(Luận văn thạc sĩ) tác động tỷ giá hối đoái đến cán cân thương mại của việt nam

BỘ GIÁO DỤC VÀ ĐÀO TẠO TRƯỜNG ĐẠI HỌC KINH TẾ THÀNH PHỐ HỒ CHÍ MINH NGUYỄN NGỌC LÂM TÁC ĐỘNG TỶ GIÁ HỐI ĐOÁI ĐẾN CÁN CÂN THƯƠNG MẠI CỦA VIỆT NAM LUẬN VĂN THẠC SĨ KINH TẾ TP HỒ CHÍ MINH – NĂM 2012 BỘ GIÁO DỤC VÀ ĐÀO TẠO TRƯỜNG ĐẠI HỌC KINH TẾ THÀNH PHỐ HỒ CHÍ MINH NGUYỄN NGỌC LÂM TÁC ĐỘNG TỶ GIÁ HỐI ĐOÁI ĐẾN CÁN CÂN THƯƠNG MẠI CỦA VIỆT NAM Chuyên ngành: Kinh tế Tài - Ngân hàng Mã số: 60340201 LUẬN VĂN THẠC SĨ KINH TẾ Người hướng dẫn khoa học: TS Nguyễn Tấn Hồng TP HỒ CHÍ MINH – NĂM 2012 LỜI CẢM ƠN Để hoàn thành chƣơng trình cao học luận văn này, tơi nhận đƣợc hƣớng dẫn, giúp đỡ góp ý nhiệt tình q thầy trƣờng Đại học Kinh tế Thành phố Hồ Chí Minh, bạn bè, gia đình đồng nghiệp Trƣớc hết, xin chân thành gửi lời cảm ơn đến Tiến sĩ Nguyễn Tấn Hoàng - ngƣời tận tình hƣớng dẫn tơi suốt q trình thực luận văn Tơi xin gửi lời cảm ơn đến toàn thể cán nhân viên BIDV hỗ trợ tạo điều kiện thuận lợi để tơi hồn thành luận văn TP.Hồ Chí Minh, ngày 12 tháng 11 năm 2012 Học viên LỜI CAM ĐOAN Tơi xin cam đoan cơng trình nghiên cứu với giúp đỡ Tiến sĩ Nguyễn Tấn Hoàng; số liệu thống kê trung thực, nội dung kết nghiên cứu luận văn chƣa đƣợc công bố công trình thời điểm Tp.HCM, ngày 12 tháng 11 năm 2012 Tác giả MỤC LỤC Mở đầu - Giới thiệu Mục tiêu nghiên cứu - Câu hỏi nghiên cứu - CHƢƠNG 1: KHUNG LÝ THUYẾT VỀ TÁC ĐỘNG CỦA TỶ GIÁ HỐI ĐOÁI ĐẾN CÁN CÂN THƢƠNG MẠI - 1.1 Tỷ giá hối đoái 1.1.1 Khái niệm 1.1.2 Phân loại - 1.1.3 Các yếu tố ảnh hƣởng đến tỷ giá 1.1.3.1 Mức chênh lệch lãi suất nƣớc 1.1.3.2 Mức chênh lệch lạm phát quốc gia - 1.1.3.3 Niềm tin nhà đầu tƣ - 1.1.3.4 Tình trạng cán cân tốn quốc tế 1.1.3.5 Sự can thiệp phủ - 1.1.3.6 Các nhân tố khác - Cán cân thƣơng mại 1.2.1 Khái niệm 1.2.2 Các yếu tố ảnh hƣởng đến cán cân thƣơng mại - 1.2.2.1 Yếu tố tỷ giá hối đoái 1.2.2.2 Yếu tố thu nhập - 1.3 Các cơng trình nghiên cứu trƣớc tác động tỷ giá hối đoái đến cán cân thƣơng mại - 10 1.3.1 Nghiên cứu nƣớc - 10 1.3.2 Nghiên cứu nƣớc 12 CHƢƠNG 2: MÔ HÌNH VÀ PHƢƠNG PHÁP NGHIÊN CỨU - 16 2.1 Mơ hình nghiên cứu - 16 2.1.1 Mơ hình Senhadji Montenegro (1998) 16 2.1.2 Mô hình VECM Uỷ Ban Kinh Tế Quốc Hội UNDP Việt Nam (2011) 17 2.1.3 Mô hình đề tài 17 2.2 Phƣơng pháp nghiên cứu 19 2.2.1 Mơ hình Var - 19 2.2.2 Các bƣớc chạy mơ hình Var - 20 2.2.3 Thu thập số liệu - 23 CHƢƠNG 3: THẢO LUẬN KẾT QUẢ NGHIÊN CỨU VỀ TÁC ĐỘNG CỦA TỶ GIÁ HỐI ĐOÁI ĐẾN CÁN CÂN THƢƠNG MẠI CỦA VIỆT NAM - 24 3.1 Các giả định mơ hình 24 3.2 Các bƣớc thực 26 3.3 Kết nghiên cứu thảo luận - 34 CHƢƠNG 4: KHUYẾN NGHỊ - 36 4.1 Chính sách tỷ giá 36 4.2 Tìm đầu cho xuất 38 Kết luận - 42 DANH MỤC TỪ VIẾT TẮT ADF Kiểm định Augmented Dickey – Fuller test AIC Tiêu chuẩn kiểm tra độ trễ Akaike APEC Diễn đàn hợp tác kinh tế Châu Á – Thái Bình Dƣơng BIDV Ngân hàng TMCP Đầu Tƣ Phát Triển Việt Nam CNY Đồng nhân dân tệ DF Kiểm định Dickey Fuller HQ Tiêu chuẩn kiểm tra độ trễ Hannan – Quinn IMF Quỹ tiền tệ quốc tế NHNN Ngân hàng Nhà Nƣớc NHTW Ngân hàng trung ƣơng SIC Tiêu chuẩn kiểm tra độ trễ Schwaiz UNDP Tổ chức Chƣơng Trình Phát Triển Liên Hợp Quốc VND Việt Nam đồng WTO Tổ chức thƣơng mại quốc tế DANH MỤC BẢNG Bảng 1.1: Tỷ giá JPY/USD theo tháng từ tháng 4/2011 đến tháng 12/2011 Bảng 3.1: Kết kiểm định tính dừng chuỗi liệu - 25 Bảng 3.2: Kết kiểm định độ trễ cho hàm xuất 25 Bảng 3.3: Kết kiểm định độ trễ cho hàm nhập - 27 Bảng 3.4: Hàm phản ứng xung tác động biến tỷ giá hối đối đến biến cịn lại hàm xuất 32 Bảng 3.5: Hàm phản ứng xung tác động biến tỷ giá hối đoái đến biến lại hàm nhập - 32 Bảng 3.6: Thay đổi xuất nhập thị trƣờng Mỹ tỷ giá tăng 1% - 33 Bảng 3.7: Thay đổi xuất nhập thị trƣờng Mỹ, Nhật, Hàn Quốc Đức tỷ giá tăng 1% 34 Bảng 4.1: Tổng mức bán lẻ hàng hoá doanh thu dịch vụ tiêu dùng thị trƣờng Việt Nam - 40 MỞ ĐẦU Giới thiệu: Tỷ giá biến số kinh tế vĩ mơ quan trọng có tác động tới nhiều mặt hoạt động kinh tế Kể từ Việt Nam mở cửa hội nhập kinh tế quốc tế năm 1986, tỷ giá VND/USD không ngừng tăng qua năm, đặc biệt sau cú sốc kinh tế khu vực giới, tỷ giá hối đối Việt Nam có biến động mạnh, ảnh hưởng không nhỏ đến xuất nhập Việt Nam Chính sách tỷ giá hối đối ảnh hưởng đến cán cân thương mại Việt Nam không xa lạ với nhiều đọc giả, nhà nghiên cứu Tuy nhiên mức độ ảnh hưởng đến đâu? Có đo lường hay không? Liệu việc gia tăng tỷ giá hối đối có thực cải thiện tình trạng nhập siêu Việt Nam nay? Đây câu hỏi cần giải đáp để có nhìn cận cảnh tác động sách tỷ giá hối đoái lên cán cân thương mại Việt Nam thời gian qua, từ đưa kiến nghị giải pháp sách tỷ biện pháp cải thiện cán cân thương mại Việt Nam thời gian tới Đó lý tác giả lựa chọn đề tài “Tác động tỷ giá hối đoái đến cán cân thƣơng mại Việt Nam” để làm đề tài nghiên cứu bảo vệ học vị Thạc Sĩ Kinh Tế Mục tiêu nghiên cứu: Nhằm nghiên cứu tác động tỷ giá hối đoái đến cán cân thương mại Việt Nam Câu hỏi nghiên cứu: Khi tỷ giá hối đoái danh nghĩa tăng lên 1%, giá trị xuất nhập Việt Nam tăng giảm %? Việc gia tăng tỷ giá hối đoái danh nghĩa ngắn hạn dài hạn có thực cải thiện cán cân thương mại Việt Nam? CHƢƠNG 1: KHUNG LÝ THUYẾT VỀ TÁC ĐỘNG CỦA TỶ GIÁ HỐI ĐOÁI ĐẾN CÁN CÂN THƢƠNG MẠI 1.1 Tỷ giá hối đoái: 1.1.1 Khái niệm: Tỷ giá hối đoái mối quan hệ so sánh sức mua đồng tiền với Đó giá chuyển đổi đơn vị tiền tệ nước thành đơn vị tiền tệ nước khác 1.1.2 Phân loại: Nếu vào chế độ quản lý ngoại hối, tỷ giá hối đoái bao gồm: Tỷ giá thức: loại tỷ giá ngân hàng trung ương nước công bố Tỷ giá hối đối cơng bố hàng ngày vào đầu làm việc ngân hàng trung ương Dựa vào tỷ giá ngân hàng thương mại tổ chức tín dụng ấn định tỷ giá mua bán ngoại tệ giao ngay, có kỳ hạn, hoán đổi Ở số nước Pháp tỷ giá hối đối thức ấn định thơng qua nhiều giao dịch vào thời điểm xác định ngày Tỷ giá kinh doanh: tỷ giá dùng để kinh doanh mua bán ngoại tệ Tỷ giá ngân hàng thương mại hay tổ chức tín dụng đưa Cơ sở xác định tỷ giá tỷ giá thức ngân hàng trung ương cơng bố xem xét đến yếu tố liên quan trực tiếp đến kinh doanh như: quan hệ cung cầu ngoại tệ, tỷ suất lợi nhuận, tâm lý người giao dịch ngoại tệ cần mua bán Tỷ giá kinh doanh bao gồm tỷ giá mua, tỷ giá bán Tỷ giá chợ đen: Tỷ giá hình thành bên ngồi thị trường ngoại tệ thức TIME SERIES ANALYSIS OF EXPORT DEMAND EQUATIONS Fully-Modified estimates x–1 p gdpx* Ep Ey Epc E yc ser R2 P-O Ep =–1 Ey =1 nobs 0.27 –0.07 2.91 –3.26 0.83 7.60 –0.09 1.15 –0.09 –3.08 b 22.32 a 1.14 0.04 0.93 –6.62 a 30.42 a 2.83 c 34 0.56 –0.11 4.33 –1.94 0.56 2.73 –0.24 –2.04 c 1.28 –0.24 7.90 a 1.28 0.08 0.95 –5.11 a 1.75 34 0.90 –0.17 9.58 –2.13 0.08 0.64 –1.73 –1.22 c 0.80 1.45 –2.24 0.79 0.04 0.99 –4.93 a –0.52 –0.36 34 0.75 –0.04 11.44 –0.70 0.65 3.37 –0.15 –0.75 2.59 –0.15 21.16 a 2.49 0.03 1.00 –5.25 a 0.80 –0.26 10.14 –1.58 0.31 1.59 –1.32 –2.07 b 1.55 –1.24 3.00 b 1.55 0.11 0.93 –4.13 c –0.50 1.07 34 0.28 –0.19 1.71 –2.01 0.74 3.08 –0.26 –2.00 c 1.03 –0.25 5.07 a 1.02 0.15 0.77 –6.61 a 5.81 a 0.13 34 0.84 –0.04 7.48 –0.30 0.36 0.91 –0.24 –0.34 2.29 –0.17 1.46 b 2.26 0.11 0.96 –3.84 c 1.10 0.82 29 0.85 –0.17 14.60 –2.61 0.20 1.87 –1.08 –2.99 a 1.31 –1.39 3.14 b 1.29 0.06 0.99 –5.47 a –0.21 0.73 34 0.80 –0.63 10.29 –3.08 0.24 1.71 –3.13 –4.08 a 1.20 –3.55 2.24 b 1.15 0.12 0.99 –13.33 a 0.38 34 0.86 –0.21 7.76 –1.70 0.19 1.12 –1.52 –1.86 b 1.39 –1.73 3.13 b 1.39 0.06 0.98 –4.61 b –0.63 0.88 34 0.84 –0.03 7.58 –0.32 0.25 1.10 –0.16 –0.35 1.52 –0.16 2.62 b 1.46 0.09 0.96 –5.64 a 1.89 0.89 34 0.85 –0.06 11.77 –1.13 0.23 1.70 –0.36 –1.14 1.51 –0.41 7.22 a 1.51 0.02 1.00 –4.44 b 2.05 2.43 34 0.56 –0.36 4.91 –3.41 0.59 2.97 –0.81 –3.96 a 1.34 –0.85 5.38 a 1.29 0.12 0.93 –5.73 a 0.93 1.36 34 0.87 –0.43 8.95 –2.98 0.02 0.06 –3.21 –1.25 0.17 0.07 –2.51 0.16 0.14 0.95 –4.34 b –0.86 –0.33 29 0.84 –0.24 11.33 –2.68 0.18 1.37 –1.44 –2.19 b 1.12 –1.43 2.63 b 1.12 0.07 0.97 –9.80 a –0.67 0.29 34 0.45 –0.58 5.36 –5.55 1.15 6.26 –1.05 2.09 –1.20 –6.61 a 40.64 a 2.09 0.03 0.99 –4.58 a –0.34 21.22 a 34 0.79 0.00 11.42 –0.05 0.49 2.90 –0.02 –0.05 2.28 –0.02 16.31 a 2.18 0.03 1.00 –3.82 2.85 b 9.17 a 34 0.49 –0.40 4.02 –2.59 0.43 3.56 –0.79 –3.08 b 0.84 –0.74 8.96 a 0.84 0.11 0.89 –4.26 b 0.81 0.66 –0.24 7.08 –1.27 0.95 3.11 –0.70 –1.43 c 2.81 –0.80 6.91 a 2.81 0.05 0.99 –4.77 a 0.61 0.90 –0.09 20.55 –0.76 0.03 0.43 –0.87 –0.77 0.31 0.48 –0.92 0.29 0.05 0.94 –4.69 a 0.11 –1.10 34 0.80 –0.07 10.49 –0.93 0.29 1.82 –0.37 –0.89 1.41 –0.44 2.63 c 1.44 0.10 0.83 –5.91 a 1.55 0.76 34 6.45 a 4.41 a 12.99 a 2.77 a –1.72 4.45 b 34 34 34 267 Abdelhak S Senhadji and Claudio E Montenegro Table (continued) Ordinary Least Squares (OLS) estimates Country x–1 p gdpx* AC ser R2 Iceland 0.61 4.61 –0.27 –2.02 0.57 2.60 0.12 0.67 0.07 0.98 Italy 0.58 4.95 –0.07 –0.87 0.95 3.25 0.18 0.93 0.04 1.00 Japan 0.82 10.00 –0.25 –1.56 0.46 1.65 0.05 0.27 0.06 1.00 Kenya 0.62 4.17 –0.34 –3.64 0.27 1.57 –0.29 –1.43 0.07 0.94 Korea 0.72 8.03 –0.61 –2.05 1.21 2.59 0.27 1.51 0.10 1.00 Malawi 0.34 2.03 –0.18 –1.22 0.79 3.38 0.19 1.16 0.11 0.93 0.78 10.80 –0.12 –0.86 0.64 3.19 0.27 1.57 0.08 0.99 Mauritius 0.78 5.96 –0.25 –1.45 0.45 1.66 –0.05 –0.37 0.15 0.90 Morocco 0.63 6.17 –0.38 –2.59 0.43 3.21 0.01 0.06 0.07 0.97 New Zealand 0.78 5.53 –0.17 –2.16 0.21 1.20 –0.24 –1.29 0.04 0.99 Niger 0.65 4.79 –0.32 –1.42 0.15 0.79 –0.15 –0.80 0.19 0.50 Nigeria 0.78 6.04 –0.04 –0.45 0.25 1.25 0.09 0.46 0.17 0.85 Norway 0.82 7.91 –0.17 –2.10 0.36 1.66 0.22 1.14 0.03 1.00 Panama 0.78 7.20 –0.23 –2.64 0.16 0.62 –0.22 –1.17 0.06 0.99 Paraguay 0.57 6.24 –0.88 –4.39 1.21 5.42 0.01 0.09 0.14 0.96 Peru 0.62 4.19 –0.06 –0.60 0.13 1.19 –0.06 –0.32 0.09 0.72 Philippines 0.52 6.01 –0.62 –6.33 0.59 4.09 0.03 0.15 0.07 0.98 Portugal 0.88 7.01 –0.25 –1.15 0.24 0.80 0.22 1.19 0.11 0.96 Senegal 0.26 1.71 –0.42 –2.58 0.42 3.15 0.00 0.01 0.11 0.84 South Africa 0.59 6.59 –0.20 –4.47 0.26 4.56 0.21 1.11 0.03 0.97 Spain 0.60 4.39 –0.06 –0.58 1.18 2.72 0.12 0.62 0.05 1.00 Malta 268 TIME SERIES ANALYSIS OF EXPORT DEMAND EQUATIONS Fully-Modified estimates x–1 p gdpx* Ep Ey Epc E yc ser R2 P-O Ep =–1 Ey =1 nobs 3.16 34 0.70 –0.28 9.71 –3.81 0.41 3.39 –0.93 1.37 –1.11 –3.76 a 11.78 a 1.39 0.04 0.98 –4.75 a 0.29 0.65 –0.05 6.55 –0.69 0.80 3.27 –0.14 –0.76 2.26 –0.13 24.63 a 2.25 0.03 1.00 –4.83 a 4.75 a 13.74 a 0.87 –0.17 19.70 –1.94 0.27 1.81 –1.27 –2.30 b 2.11 –1.33 4.21 a 2.02 0.03 1.00 –9.74 a –0.48 0.84 –0.33 7.66 –4.71 0.03 0.21 –2.07 –1.56 c 0.17 0.25 –2.36 0.17 0.05 0.94 –7.49 a –0.81 0.76 –0.52 10.52 –2.15 1.04 2.73 –2.17 –2.80 b 4.34 –2.15 9.85 a 4.31 0.08 1.00 –4.95 a –1.51 7.58 a 34 0.50 –0.05 4.91 –0.55 0.63 4.16 –0.10 –0.57 1.25 –0.11 8.43 a 1.20 0.06 0.93 –10.70 a 5.01 a 1.70 34 0.84 –0.04 13.39 –0.33 0.46 2.52 –0.22 –0.34 2.80 –0.21 6.89 a 2.79 0.06 0.98 –3.88 c 1.18 4.43 a 34 0.89 –0.21 10.24 –1.82 0.34 1.91 –1.92 –0.96 3.17 –1.67 2.28 c 3.24 0.10 0.94 –6.02 a –0.46 1.56 34 0.81 –0.28 8.06 –2.19 0.22 1.52 –1.47 –1.41 1.12 –1.45 3.95 b 1.11 0.06 0.97 –6.42 a –0.45 0.42 34 0.90 –0.13 9.33 –2.42 0.08 0.64 –1.25 –0.94 0.78 1.49 –1.62 0.80 0.03 0.99 –9.50 a –0.19 –0.41 34 0.84 –0.28 8.60 –1.80 0.06 0.47 –1.74 –1.16 0.38 0.50 –1.83 0.36 0.13 0.46 –7.41 a –0.49 –0.82 34 0.91 –0.04 9.31 –0.65 0.15 1.03 –0.50 –0.43 1.69 1.15 –0.43 1.72 0.12 0.85 –5.14 a 0.44 0.47 34 0.90 –0.15 10.40 –2.32 0.17 0.91 –1.51 –1.36 c 1.65 –1.73 3.67 b 1.65 0.03 1.00 –9.43 a –0.46 1.44 34 0.85 –0.17 12.21 –2.75 0.07 0.41 –1.14 –1.68 c 0.47 0.50 –1.07 0.47 0.04 0.99 –7.33 a –0.20 –0.56 34 0.64 –0.96 7.93 –5.70 1.11 5.42 –2.67 3.08 –2.80 –4.19 c 10.66 a 2.96 0.12 0.96 –4.75 a –2.62 b 0.78 0.00 8.17 –0.05 0.12 1.64 –0.02 –0.05 0.53 2.30 –0.02 0.54 0.06 0.71 –6.30 a 0.59 –0.51 8.64 –6.60 0.49 4.20 –1.24 –6.92 a 1.20 –1.22 9.52 a 1.19 0.05 0.98 –4.59 b –1.32 1.57 34 0.93 –0.20 9.84 –1.21 0.09 0.38 –2.92 –0.50 1.30 0.75 –2.89 1.29 0.08 0.96 –4.93 a –0.33 0.17 34 0.45 –0.28 3.64 –2.27 0.32 2.79 –0.50 –2.59 b 0.58 –0.47 3.93 a 0.58 0.08 0.84 –6.64 a 2.54 b –2.85 34 0.65 –0.18 8.91 –5.45 0.23 5.24 –0.51 0.66 –0.50 –4.15 a 10.33 a 0.65 0.02 0.97 –9.12 a 4.02 a –5.35 a 34 0.67 –0.06 6.05 –0.74 0.94 2.64 –0.18 –0.82 2.75 0.04 1.00 –12.06 a 3.80 b 11.71 a 34 2.86 –0.19 18.01 a 2.21 c –1.22 34 34 34 7.20 a 34 3.34 c –2.08 c 34 269 Abdelhak S Senhadji and Claudio E Montenegro Table (concluded) Ordinary Least Squares (OLS) estimates Country x–1 p gdpx* AC ser R2 Sweden 0.55 5.01 –0.13 –1.88 0.76 3.68 0.33 1.84 0.03 1.00 Switzerland 0.31 2.91 –0.12 –2.42 1.18 6.24 0.34 2.04 0.02 1.00 Togo 0.57 3.20 –0.21 –1.21 0.58 1.22 0.13 0.69 0.22 0.90 Trinidad and Tobago 0.24 1.58 –0.29 –4.63 0.91 4.28 0.17 0.91 0.10 0.96 Tunisia 0.59 5.62 –0.17 –1.26 1.15 3.67 –0.09 –0.47 0.07 0.99 Turkey 0.82 10.59 –0.69 –2.50 0.31 1.15 0.09 0.45 0.14 0.98 United Kingdom 0.58 7.19 –0.16 –2.59 0.61 4.84 0.03 0.17 0.03 1.00 United States 0.79 8.41 –0.19 –1.42 0.26 2.20 0.48 2.86 0.05 0.99 Uruguay 0.66 5.70 –0.48 –2.67 0.21 1.12 –0.14 –0.78 0.09 0.97 Yugoslavia 0.47 3.45 –0.23 –3.33 0.67 2.92 –0.10 –0.54 0.07 0.97 Zaire 0.50 3.84 –0.15 –2.27 0.58 2.69 0.15 0.72 0.14 0.91 Mean 0.61 –0.27 0.59 Median 0.64 –0.21 0.53 Stdev 0.19 0.20 0.35 Min 0.04 –0.88 0.05 Max 0.88 –0.01 1.32 aSignificant at percent at percent cSignificant at 10 percent Note: The dependent variable is real export of goods and nonfactor services, x The explanatory variables are the lagged dependent variable, x –1; the real exchange rate, p, computed as the ratio of exports deflator to the world export unit value index, and the weighted (by export shares) average of trade partners’ GDP minus exports, gdpx* The export demand equation is estimated using both OLS and the Phillips-Hansen’s Fully Modified estimator The long-run price and income elasticities are given by Ep and Ey, respectively E pc and E yc give the long run price and income elasticities corrected for bias (see Table in Senhadji and Montenegro, 1998) For each country, the estimated coefficients and their t-statistic (below the coefficient estimates) are provided The following statistics are also provided: Durbin’s test for autocorrelation, AC; R 2; standard error of the regression, ser; and the number of observations for each country, nobs Cointegration between the three variables in the export demand equation is tested using the Phillips-Ouliaris residual test given in column P-O Finally, the columns labeled Ep = –1 and Ey = report the two-tailed test for unitprice and unit-income elasticities, respectively The asymptotic critical values for the Phillips-Ouliaris test at 10 percent, percent, and percent are, respectively, –3.84, –4.16, and –4.64 Exact critical values (from Table in Senhadji and Montenegro, 1998) are used to compute the significance level of Ep, Ey, Ep = –1, and Ey = bSignificant 270 TIME SERIES ANALYSIS OF EXPORT DEMAND EQUATIONS Fully-Modified estimates x–1 p gdpx* Ep Ey E pc E yc ser R2 P-O Ep =–1 nobs 0.68 –0.09 6.26 –1.37 0.53 2.58 –0.29 1.65 –0.30 –1.29 c 14.22 a 1.59 0.03 1.00 –4.13 b 5.62 a 34 0.42 –0.10 4.36 –2.34 0.98 5.65 –0.17 1.69 –0.18 –2.52 b 39.10 a 1.62 0.02 1.00 –9.30 a 12.07 a 15.92 a 34 0.84 –0.05 5.82 –0.36 0.21 0.53 –0.33 –0.38 1.27 0.74 –0.34 1.22 0.17 0.89 –6.74 a 0.79 0.16 34 0.37 –0.25 2.80 –4.58 0.78 4.20 –0.39 –6.13 a 1.24 –0.31 9.41 a 1.22 0.09 0.96 –5.40 a 9.49 a 1.81 c 34 0.78 –0.17 8.60 –1.68 0.54 1.93 –0.78 –1.29 2.43 –0.77 7.64 a 2.42 0.05 0.99 –6.00 a 0.36 4.50 b 33 0.88 –0.58 15.84 –2.96 0.06 0.30 –4.72 –2.32 b 0.51 0.33 –5.38 0.51 0.10 0.98 –4.72 a –1.83 c –0.32 34 0.66 –0.12 9.38 –2.45 0.48 4.29 –0.35 1.43 –0.33 –2.54 b 22.81 a 1.42 0.02 1.00 –5.41 a 4.71 a 6.86 a 34 0.96 –0.03 9.52 –0.23 0.05 0.34 –0.73 –0.27 1.04 0.93 –0.69 1.04 0.05 0.99 –3.53 0.10 0.04 34 0.75 –0.39 9.35 –3.05 0.15 1.02 –1.55 –2.94 a 0.59 1.24 –1.77 0.59 0.06 0.97 –5.92 a –1.05 –0.87 34 0.55 –0.19 5.84 –3.80 0.52 3.30 –0.42 –5.13 a 1.17 –0.41 8.55 a 1.16 0.05 0.97 –6.03 a 7.24 a 1.23 31 0.58 –0.15 6.28 –2.91 0.39 2.00 –0.37 –2.98 b 0.93 –0.37 2.48 b 0.92 0.10 0.90 –4.57 b 5.08 a –0.19 31 0.72 –0.21 0.41 –1.02 1.47 –1.07 1.45 0.79 –0.17 0.32 –0.78 1.30 –0.77 1.29 0.17 0.19 0.31 0.97 0.85 1.04 0.84 0.27 –0.96 0.02 –4.72 0.17 –5.38 0.16 0.96 1.15 –0.02 4.34 –0.02 4.31 0.00 3.20 b Ey =1 271 Abdelhak S Senhadji and Claudio E Montenegro Developing countries, except Asia, have significantly lower income elasticities than industrial countries Developing countries also show lower price elasticities than industrial countries Finally, the lower income elasticities for developing countries in general, and for Africa in particular, are even more forcefully demonstrated by the following weighted least squares regressions:6 – Ey = 1.83 – 1.04daf – 0.40das – 0.54dla – 0.62dme , R = 90, N = 53; (25.77)(–6.99) (–1.14) (–2.28) (–3.89) Ey = 1.83 – 0.78dldc, (24.71)(–6.69) – R = 89, N = 53 (7) (8) While developing countries’ income elasticities are lower, they remain larger than one Consequently, growth in their partner countries will translate into growth of at least the same magnitude of their exports Thus trade remains an important engine of growth for all developing countries III Conclusion The paper provides income and price elasticities of the export demand function for 53 industrial and developing countries, estimated within a consistent framework and taking the possible nonstationarity in the data into account The long-run price and income elasticities generally have the expected sign and, in most cases, are statistically significant The average price elasticity is close to zero in the short run but reaches about one in the long run Twenty-two of the 53 countries in the sample have point estimates of long-run price elasticity larger than one, and for 33 countries the unit-price elasticity cannot be rejected It takes six years for the average price elasticity to achieve 90 percent of its long-run level A similar pattern holds for income elasticities in that exports react relatively slowly to changes in trade partners’ income The short-run income elasticities are on average less then 0.5, while the long-run income elasticities are on average close to 1.5 Thirty-nine countries have point estimates of long-run income elasticity that are larger than one, and for 35 countries the unit-income elasticity cannot be rejected Thus, exports significantly react to both movements in the activity variable and the relative price, though slowly A comparison with Reinhart (1995), who uses a similar methodology, shows that her estimates of the price elasticities are significantly lower Her mean estimate (over the 10 developing countries showing the right sign) is –0.44, while it is –1.14 in this paper (where the mean is over the 37 developing countries in the sample) Conversely, her average income elasticity is 1.99 compared to 1.32 in this paper These differences may simply reflect the difference in the periods of analysis and sample sizes While developing countries show, in general, lower price elasticities than industrial countries, Asian countries have significantly higher price elasticities 6All the variables in the equations have been weighted by the inverse of the standard error of the corresponding elasticity 272 TIME SERIES ANALYSIS OF EXPORT DEMAND EQUATIONS than both industrial and developing countries Furthermore, Asian countries benefit from higher income elasticities than the rest of the developing world, corroborating the general view that trade has been a powerful engine of growth in the region Africa, in contrast, faces the lowest income elasticities References Faini, Riccardo, Fernando Clavijo, and Abdelhak Senhadji, 1992, “The Fallacy of Composition Argument: Is It Relevant for LDCs’ Manufactures Exports?” European Economic Review, Vol 36 (May), pp 865–82 Goldstein, Morris, and Mohsin Khan, 1982, “Effects of Slowdown in Industrial Countries on Growth in Non-Oil Developing Countries,” Occasional Paper No 12 (Washington: International Monetary Fund) ———, 1985, “Income and Price Effect in Foreign Trade,” in Handbook of International Economics, ed by Ronald Jones and Peter Kenen (Amsterdam: North-Holland) Hamilton, James, 1994, “Time Series Analysis,” (Princeton, N.J.: Princeton University Press) Hansen, Bruce, 1992, “Efficient Estimation and Testing of Cointegrating Vectors in the Presence of Deterministic Trends,” Journal of Econometrics, Vol 53 (July–September), pp 87–121 Lewis, Arthur, 1980, “The Slowing Down of the Engine of Growth,” American Economic Review, Vol 70 (September), pp 555–64 Marquez, Jaime, and Caryl McNeilly, 1988, “Income and Price Elasticities for Exports of Developing Countries,” Review of Economics and Statistics, Vol 70 (February), pp 306–14 Ostry, Jonathan, and Andrew Rose, 1992, “An Empirical Evaluation of the Macroeconomic Effects of Tariffs,” Journal of International Money and Finance, Vol 11 (February), pp 63–79 Pesaran, M Hashem, and Yongcheol Shin, forthcoming, “An Autoregressive Distributed Lag Modelling Approach to Cointegration Analysis,” in Centennial Volume of Ragner Frisch, ed by S Strom, A Holly, and P Diamond (Cambridge: Cambridge University Press) Phillips, Peter C.B., and Bruce Hansen, 1990, “Statistical Inference in Instrumental Variables Regression with I(1) Processes,” Review of Economic Studies, Vol 57 (January), pp 99–125 Phillips, Peter C.B., and Mico Loretan, 1991, “Estimating Long-run Economic Equilibria,” Review of Economic Studies, Vol 58 (May), pp 407–36 Riedel, James, 1984, “Trade as the Engine of Growth in Developing Countries, Revisited,” Economic Journal, Vol 94 (March), pp 56–73 Reinhart, Carmen, 1995, “Devaluation, Relative Prices, and International Trade,” Staff Papers, Vol 42 (June), pp 290–312 Rose, Andrew, 1990, “Exchange Rates and the Trade Balance: Some Evidence from Developing Countries,” Economic Letters, Vol 34 (November), pp 271–75 ———, 1991, “Role of Exchange Rates in a Popular Model of International Trade: Does the ‘Marshall-Lerner’ Condition Hold?” Journal of International Economics, Vol 30 (May), pp 301–16 Senhadji, Abdelhak, 1998, “Time Series Estimation of Structural Import Demand Equations: A Cross-Country Analysis,” Staff Papers, Vol 45 (June), pp 236–268 Senhadji, Abdelhak, and Claudio Montenegro, 1998, “Time Series Analysis of Export Demand Equations: A Cross-Country Analysis,” IMF Working Paper 98/149 (Washington: International Monetary Fund) 273 Exports: August-2005 ESTIMATING EXPORT EQUATIONS B Bhaskara Rao and Rup Singh University of the South Pacific, Suva (Fiji) Abstract Accurate estimates of the price and income elasticities of exports are valuable for growth policies based on trade promotion However, not sufficient attention seems to have been paid to the specification of the relative price variable in some influential empirical works This paper estimates the export equation for Fiji to show that inappropriate specification of the relative price variable may give under estimates of the price elasticity and over estimates of the income elasticity JEL: F14, F41, E21, C22; KEYWORDS: Exports, Price and Income Elasticities, Export-lead Growth Policy Exports: Fiji INTRODUCTION Estimating export demand functions for developing countries is important because the relative price and income elasticities indicate the scope for export-lead growth policies The higher are these elasticities, the higher is the scope for export based growth policies Therefore, it is important to avoid misspecification biases However, in the current empirical literature the specification of the export equations are unsatisfactory The usual specification for a country’s exports X in the log-liner form is: lnXt = α0 − α1 ln PD + α2 lnYF E × PF (1) where PD is domestic price of exports, PF is price level of trading partners, E is exchange rate, measured as the price of foreign currency in domestic currency and YF is income of trading partners Note that the relative price variable has three components viz., PD , E and (PF ) A 1% percent decline in relative prices could be due to a 1% decline in PD or a 1% increase in E (depreciation) or PF However, in some influential empirical works e.g., Senhadji and Montenegro (1999), E is ignored in the relative price variable This procedure seems to be widely used to quickly obtain expected empirical results with techniques like the fully modified OLS (FMOLS) of Phillips and Hansen (1990) and the bounds test (ARDL) of Pesaran and Shin (1995); see Senhadji (1998) and Dutta and Ahmed (2004) for ignoring E in imports Although the effects of omitting E on the estimated elasticities are difficult to estimate, it may be said that this could lead to an under estimation (over estimation) of the absolute value of price elasticity (α1 ) if devaluations (appreciations) dominate the sample It is hard to say how this ommission effects the estimate of income elasticity.1 This paper illustrates the A formal econometric proof is complicated because we have used non-linear estimation methods The approximate bias in OLS estimates can be computed by estimating three equations: (A) is the correct specification, (B) is the misspecified equation and (C) is an auxiliary regression: Exports: August-2005 aforesaid biases by estimating the export demand function for Fiji (1970-2002) We shall estimate the long run export equation, using the Phillips-Hansen FMOLS, the Johansen (1988) cointegration approach (JM L) and the LSE-Hendry general to specific approach (GETS); see Hendry (1987) We have ignored the bounds approach of Pesaran and Shin because of its large range of indeterminacy for the cointegration test statistic Although JM L gave plausible point estimates of these two elasticities, they were insignificant In comparison, both GETS and FMOLS gave good and significant estimates EMPIRICAL RESULTS The specifications of export equation, with and without E in the relative price variable, are: lnXt = α0 − α1 ln PD + α2 lnYF E × PF (2) PD + β2 lnYF PF (3) lnXt = β0 − β1 ln Definitions of the variables and sources of data are in the Appendix JML estimates of (2) and (3) are given in the first two columns of Table-1.2 The point estimates of the implied price and income elasPD + α2 lnYF E × PF (A) lnXt = β0 − β1 ln PD + β2 lnYF PF (B) lnPF = γ0 + γ1 ln PD + γ2 lnYF PF (C) lnXt = α0 − α1 ln Maddala (1992) shows that the expected values of the estimated coefficients from the misspecified equation (B) are: E(β1 ) = (1 + γ1 ) × α1 and E(β2 ) = (1 + γ2 ) × α2 Thus, the magnitude of this bias depends on the coefficients in the auxiliary equation We have used a VAR(1) model, after determining its optimal order with both AIC and SBC However, we faced problems with the Johansen JML procedure The null that there is at least one cointegrating vector for equation (2) could be Exports: Fiji ticities are correctly signed but in neither equation they are significant Exogeneity tests showed that both the relative price variables are weakly exogenous and the lagged ECM term is significant only at the 11.8% and 10% levels, respectively, in the VAR of equations for (2) and (3) These estimates, normalized on lnX, are reported as JML(2) and JML(3) They should be interpreted only as an indication that ignoring E, in the relative price variable, would lead to an under estimation of the absolute value of relative price elasticity, −2.578 against −1.357 and to an over estimation of income elasticity, 1.147 against 1.641 Using these insights, we have estimated equations (2) and (3) with GETS.3 The GETS estimates are in columns (3) and (4) of Table-1 The estimated elasticities, in both equations, are correctly signed and significant However, the point estimates of price elasticities show that it is only slightly higher in (3) but the null that these two estimates are equal could not be rejected at the 5% level of significance The computed test statistic is χ21 = 1.022 (p = 0.312) In contrast, there are significant difference in the estimates of income elasticities While equation (2) implies an income elasticity of 1.164, equation (3) gives an estimate of 1.652 The computed test statistic for the null that these are equal is χ21 = 15.683 (p = 0.000) and the null is rejected Estimates with FMOLS are in columns (5) and (6) Unlike the Johansen JML and GETS, FMOLS does not seem to have the option to restrict the intercept and trend in the underlying VAR Consequently, it became necessary to add a supply shock variable (SS) with a lag of two periods to get meaningful estimates of equation (2) SS is retained in equation (3) for comparable results.4 The estimated coefficients in both equations, except that of SS, are significant and have the expected signs It is noteworthy that, like in GETS, there accepted only at the 90% level by the trace test For equation (3) this null is accepted by both the eigenvalue and trace tests at the 95% level In the Johansen JML, it was necessary to use a restricted intercept in the VAR Therefore, we have estimated the GETS equations with NLLS to allow for this restriction However, the intercept was significant only in (2) SS is found be I(1) with a long lag structure of periods in the ADF test Exports: August-2005 Table Export Equations for Fiji Alternative estimates Variable JML(2) JML(3) PD ln E×P −2.578 F ln PPD F GETS(2) GETS(3) FMOLS(2) FMOLS(3) −0.862 −1.018 (2.56) (3.27) −1.357 lnYF 1.147 1.641 1.164 (9.44) Const 2.431 0.056 1.971 (3.75) SS −1.202 −1.024 (3.78) (4.74) 1.652 (64.48) 0.995 (8.88) 1.379 (15.88) 2.536 (4.37) 0.974 (1.99) −0.001 −0.001 (0.12) (0.16) Notes: The cointegrating vectors are normalized on lnX JML(2) means JML estimate of equation (2) etc t-ratios are in the parentheses t-ratios for the GETS equations are based on the Newey-West adjustment t-ratios for the JML equations in columns (1) and (2) are not reported because they are highly insignificant Microfit 4.1 of Pesaran and Pesaran (1997) is used for estimation is no significant differences in the estimated relative price elasticities of −1.018 against −1.024, but income elasticities are significantly different at 0.995 against 1.379 The test statistics for the null of no significant difference between them is χ2 = 11.710 [p = 0.001] and the null is rejected From these alternative estimates, it can be said that excluding the exchange rate in the relative price variable is perhaps less serious for the estimates of the price elasticity This may be partly due to the fact that depreciation regimes are relatively less dominant in our sample for Fiji The Fiji dollar was stable or appreciated in 19 out of 33 periods It would be interesting to know whether the price elasticity estimates would be close if depreciation regimes dominate in Exports: Fiji the samples of other developing countries However, in these alternative methods, the income elasticity is unequivocally overestimated by 40% to 65% when E is excluded in the relative price variable.5 Such a magnitude of overestimation gives the misleading implication that by liberalizing trade, a developing country can substantially increase exports if its trading partners grow faster What seems to be necessary for the success of an export-lead growth policy is a substantial reduction in the relative prices, either by decreasing domestic costs of exports or through timely adjustments to the exchange rate CONCLUSIONS In this paper we have empirically shown that neglecting the exchange rate in the relative price variable of the export equation causes an overestimation of the income elasticity Although it could not be conclusively shown that this leads to an underestimation of the relative price elasticity, because our sample is not dominated by depreciation regimes, we conjectured that underestimation is likely for countries in which depreciation regimes dominated the sample Therefore, we hope that our conjecture will be tested by other investigators A clear cut policy implication of our findings is that, since income elasticities are substantially overestimated in the existing empirical works that exclude the exchange rate in the relative prices, fresh estimates are necessary to determine the scope for export-driven growth policies It is also reasonable to conjecture that the relative price and income elasticities in the import equations will be similarly biased if the relative price variable is improperly specified Since these biases can be avoided by including the exchange rate in the relative price variable, the current popular practice of ignoring the exchange rate is unwarranted The insignificant bias in the relative price elasticity is due to a small value of 0.046 for γ1 in the auxiliary regression In contrast, the estimate of γ2 is 0.370 implying that income elasticity could be overestimated, in the misspecified equation, by about 40% See footnote Exports: August-2005 Data Appendix YF is the trade weighted average real income of Fiji’s major trading partners, viz., Australia, New Zealand, USA, UK/EU and Japan Trade weights are computed as the share of trade to each of these countries relative to Fiji’s total trade Xt is Fiji’s total exports of goods and services (FOB) deflated by the export price index PD is the price of Fiji’s export goods, computed as the weighted average of Fiji’s unit value index of major domestic exports Et is the weighted average exchange rate and is the price of a unit of foreign currency in domestic currency PF is the import weighted average of major trading partners’ export price indices Import weights are computed as the share of respective imports to total imports SS is a proxy for export productivity proxied with the average productivity of sugar per hectare from the Key Economics Statistics, various years Sugar is the major export of Fiji Notes: Data on the are from the IFS CD-ROM 2003 and the Reserve Bank of Fiji Quarterly Review, for various years Exports: Fiji References Dutta, D and Ahmed, N., (2004) “An aggregate import demand function for India: a cointegration analysis”, Applied Economics, pp.1-7 Hendry, D F., (1987) “Econometrics methodology: a personal perspective”, in T F Bewley (ed.) Advances in Econometrics Fifth World Congress, Vol.2 Cambridge: Cambridge University Press Johansen, S., (1988) “Statistical analysis of cointegrating vectors”, Journal of Economics Dynamics and Control, pp.231-254 Maddala, G S., (1992) Introduction to Econometrics, New York: Macmillan Publishing Company Pesaran, M H and Shin, Y., (1995) “An autoregressive distributed lag modeling approach to cointegration analysis”, in Storm, S , Holly, A and Diamond, P (eds.) Centennial Volume of Rangar Frisch, Econometric Society Monograph, Cambridge: Cambridge University Press Pesaran, M H and Pesaran, B., (1997) Working with Microfit 4.0, (Oxford: Oxford University Press) Phillips, P C B and Hansen, B (1990) “ Statistical inference in instrumental variables regressions with I(1) processes”, Review of Economic Studies, pp.99-125 Senhadji, A S and Montenegro, C E., (1999) “Time series analysis of export demand equations: a cross country analysis”, IMF Staff Papers, pp.259-273 —— & —— (1998) “Time series analysis of export demand equations: a cross country analysis”, IMF Working Paper 98/149.IMF Staff Papers, Washington: International Monetary Fund Senhadji, A S., (1998) “Time-series estimation of structural import demand equations: a cross-country analysis”, IMF Staff Papers, pp.236-268 ... có thực cải thiện cán cân thương mại Việt Nam? CHƢƠNG 1: KHUNG LÝ THUYẾT VỀ TÁC ĐỘNG CỦA TỶ GIÁ HỐI ĐOÁI ĐẾN CÁN CÂN THƢƠNG MẠI 1.1 Tỷ giá hối đoái: 1.1.1 Khái niệm: Tỷ giá hối đoái mối quan hệ... cứu tác động tỷ giá hối đoái đến cán cân thương mại Việt Nam Câu hỏi nghiên cứu: Khi tỷ giá hối đoái danh nghĩa tăng lên 1%, giá trị xuất nhập Việt Nam tăng giảm %? Việc gia tăng tỷ giá hối đoái. .. hướng cán cân thương mại Trong phần nghiên cứu kỹ yếu tố tác động đến cán cân thương mại Thơng thường, có hai yếu tố, là: yếu tố tỷ giá hối đoái yếu tố thu nhập 1.2.2.1 Yếu tố tỷ giá hối đoái Tỷ giá