = 1.834613; Probability = 0.182390
The variables are significant.
Redundant variables: LA, LA(-1), LA(-2), LA(-3) and LA(-4)
F-statistic = 3.591426; Probability = 0.029284
The variables are significant.
Redundant variables: D2T, D3T and D4T
F-statistic = 3.878624; Probability = 0.035059
The variables are significant.
Redundant variables: LC(-4) and LA(-4)
F-statistic = 6.480220; Probability = 0.011158
The variables are not significant.
Redundant variables: LC(-3) and LA(-3)
F-statistic = 1.419719; Probability = 0.276900
The variables are not significant.
Redundant variables: LC(-2) and LA(-2)
F-statistic = 1.425754; Probability = 0.275533
The variables are not significant.
Redundant variables: LC(-1) and LA(-1)
F-statistic = 0.251064; Probability = 0.781659
The variables are not significant.
On the basis of these results we can delete from the ADL (4,4) model the not-significant
variables and estimate the reduced model.
Reduced Model estimate
The reduced model is the following:
LC = 0 + 1LC(-4) + 2LA + 3LA(-4) + 4T + 5D2T + 6D3T + 7D4T + 8DU94
LS // dependent variable is LC
Section 4 – The econometric model
26
Sample: 1992:1 to 1998:4
No. of observations: 28, after adjusting endpoints
Variable
Coefficient
SE
T-Statistic
Probability
C
1.838552
2.130271
0.863060
0.3989
LC(-4)
0.343826
0.142974
2.404822
0.0265
LA
0.232601
0.078135
2.976925
0.0077
LA(-4)
0.258044
0.080913
3.189142
0.0048
T
0.013535
0.002919
4.637063
0.0002
D2T
-0.002869
0.001437
-1.997081
0.0603
D3T
-0.002471
0.001511
-1.635629
0.1184
D4T
0.002142
0.001186
1.806741
0.0867
-0.075700
0.022680
-3.337685
0.0035
DU94
R-squared
0.889521
Mean dependent var
10.28433
Adjusted R-squared
0.843003
S.D. dependent var
0.065112
SE of regression
0.025799
F-statistic
Sum squared resid
0.012646
Probability (F-statistic)
Log likelihood
68.10594
Durbin-Watson stat.
1.592515
9.12221
0.000000
Breusch-Godfrey Serial Correlation LM Test (4 lags)
F-statistic
1.552114
Probability
0.237907
White heteroscedasticity test
F-statistic
2.383313
Probability
0.063325
ARCH heteroscedasticity test (4 lags)
F-statistic
2.001892
Probability
0.135124
The goodness of fit can be tested by the following graphs:
10.5
0.10
10.4
0.05
0.10
10.3
0.05
10.2
0.00
10.1
0.00
-0.05
-0.05
-0.10
-0.10
92
93
94
Residual
95
96
Actual
97
98
94:3 95:1 95:3 96:1 96:3 97:1 97:3 98:1 98:3
Fitted
Recursive Residuals
± 2 S.E.
1.6
15
10
1.2
5
0.8
0
0.4
-5
0.0
-10
-0.4
-15
94:3 95:1 95:3 96:1 96:3 97:1 97:3 98:1 98:3
CUSUM
5% Significance
94:3 95:1 95:3 96:1 96:3 97:1 97:3 98:1 98:3
CUSUM of Squares
5% Significance
Price interaction between aquaculture and fishery
27
The reduced model gives also a good interpolation of data, in terms of R 2 (0.889521) and
adjusted R2 (0.843003). This means that the variation of LC derives 88.95% from the regression.
These results are inferior to those from the model ADL (4,4), so the reduction of the model ADL
(4,4) in the reduced model involves a worsening of the regression. Based on the statistic T, only
the constant, the parameter LC(-4), LA and LA(-4), as well as the dummy variable, are significant
at 5%. This means that the variation of the dependent Variable LC could be determined from: the
captured price at time t-4 (LA(-4)); from the farmed price at time t and at time t-4 (LA, LA(-4));
and from exceptional events like an increase in the captured quantities.
The stochastic components are not autocorrelated. That means the residual components
relative to different periods (in this case, quarters) are not correlated.
There is no heteroscedasticity in the model.
Based on the recursive residuals test, the model is stable, as well as based on the Cusum
test. Based on the Cusum of Squares test, it is unstable.
Section 4 – The econometric model
28
Restrictions on the reduced model
The nine restrictions are tested by the Wald test.
(i)
Wald test for the static model
F-statistic = 7.737048; Probability = 0.003483
Chi-squared = 15.47410; Probability = 0.000436
H 0 : 1   3  0

H A : 1 ,  3 ,  0
(ii)
Wald test for the AR 1 model
F-statistic = 6.924188; Probability = 0.005512
Chi-square = 13.84838; Probability = 0.000984
H 0 :  2   3  0

H A :  2 ,  3 ,  0
(iii)
Wald test for the tendency indicator model
F-statistic = 5.884440; Probability = 0.010259
Chi-square = 11.76888; Probability = 0.002782
 H 0 : 1   2  0

 H A : 1 ,  2  0
(iv)
Wald test for the rates of growth model
F-statistic = 20.95566; Probability = 0.000016
Chi-square = 41.91132; Probability = 0.000000
 H 0 : 1  1,  3    2

 H A : 1  1,  3    2
(v)
Wald test for distributed lags model
F-statistic = 5.783171; Probability = 0.026537
Chi-square = 5.783171; Probability = 0.016180
H 0 : 1  0

H A : 1  0
(vi)
Wald test for the partial adjustments model
F-statistic = 10.17063; Probability = 0.004830
Chi-square = 10.17063; Probability = 0.001427
H 0 :  3  0

H A :  3  0
(vii)
Wald test for the restriction to common factor model
F-statistic = 10.74012; Probability = 0.003964
Chi-square = 10.74012; Probability = 0.001048
H 0 :  3   1 *  2

H A :  3   1 *  2
(viii)
Wald test for the deferred model
F-statistic = 8.862081; Probability = 0.007747
Chi-square = 8.862081; Probability = 0.002912
Based on these results, we reject the null hypothesis of each
impossibility of estimating the resultant model.
H 0 :  2  0

H A :  2  0
test.
That implies the
Co-integration analysis
For discussion of the concepts of unit root and co-integration, see relevant section.
* indicates MacKinnon critical values for rejection of hypothesis of a unit root.
ADF test on LC
ADF Test Statistic = -4.465239; 1% Critical Value = -3.6576
Since ADF Test Statistic < 1% Critical Value*, then LC is not integrated.
ADF test on LA
ADF Test Statistic = -6.237515; 1% CriticalValue* = -4.2949
Since ADF Test Statistic < 1% CriticalValue*, then LA is not integrated.
Since the LA and LC series are both stationary, a co-integration relationship does not exist.
In this case we can estimate the model at first difference.
First Difference Model estimate
The model is: LC = 0 + 1LA + 2T + 3D2T + 4D3T + 5D4T + 6DU94
LS // dependent variable is LC
Price interaction between aquaculture and fishery
29
Sample: 1991:2 to 1998:4
No. of observations: 31, after adjusting endpoints
Variable
Coefficient
SE
T-Statistic
Probability
C
-0.005883
0.023717
-0.248037
0.8062
LA
0.079551
0.154230
0.515793
0.6107
T
-0.005184
0.002737
-1.894129
0.0703
D2T
0.007550
0.002576
2.931189
0.0073
D3T
0.007033
0.001735
4.052618
0.0005
D4T
0.009438
0.001707
5.529507
0.0000
DU94
-0.010827
0.041434
-0.261317
0.7961
R-squared
0.625758
Mean dependent var.
Adjusted R-squared
0.532197
S.D. dependent var.
0.003370
0.087854
SE of regression
0.060089
Sum squared resid.
0.086657
Log likelihood
47.14965
F-statistic
6.688269
Durbin-Watson stat.
2.977549
Probability (F-statistic)
0.000295
Breusch-Godfrey Serial Correlation LM Test (4 lags)
F-statistic
3.913448
Probability
0.016629
Breusch-Godfrey Serial Correlation LM Test (1 lag)
F-statistic
7.526868
Probability
0.011574
White heteroscedasticity test
F-statistic
0.882398
F-statistic
8.053418
Probability
0.571470
ARCH heteroscedasticity test (4 lags)
Probability
0.000370
ARCH heteroscedasticity test (1 lag)
F-statistic
8.092822
Probability
0.008216
Ramsey RESET Test (4 lags)
F-statistic
5.393020
Probability
0.004111
Ramsey RESET Test (3 lags)
F-statistic
7.120685
F-statistic
6.179006
Probability
0.001766
Ramsey RESET Test (2 lags)
Probability
0.007419
The goodness of fit can be tested by the following graphs:
0.2
0.2
0.1
0.0
0.1
-0.1
0.2
-0.2
0.1
0.0
-0.3
0.0
-0.1
-0.1
-0.2
-0.2
92
93
94
Residual
95
96
Actual
97
Fitted
98
94:3 95:1 95:3 96:1 96:3 97:1 97:3 98:1 98:3
Recursive Residuals
± 2 S.E.
Section 4 – The econometric model
30
1.6
15
10
1.2
5
0.8
0
0.4
-5
0.0
-10
-0.4
-15
94:3 95:1 95:3 96:1 96:3 97:1 97:3 98:1 98:3
CUSUM
5% Significance
94:3 95:1 95:3 96:1 96:3 97:1 97:3 98:1 98:3
CUSUM of Squares
5% Significance
Price interaction between aquaculture and fishery
31
The first differences model doesn’t give a good interpolation of data in terms of R 2
(0.625758) and adjusted R2 (0.532197). This means that the variation of LC derives 62.57%
from the regression.
Based on the T-statistic, only the dummy variables are significant. This means that the
variation of the growth rate of LC depends only on the seasonally component.
The stocastic components are not autocorrelated. That means that the residual components
relative to different periods (in this case, quarters) are not correlated.
There is heteroscedasticity in the model.
Based on the Ramsey Reset test, the model is correctly specified.
Based on the recursive residuals test, the model is stable, confirmed by the Cusum and
Cusum of Squares test.
The disadvantage of this model is that by differentiating the variables the long-run
information is lost.
Granger Causality Test
For discussion of the concepts of unit root and co-integration, see relevant section.
Paired Granger Causality Tests
Sample: 1991:1 to 1998:4
Lags: 4
Null Hypothesis:
[1]
LA does not Granger Cause LC
[2]
LC does not Granger Cause LA
Observations
F-Statistic
Probability
28
2.44236
0.08208
1.2686
0.31684
[1] Since F-statistic = 2.44236 < F0.058,19 = 2.48, then LA does not Granger Cause LC
[2] Since F-statistic = 1.26863 < F0.058,19 = 2.48, then LC does not Granger Cause LA
Comments and conclusions on the Seabream case
Any of the estimated models gives satisfactory results. Not all the parameters of the estimated
models are significant and in some case the models are not stable and incorrectly specified. The
series LC and LA are both stationary; this excludes linkage between the two series, and the two
different product forms – captured seabream and farmed seabream – are not substitutes for each
other. Therefore there is no link between their prices, so that a long-run change in one price has
no impact on the long-run price of the other product.
The short-run adjustment parameter – LA – also has no influence on the variation of
dependent variable LC, as shown from the first difference model.
The Granger causality test confirms this results. In both cases, in fact, we accept the null
hypothesis, namely that: LA does not Granger Cause LC in case [1], and LC does not Granger
Cause LA in case [2].
Section 5 – Conclusions
32
SECTION 5
CONCLUSIONS
In this study, the impact of aquaculture on fishing has been analysed using an empirical and
econometric approach.
Based on the estimated models and on the Augmented Dickey-Fuller tests on time series,
we conclude that captured and farmed species are not substitutes for each other with no link
between their prices, and a long-run change in farmed species prices has no impact on the longrun price of captured species. In terms of econometrics, the time series for seabass prices are cointegrated, and a relationship could be found. However, the short- and long-run adjustment
parameters of the ECM model are not significant. The time series for seabream prices is
stationary, and the short-run adjustment parameter of the first difference model is not significant.
The Granger causality test confirms these results. In both cases – seabass and seabream –
neither farmed species price affects the captured species price, and neither captured species price
affects the farmed species price.
These results indicate that there are two separate markets: a market for captured products
and a market for farmed products. The first is characterized by a long-term stable prices; the
second is characterized by a decreasing prices, linked to increasing farmed production and lowcost imports.
The expansion and the success of the farmed product is due to the stability of the supply and
to its low market price. The low price is due to the lower cost of the farmed product imported into
Italy from Greece.
Italy is in fact the target-market of producers of seabass and seabream from the entire
Mediterranean area, and in particular of Greek producers. This situation can be attributed to the
increasing seafood demand in Italy, with growth in imports and increased dependence on foreign
supplies.
The increasing demand has made the Italian market interesting to international suppliers.
These, at the beginning of the 1990s, were attracted by the high prices at that time. As supplies
increased, competition caused a sharp and continuous decrease in farmed seabass and seabream
prices.
In such a context, it is questionable if the farmers can continue to sell their production
simply by reducing prices. Farm-gate prices have for many fishfarmers, especially in Italy where
production costs are higher than in Greece, now reached a break-even point. In the future,
factors such as quality and distribution will increase their importance as competitive parameters.
Also for this reason, capture fisheries is not an activity that should suffer the consequences of the
low price politics of aquaculture, but can in fact benefit from it. Fisheries, in fact, should defend
its market quotas:

by valorizing the quality of the captured product; and

by identifying target buyers willing to pay higher prices for superior quality products.
In fact, members of sensory test panels repeatedly express their preference for the captured
seabass and seabream, commenting that farmed fish are more fatty; softer; and with less taste than
the captured type.
Price interaction between aquaculture and fishery
33
From the estimates presented here, we can conclude that the markets for farmed and
captured fish should be considered separate. In fact, the variables linked differ either in terms of
product characteristics or in terms of market demand.
Finally, the impact of aquaculture on capture fisheries should not be considered as a
substitution process of the farmed product for the captured one. Undoubtedly, the fishing sector is
in a state of crisis, but the causes have to be sought in marine environment degradation and
overexploitation of marine resources.
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34
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