Silviase/jgov_v1.4_full_ann huggingface dataset:
(安全性の確保及び品質の安定化への取組) 47
【事例】養殖生産物のトレーサビリティへの取組
(鹿児島県 東町漁業協同組合) 48
(養殖水産物の品質の進歩とブランド化) 48
(ブランド化の課題) 49
(養殖水産物の輸出の促進) 50
【事例】全国養殖魚輸出振興協議会の発足（(一社)全国海水養魚協会) 50
【事例】完全養殖クロマグロを輸出（熊本県（株)ブリミー・福吉魚類(株)） 51
【コラム】ノルウェーの魚介類輸出促進政策 51
(2) 養殖水産物が無くなったら 51
(3) 養殖水産物への評価 52
(養殖水産物に対する消費者の評価は向上) 52
(価格面でも評価の高まりが表れている) 53
(天然と養殖のイメージと改善が求められている点) 54
第4節 養殖業の持続的発展のために 56
(1) 持続的な供給体制の構築 56
(計画的な養殖生産の必要性) 56
(2) 良好な漁場環境の確保 57
(漁場環境の維持) 57
(沖合養殖) 57
(閉鎖循環式陸上養殖の開発) 57
【コラム】旬をずらして赤潮被害を軽減 58
(3) 天然資源の適切な利用 58
(4) 環境の変化や天然資源の維持のための技術開発 59
(人工種苗への転換) 59
(新たな養魚用餌料の開発) 59
(育種技術による環境悪化や経営改善等への対応) 59
【コラム】育種と遺伝子組換え技術の違い 60
(5) 消費者が求める安全・安心な養殖生産の更なる推進 60
(6) 食料安全保障等への貢献 60

第Ⅱ章 平成24年度以降の我が国水産の動向
第1節 我が国における水産資源の管理 64
(1) 我が国周辺の水産資源の状況 64
【コラム】クジラと他の水産資源との関係 64
(2) 資源評価の実施体制 65
(3) 資源管理の必要性 66
(4) 我が国の漁業管理の特徴 66
(我が国漁業の特徴) 66
(我が国の資源管理の特徴) 67

(5) 我が国における資源管理の具体的実施状況 68
(漁業権漁業) 68
(指定漁業等) 69
(届出漁業等) 69
(漁業調整委員会の役割) 70
(TAC制度) 70
(TACの自主的管理) 71
(IQ制度の導入) 72

(6) 自主的な資源管理とそれに参加する漁業者への支援 73
(7) 資源管理の国際協力 74

第2節 我が国の漁場環境をめぐる動き 75
(1) 漁場環境の保全 75
ア 我が国周辺水域での気候変動と漁業への影響 75
(平成25 (2013)年夏の海水温上昇) 75
(海水温の上昇等によるとみられる漁業の異変) 75
(気候変動の水産業への影響) 76
(気候変動がもたらす影響への対応) 76
【事例】気候変動に適応した漁業をめざして ((独)海洋研究開発機構ほか) 77
イ 漁場環境の変化と復旧 77
(藻場・干潟等の消失による水産資源への影響) 77
【事例】干潟機能の改善のための取組 (大分県 宇佐干潟保全の会) 78
(内水面における水産資源の増殖と漁場管理) 78
【コラム】少雨によるとみられるアユの減少 (淀川水系) 78
(種苗放流による資源造成の推進) 78
【コラム】33回目を迎えた全国豊かな海づくり大会 80
(2) 野生生物等による漁業被害 80
(海面における漁業被害と対応) 80
(内水面における生態系や漁業への被害) 81
(3) 実効ある資源管理のための取組 82
(我が国の沿岸・沖合における漁業関係法令違反等の取締り) 82
(外国漁船の取締り) 83

第3節 我が国水産業をめぐる動き 84
(1) 漁業・養殖業の動向 84
ア 漁業・養殖業の国内生産 84
【コラム】世界の漁業・養殖業生産量の順位の変動 86
イ 漁業経営をめぐる動向 86
(水産物価格の推移) 86
(沿岸漁船漁家の経営状況) 87
(漁船漁業を営む会社経営体の経営状況) 88
(販売金額規模及び経営者の年齢でみた漁業経営体の状況) 89
(燃油の価格動向) 90
(漁業のコスト対策) 91
(省エネルギーへの取組) 92
【事例】人工衛星を活用して燃油を節減
(一社)漁業情報サービスセンター（JAFIC） 93
(漁船漁業の収入安定対策) 93
(漁船の船齢) 94
(漁船漁業の操業体制の転換に向けて) 95
【事例】協業化と漁船の小型化による収益性の改善
(千葉県 銚子市漁業協同組合) 95
【事例】冷凍カツオ・マグロの新しい解凍・保蔵技術の開発
(日本かつお・まぐろ漁業協同組合、(独)水産総合研究センター) 95
【コラム】水産日本の復活に向けて「浜の応援団」 96
ウ漁業に関わる人々をめぐる動向
(漁業就業者数の動向) 96
(新規漁業就業者の状況) 97
【コラム】漁業の担い手を育てる漁業学校 99
(漁業における外国人労働力) 99
(水産業を支える女性の力) 99
【事例】海女さん体験塾（徳島県 伊座利漁業協同組合） 99
【事例】女性部の活動による水産物の高付加価値化
(鹿児島県 奄美漁業協同組合女性部) 100
エ安全な漁業労働環境の確保 100
(漁船海難事故及び海中転落者の状況) 100
(船員及び陸上労働者の災害の状況) 101
(漁業の安全対策) 102
【コラム】衝突事故の防止に有効なAIS（船舶自動識別装置） 103
【事例】海難遺児への支援（(公財)漁船海難遺児育英会） 103
2）漁業協同組合をめぐる動向
(漁業協同組合の役割) 104
【事例】漁師自慢の魚「プライドフィッシュ」プロジェクト
(全国漁業協同組合連合会) 104
(漁業協同組合の経営状況) 104
(漁業協同組合の組織状況) 105
3）水産物の流通・加工をめぐる動向
(水産物流通の特徴と卸売市場の機能) 105
(産地卸売市場の動向) 106
(消費地卸売市場の動向) 107
(水産物の表示) 107
(ホテル・百貨店等における食品表示の不正事案) 107
(水産加工業の特徴と役割) 108
(水産加工業の現状) 109
(水産加工業の課題) 110
【コラム】缶詰の復権 110
(HACCP対応施設の導入状況) 111
(日本国内における水産エコラベル認証の動き) 111
第4節 水産物の消費・需給をめぐる動き 113
(1) 水産物需給の動向 113
(我が国の魚介類の生産・消費構造) 113
(我が国の食用魚介類自給率) 113
(2) 我が国の水産物の輸出入の動向 114
(我が国の水産物輸入の動向) 114
(輸入水産物の安全性等に関する監視体制) 115
(我が国の水産物輸出の動向) 115
(世界における日本産水産物への評価と流通状況) 116
(水産物輸出拡大の必要性と課題) 118
【事例】国産水産物輸出促進のためのキャッチコピー及びロゴマークの策定 (農林水産省) 119
(3) 水産物消費の状況 119
(水産物消費の傾向) 119
(水産物に対する消費者の意識) 120
【事例】モニター試食会による魚食普及の取組 (山梨県 甲府中央魚市(株)) 121
【事例】顧客の魚への親しみを喚起する大朝市 ((株)マルエツ) 121
【事例】魚食普及を目指していち早く条例を制定 (兵庫県美方郡香美町) 122
【事例】女性ならではの魚食普及の取組 (長崎市 魚のまち長崎応援女子会) 122
(消費者の意識や社会情勢の変化を踏まえた販売戦略の必要性) 122
(学校給食等での食育の重要性) 123
【事例】「お魚かたりべ」の活動 125
(官民協働での魚食普及に向けた活動の展開) 125
【事例】魚の国のしあわせFish-1グランプリ 126
(和食と水産物) 127
【コラム】水産物と日本食のユネスコ無形文化遺産「和食」 128
(水産物と健康) 128
【コラム】どれぐらいの量の魚を食べると「健康に良い」のか 130
第5節 水産業をめぐる国際情勢
(1) 世界の漁業・養殖業生産
(世界の漁業・養殖業生産)
(世界の水産資源の状況)
(世界の漁業生産構造)
(2) 世界の水産物消費
(増大する世界の水産物消費)
(3) 世界の水産物貿易
(拡大する世界の水産物貿易)
(各国の水産物輸入の状況)
(国際的な水産物価格の動向)
(4) 我が国の国際漁業関係
アニ国間等の漁業関係
(ロシアとの関係)
(韓国との関係)
(中国との関係)
(台湾との関係)
(その他諸国との関係)
(外国水域における我が国漁船の操業確保の状況)
イ多国間の漁業関係
(太平洋におけるマグロ類の保存管理機関の動向)
(大西洋におけるマグロ類の保存管理機関の動向)
(インド洋におけるマグロ類の保存管理機関の動向)
(みなみまぐろ保存委員会の動向)
(北太平洋公海における地域漁業管理機関の設立)
(捕鯨をめぐる動き)
【コラム】鯨肉がハラール認証を取得
(世界貿易機関 (WTO) の動き)
(環太平洋パートナーシップ (TPP) 協定交渉の動き)
ウ海外漁業協力
(海外漁業協力)
(水産業における外国人技能実習制度)
第6節 安全で活力ある漁村づくり
(1) 浜の活力再生プラン
(2) 水産業・漁村における地域資源の活用
(地域資源を活用した漁村の活性化)
【事例】地の利と地域資源を活用して地域の活性化を図る
(千葉県 保田漁業協同組合)
(漁村地域における6次産業化・地産地消の取組)
【事例】A-FIVEによる「クルマエビの周年販売」への支援（沖縄県 沖縄栽培水産(株)）147

（3）水産業・漁村が発揮する多面的機能147

【コラム】世界遺産「知床」における漁業の役割148

（4）水産業の振興における漁港の役割149

（5）漁村地域における防災機能の強化と減災対策の推進149

（漁村の現状）149

（災害に強い漁業地域づくり）150

【事例】漁業協同組合による災害時の相互支援協定（高知県すくも湾漁業協同組合、島根県漁業協同組合JFしまね）151

（6）水産業・漁村地域における省エネルギーと再生可能エネルギーの活用152

第7節 東日本大震災からの復興に向けた動き153

（1）水産業・漁村の復興状況153

（水産関係施設等の復旧・復興の概況）153

（漁場生産力向上のための技術開発等の実施）157

（「東日本大震災復興特別区域法」に基づく漁業に関する復興特区）157

（被災地での協業化に向けた取組）158

【事例】共同利用施設での養殖（岩手県新おおつち漁業協同組合）158

（新しい地域特産品の開発等に向けた取組）158

（2）原発事故への対応159

（東電福島第一原発の状況）159

【コラム】国の基準値（100ベクレル／kg）の考え方160

（福島県沖での試験操業の状況）161

（福島県及び近隣県での水産物の放射性物質調査）162

（消費者への情報提供の充実）164

【事例】震災後の海を取り巻く状況の紹介165

（福島県いわき市漁業協同組合）165

（海外に向けた取組）165

（漁業者等への賠償）167

水産業・漁村地域の活性化を目指して171

一平成25（2013）年度農林水産祭受賞者事例紹介168

参考図表171

注 本資料に掲載した地図は、必ずしも、我が国の領土を包括的に示すものではありません。
# 第2部 平成25年度 水産施策

# 平成25年度に講じた施策

## 目次

I 概説
1 施策の重点 195
2 財政措置 196
3 税制上の措置 197
4 金融上の措置 197
5 政策評価 197

Ⅱ 東日本大震災からの復興
1 復興の実現に向けた施策とその着実な実施 197
(1) 漁港 197
(2) 漁場・資源 198
(3) 漁船 198
(4) 養殖・栽培漁業 198
(5) 水産加工・水産流通 198
(6) 漁業経営 198
(7) 漁業協同組合 199
(8) 漁村 199

2 東京電力株式会社福島第一原子力発電所事故の影響の克服 199
(1) 水産物の放射性物質調査の徹底による安全な水産物の供給と風評被害の払拭 199
(2) 操業の再開に向けた支援 200
(3) 東電福島第一原発事故で被害を受けた漁業者への賠償 200

Ⅲ 新たな資源管理体制下での水産資源管理の強化
1 我が国の排他的経済水域における資源管理の強化 200
(1) 資源管理指針・資源管理計画による資源管理の推進 200
(2) 種苗放流による資源造成の推進 201
(3) 遊漁者の資源管理に対する取組の促進 201
(4) 漁業許可制度等の適切な運用 201
(5) 資源管理のルールの遵守を担保する仕組みの推進 201
2 国際的な資源管理の推進 202
（1）我が国周辺国等との連携・協力による資源管理の推進 202
（2）公海域等における資源管理の推進と海外漁場の確保 202
3 資源に関する調査研究の充実 203
4 環境負荷の少ない持続的な養殖業の確立 203
（1）漁場改善計画の着実な実行と人工種苗への転換の推進 203
（2）赤潮対策等の実施 203
（3）疾病対策の実施 203
5 多様な海洋生物の共存下での漁業の発展の確保 204
（1）水産資源を育成する藻場・干潟等の適切な管理 204
（2）漂流・漂着物対策の実施 204
（3）大型クラゲ、トド等による漁業被害防止対策の推進 204
（4）生物多様性に配慮した海洋生物資源の保存・管理の推進 205

IV 意欲ある漁業者の経営安定の実現

1 資源管理・漁業経営安定対策による漁業経営の安定の確保 205
2 漁業保険制度の適切な運営 205

V 多様な経営発展による活力ある生産構造の確立

1 国際競争力のある経営体の育成に向けた漁業経営の体質強化 206
2 漁業の高付加価値化等の推進 206
3 融資・信用保証による経営支援の的確実施 206
4 担い手の確保・人材育成と女性の参画の促進 207
（1）担い手の確保 207
（2）人材の育成 207
（3）女性の参画の促進 207
5 沖縄漁業基金の創設 208

VI 漁船漁業の安全対策の強化 208

VII 水産物の消費拡大と加工・流通業の持続的発展による安全な水産物の安定供給

1 消費者への情報提供の充実 209
2 魚食普及の推進 209
3 水産物流通の品質・衛生管理対策の推進 209
（1）漁港における品質・衛生管理対策の推進 210
（2）水産加工業等におけるHACCPの導入の促進 210
（3）安全で信頼される養殖生産物の供給 210
4 多様な流通ルートの構築 210
5 水産加工による付加価値の向上と販路拡大 210
6 加工・流通機能の発揮による適切な需給バランスの確保 211
7 水産物の輸出促進 211
（1）海外への正確な情報提供 211
（2）相手国の衛生基準への適合と高付加価値化の推進 211
8 水産物の輸入の確保 212

Ⅷ 安全で活力ある漁村づくり
1 漁港・漁村の防災機能・減災対策の強化 212
2 水産物の安定供給の基盤となる漁港機能の保全・強化 212
（1）漁港機能の保全対策の推進 212
（2）漁港・漁村における再生可能エネルギーの活用に向けた取組 213
3 地域資源の活用と水産業・漁村の多面的機能の発揮 213
（1）都市住民等との交流による漁村の活力の増進 213
（2）漁業と海洋性レクリエーションとの調和がとれた海面利用の促進 213
（3）多面的機能の発揮の促進 214

Ⅸ 水産業を支える調査・研究、技術開発の充実
1 水産業の未来を切り拓く新技術の開発及び普及 214
2 海洋モニタリング等の基礎的な調査・研究の着実な実施 214

Ⅹ 水産関係団体の再編整備等
1 漁業協同組合系統組織の再編整備等 215
2 漁業保険団体の事業基盤の確保 215

Ⅺ その他重要施策
1 WTO交渉等への取組 215
2 政策ニーズに対応した統計の作成と利用の推進 216

Ⅻ 水産に関する施策を総合的かつ計画的に推進するために必要な事項
1 東日本大震災の経験を踏まえた施策の展開 216
2 関係府省庁の連携による施策の効率的な推進 216
3 消費者・国民のニーズを踏まえた公益的な観点からの施策の展開 216
4 事業者や産地の主体性と創意工夫の発揮の促進 217
5 財政措置の効率的かつ重点的な運用 217
### Appendix 1. Error Correction Models

#### A.1.1 “Missing-Money” Problem

We studied specification of the money demand function and its stabilities and analyzed short-run dynamics with error learning hypothesis and adjustment mechanism to some depth in Chapter 3. The notion of stable demand-for-money function lies at the heart of macroeconomics, whether theoretical or econometrical. In IS-LM framework, it links real and monetary sectors of Keynesian models. In monetarist tradition, interaction between money supply and demand for money from the basis of the price level determination. Culminating in famous Goldfeld's (1973) evidence in the 60's, of a close and predictable relationship between the quantity of money and few explanatory variables in the money demand function such as income, prices, and interest rates was fundamental in the macroeconomic policy formulation. However, the question of whether the demand function is “reasonably stable” has been one of the central issues of the macroeconomic research in the last two decades.

Goldfeld (1976) found out that the dynamic simulations by real money partial adjustment model continuously overpredicted demand for money during mid 1970's for the U.S. economy. With a decade of evidence, the professionals has agreed that there was indeed a structural shift in the U.S. money demand shortly after 1973. These results have become known as the "Case of Missing Money." This notorious instability in the empirical U.S. money demand relationship in turn led to following strands of research on money demand. First, some studies try to explain these phenomenon on the basis of the misspecification in the money demand function. The attempts may include works such as Laidler (1980) of the use of permanent income rather than current income, Slovin and Suska (1983) of inclusion of interest rate volatility, Roley (1985) of temporal aggregation problem, Hwang (1985) of nominal vs. real adjustment. Second, to take into account of the effect of the financial innovation and deregulations, a number of attempts to modify traditional specifications of money demand function were carried out. This literature may include studies as Leiberman (1977) of time trends, Enzler, Johnson and Paulus (1976), Goldfeld (1976) of ratchets on interest rate and income, Dotsey (1985) of the number of electronic fund transfers over the Fed wire. Third, some have shed doubts on the theoretical foundation of the conventional money demand function. Carr and Darby (1981) emphasizes the role of money supply shocks. Clower and Howitt (1978), Akerlof (1979), Santomero and Seater (1981), Milbourne, Buckholtz, and Wasan (1983), Grossman and Weiss (1983), Romer (1986), Rotenberg (1986), MacCallum and Goodfriend (1987) all provided important extensions of conventional Baumol - Tobin transaction based money demand model, or its
Miller-Orr variant. Furthermore, Cooley and LeRoy (1981) and Gordon (1984) cast doubts on the econometric procedures of the estimation of the traditional money demand function. Finally the increasing discomfort experienced with the instability issue induced some researchers for alternative modelling strategy to capture short-run dynamics. This literature begins with the general auto-regressive distributed lag specification methodology, summarized in Hendry, Pagan, and Sargan (1984). This growing arsenal of new econometric techniques with special emphasis on the error-correction mechanism has provided us much more sophisticated examination of economic dynamics. This school also focuses on intended overparameterization, followed by a data-based simplification to a simpler specification of the model. Moreover, Baba, Hendry, and Starr (1992) and Rose (1985) apply this methodology for U.S. economy. They offer rather successful attempts in explaining missing money episodes in terms of misspecified dynamics.

This promising modelling approach is closely related to the other research route, called co-integration. Granger (1983), Granger and Weiss (1983) proved that if the vector $ X_{t} $ is co-integrated, then $ X_{t} $ has an error-correction representation and furthermore, if $ X_{t} $ is generated by an error-correction mechanism, then components of $ X_{t} $ are co-integrated. With this error-correction and co-integration modelling, we can empirically analyze long-run equilibrium and short-run dynamics simultaneously. Furthermore, we are no longer restricted to the traditional approach of modelling short-run dynamics by the partial-adjustment or adaptive expectations mechanism.

Though controversy concerning the existence of the stable money demand function still remain unsettled, there is no doubt that error correction model and cointegration analysis is the most important development of econometric techniques of the last decade. Therefore in this appendix we apply error-correction model of the money demand function for four countries in our study.

The rest of the appendix is divided as the followings. In the next section, an error-correction and co-integration model will be briefly explained. Section three presents the results of the unit root test. The estimated results of error correction model will be shown and discussed in section four.

#### A.1.2 Error-correction and Co-integration Approach

Developments of this ever-increasing literature on this approach are summarized, for example, in Alogoskoufis and Smith (1995) on error correction model and Muscatelli and Hurn (1995) on cointegration analysis. A number of methods have been proposed to estimate error-correction models which may generate quantitatively different results in finite small samples. In this appendix, as a preliminary approach, the basic two-step procedure, proposed
by Engle and Granger (1987) is adopted. In this subsection, we will briefly review the their method.

Given that each macroeconomic variable in the money demand function has a unit root, in the first stage, following long-run long-run relationship is estimated without regard for the short-run dynamics of money demand.

$$ \mathbf{m}_{\mathbf{t}}=\boldsymbol{\alpha}_{0}+\boldsymbol{\alpha}_{1}\mathbf{y}_{\mathbf{t}}+\boldsymbol{\alpha}_{2}\mathbf{r}_{\mathbf{t}}+\mathbf{u}_{\mathbf{t}} $$

The residuals of the this regression are tested for its stationarity to determine whether the regression represent a cointegrating relationship. This is done by running the following auxiliary regression.

$$ \Delta\mathbf{u}_{\mathbf{t}}=\beta_{1}\mathbf{u}_{\mathbf{t}-\mathbf{l}}+\sum_{\mathbf{i}=\mathbf{l}}^{\mathbf{n}}\beta_{1+\mathbf{i}}\Delta\mathbf{u}_{\mathbf{t}-\mathbf{i}}+\boldsymbol{\varepsilon}_{\mathbf{t}} $$

If that is case, the OLS estimates of the coefficients are consistent and converge rapidly to the true long-run parameters or more precisely, the OLS estimates of the cointegrating vector converges to the true value at a rate 1/T. If stationarity is rejected, then data do not support the existence of a stable long-run relationship between variables. This phenomena might be particularly important for us because this rejection of cointegration implies that money, income, and interest rate have not been related in a particularly stable, predictable way over the data sample. However, we should note that the tests do require a large number of observations to result in convincing results.

In the second stage, following error-correction model,

$$ \Delta\mathbf{m}_{\mathbf{t}}=\gamma_{0}+\gamma_{1}\mathbf{u}_{\mathbf{t}-1}+\sum_{\mathbf{i}=0}^{\mathbf{n}}(\gamma_{2\mathbf{i}}\Delta\mathbf{y}_{\mathbf{t}-\mathbf{i}}+\gamma_{3\mathbf{i}}\Delta\mathbf{r}_{\mathbf{t}-1}+\boldsymbol{\upmu} $$

will be estimated. The term $ u_{t-1} $ here represents deviations from long-run equilibrium and gives the equation its error correcting properties. The general distributed lag structure of equation (A.3) can be reduced to a more compact form, in principle, by the “general to specific” method.

#### A.1.4 Unit Root Test

Recent econometric research focused more attention on the time series properties of the data used in the regressions. In particular, as Granger and Newbold (1974) argue, regression of one nonstationary variable on another nonstationary variable may result in so-called spurious regression problem. Though error-correction and co-integration modelling is a significant econometrical contribution under these circumstances, prior to testing for co-
integration, we have to test whether each macroeconomic variable is integrated of order one. Nonstationarities of the macroeconomic variables are examined by the ADF (augmented Dickey-Fuller) test. The table A.1.1 through A.1.6 summarize results. It must be mentioned that the results are with linear time trend, except the case of interest rates.

For Japan, concerning M2, GNP, we conclude that we can not reject the hypothesis of a unit root. However, non-stationarity of level of the interest rate and the price level can be questioned, especially for the second period.

For Korea, the results in table A.1.3 and A.1.4 are similar to Japan. Nonstationarity of the price level is questionable for the second period. However, the unit root hypothesis cannot be rejected for the level of the interest rate variable in both periods.

Unit root hypothesis can not be rejected in most of Indonesian results. Nonetheless, the unit root hypothesis has to be rejected for the interest rate variables for the second period.

Because of some of these results, strictly speaking, it is not appropriate to use standard cointegration procedure, we still proceed on investigating possible cointegration relationship among macroeconomic variables in the money demand function, nonetheless.

#### A.1.5 Error Correction Model

To test whether money demand functions are co-integrated, long-run relationship (A.1) is estimated for each country with several combinations of the independent variables. The resulting test statistics from auxiliary regression with various lag structure are also shown in the Tables A.1.7 - A.1.12.

However, for Korea and Indonesia, the null hypothesis of the nonstationarity for the residuals can not be rejected at the 5% level of the significance. Thus we may tentatively conclude that there is no-cointegration (therefore, no long-run equilibrium) for the Korean and Indonesian money demand function at the usual levels of significance for these sample periods.

For Japan in Table A.1.7 and A.1.8, in the cointegrating regression, nonstationarity for the residuals in the auxiliary regression can be rejected at the 5% level of significance for both periods. Following error correction models are shown in table A.1.13 and A.1.14. However, coefficients of error term was insignificant for the first period.

Here, let us review the results of previous studies using error-correction models for money demand function for our sample countries. First for Japan, there exist several empirical studies using this methodology. Yoshida (1989) used the data from 1968QI to 1985Q3. He also adopted two step estimation procedures. Assuming price homogeneity, he estimated the error-correction model of the money demand function with expected significant sign on the error correction term. He examined the stability properties of the
error-correction model in details as well. Kitaoka (1992) focused identification problem and applied error-correction model to examine the issue. He used data from 1972Q2 to 1990Q4 two-step estimation procedure as well. In case of money demand function estimation, he found out the plausible error correction mechanism. Baba (1995) used data from 1973Q1 to 1992Q4 and found rather stable error-correction type of money demand function. Though his sample period is very similar to ours, he adopts different and rather sophisticated techniques such as Phillips-Perron (1988) unit root test, Johansen's (1988) test for conintegrating vector, Hansen's (1992) stability tests. Furthermore, his money demand function includes variables such as the stock of financial wealth, different measures of interest rates. Its lag structure is rather different as well.

Regarding other countries, Tseng and Corker (1991) applied error-correction models for several developing countries, he examined the effects of financial liberalization on money demand and monetary policy implementation for nine South-East Asian countries: Indonesia, Korea, Malaysia, Myanmar, Nepal, Philippines, Singapore, Sri Lanka, Thailand. We only focus here the results of money demand function estimation for Korea, Indonesia, and Philippines. Before discussing in details we have to note that most of the data of the price deflators and GDP in this study were interpolated from annual or semi-annual data by them. For Korea, the data period is from 1970Q1 to 1989Q4. For real broad money M2 seems to be cointegrated with income and the opportunity cost of holding money measured by the interest rate in the curb market minus deposit rates weighted by the share of quasi-money share, though evidence is not completely convincing. Regarding error-correction model, the estimated result is somewhat satisfactory. However, it also indicates instability of the function during 80's. Regarding Indonesia, the data used is from 1970Q1 to 198Q4. Real broad money appears to be cointegrated with income and an opportunity cost captured by the foreign interest rate (three month LABOR) minus a deposit rate weighted by the share of quasi-money. Satisfactory error-correction model is also estimated as well. Finally for Philippines, the data is from 1973Q1 to 1989Q4. Real broad money seems to be cointegrated with income. However, various measures of the opportunity costs and interest rate were found insignificant in the long-run regression. The estimated error-correction model was rather problematic as well.

Taken all these results including ones for the Japanese economy, our estimated results of error-correction models are considerably different. However, many factors are different; the data itself, sample period, measure of the interest rates, measure of opportunity costs, the addition of dummy variables in some cases and the (sometimes invalid) assumption of price homogeneity, and so on. At this stage it seems that the data restriction of study seems to be most responsible for the differences. Nevertheless, other factors such as
structural change, outlier, omission of variables and so on can lead to false conclusion in cointegrating analyses, more research would surely be needed.
COUNTRY	PERIOD	COUNTRY	PERIOD
JAPAN	1962Q1-1973Q1	JAPAN	1973Q2-1990Q1

ADF	95% CRITICAL VALUE	ADF	95% CRITICAL VALUE

VARIABLE	M2	VARIABLE	M2
ADF (1)	-1.7643	-3.5162	ADF (1)	-2.0803	-3.4779
ADF (2)	-1.5165	-3.5189	ADF (2)	-2.1489	-3.479
ADF (3)	-0.61857	-3.5217	ADF (3)	-2.3033	-3.4801
ADF (4)	0.045348	-3.5247	ADF (4)	-2.1871	-3.4812

VARIABLE	GNP	VARIABLE	GNP
ADF (1)	-1.7337	-3.5162	ADF (1)	-2.9713	-3.4779
ADF (2)	-2.1541	-3.5189	ADF (2)	-3.328	-3.479
ADF (3)	-2.1845	-3.5217	ADF (3)	-1.7433	-3.4801
ADF (4)	-2.0486	-3.5247	ADF (4)	-2.006	-3.4812

VARIABLE	P	VARIABLE	P
ADF (1)	-3.0875	-3.5162	ADF (1)	-4.6984	-3.4779
ADF (2)	-2.7304	-3.5189	ADF (2)	-3.2349	-3.479
ADF (3)	-1.6402	-3.5217	ADF (3)	-3.9062	-3.4801
ADF (4)	-1.5381	-3.5247	ADF (4)	-3.6173	-3.4812

VARIABLE	R	VARIABLE	R
ADF (1)	-3.048	-2.9303	ADF (1)	-2.343	-2.9055
ADF (2)	-3.2642	-2.932	ADF (2)	-2.8175	-2.9062
ADF (3)	-2.0367	-2.9339	ADF (3)	-2.9528	-2.9069
ADF (4)	-1.8228	-2.9358	ADF (4)	-3.2279	-2.9077
COUNTRY	PERIOD
KOREA	1973Q3-1979Q4

ADF	95% CRITICAL VALUE

VARIABLE	M2
ADF (1)	-1.9079	-3.6119
ADF (2)	-2.2399	-3.6219
ADF (3)	-2.9108	-3.6331
ADF (4)	-2.6808	-3.6454

VARIABLE	GNP
ADF (1)	-2.3957	-3.6119
ADF (2)	-3.3463	-3.6219
ADF (3)	-2.2667	-3.6331
ADF (4)	-2.3555	-3.6454

VARIABLE	P
ADF (1)	-3.6865	-3.6119
ADF (2)	-2.0844	-3.6219
ADF (3)	-2.8299	-3.6331
ADF (4)	-2.6399	-3.6454

VARIABLE	R
ADF (1)	-0.86916	-2.9907
ADF (2)	0.14963	-2.997
ADF (3)	1.62	-3.0039
ADF (4)	2.2962	-3.0115
COUNTRY	PERIOD	COUNTRY	PERIOD
INDONESIA	1977Q1-1983Q4	INDONESIA	1983Q1-1990Q1

ADF	95% CRITICAL VALUE	ADF	95% CRITICAL VALUE

VARIABLE	M2	VARIABLE	M2
ADF (1)	-2.2174	-3.5943	ADF (1)	0.019939	-3.5867
ADF (2)	-2.0385	-3.6027	ADF (2)	0.39398	-3.5943
ADF (3)	-1.8715	-3.6119	ADF (3)	0.15243	-3.6027
ADF (4)	-1.8801	-3.6219	ADF (4)	0.21395	-3.6119

VARIABLE	GNP	VARIABLE	GNP
ADF (1)	-1.7768	-3.5943	ADF (1)	-1.451	-3.5867
ADF (2)	0.21714	-3.6027	ADF (2)	-1.3818	-3.5943
ADF (3)	0.58607	-3.6119	ADF (3)	-1.6062	-3.6027
ADF (4)	0.68799	-3.6219	ADF (4)	-1.4676	-3.6119

VARIABLE	P	VARIABLE	P
ADF (1)	-3.4707	-3.5943	ADF (1)	-2.4904	-3.5867
ADF (2)	-0.79348	-3.6027	ADF (2)	-2.03	-3.5943
ADF (3)	-0.63315	-3.6119	ADF (3)	-2.4309	-3.6027
ADF (4)	-0.71339	-3.6219	ADF (4)	-1.4231	-3.6119

VARIABLE	R	VARIABLE	R
ADF (1)	0.70182	-2.9798	ADF (1)	-3.1774	-2.975
ADF (2)	0.46714	-2.985	ADF (2)	-3.0698	-2.9798
ADF (3)	0.58196	-2.9907	ADF (3)	-3.3432	-2.985
ADF (4)	0.74775	-2.997	ADF (4)	-3.2424	-2.9907