BÁO CÁO MÔN KINH TẾ LƯỢNG ỨNG DỤNG MÔ HÌNH ARIMA TRONG DỰ BÁO GIÁ DẦU THÔ THẾ GIỚI

... BÁO CÁO MÔN KINH TẾ LƯỢNG ỨNG DỤNG MÔ HÌNH ARIMA TRONG DỰ BÁO GIÁ DẦU THÔ THẾ GIỚI Sinh viên thực hiện: Đặng Thị Thu HiềnMã sinh viên: CQ500927 Giới thiệu về mô hình Arima Trong ... Xây dựng mô hình Arima cho giá dầu thô thế giới 1. giới thiệu về số liệu:Nguồn cập nhật số liệu là từ trang web www.eia.gov là trang web của cục năng lượng Hoa Kỳ.Số liệu là giá dầu thô thế giới ... nắm bắt tình hình biến động đưa ra các chính sách phù hợp. Trong khuôn khổ đề tài, chúng tôi đề xuất sử dụng mô hình ARIMA và phương pháp Box-jenkins để dự báo giá dầu thô thế giới trong ngắn...

... ARIMA là thành công của nó trong dự báo. Trong một số trường hợp dự báo thu được từ phương pháp này có tính tin cậy cao hơn so với các dự báo thu được từ các phương pháp lập mô hình kinh tế ... hình kinh tế lượng truyền thống khác, đặc biệt là đối với dự báo ngắn hạn. Dựa vào mô hình ARIMA ước lượng được, tiến hành xác định giá trị dự báo và khoảng tin cậy cho dự báo với độ tin ... một mô hình đúng, mô hình có AIC (Akaike Information Criterion) nhỏ nhất sẽ được lựa chọn. 2.2.5. Dự báo bằng mô hình ARIMA Một trong số các lý do về tính phổ biến của phương pháp lập mô hình...

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Ử DỤNG MÔ HÌNHSỬ DỤNG MÔ HÌNH ARIMA ARIMA TRONG DỰ BÁO CHUỖI THỜI GIANTRONG DỰ BÁO CHUỖI THỜI GIANCAO HÀO THI2NỘI DUNGNỘI DUNG Giới thiệu xây dựng Mô Hình ARIMA (Auto-Regressive ... Trượt Ứng dụng dự báo giá cá sông tại Tp. HCM 3GIỚI THIỆU GIỚI THIỆU  Mô hình nhân quả Mô hình chuỗi thời gian Hai loại mô hình dự báo chính: 4 Đối với các chuỗi thời gian ARIMA thường ... sử dụng để dự báo  Theo mô hình ARIMA, giá trị dự báo sẽ phụ thuộc vào các giá trị quá khứ và tổng có trọng số các nhiễu ngẫu nhiên hiện hành và các nhiễu ngẫu nhiên có độ trễ5MÔ HÌNH ARIMA...

... 7.401456985 ) tương ứng với mức ý nghĩa là trong phân tích phương sai ANOVA sẽ cho ta thấy mô hình hồi qui tuyến tính phù hợp với mô hình nghiên cứu. Do vậy giá trị ước lượng được đưa vào phương ... phí tiền mặt- Doanh thu: Tổng giá trị sản lượng thu hoạch trong năm, được tính từsản lượng sản phẩm nhân với đơn giá sản phẩm đó.Doanh thu = Số lượng * Đơn giá - Lợi nhuận: Là phần chênh lệch ... và bằng PP bình phương tối thiểu. Từ kết quả của mô hình hãy cho kết luận về hiệu suất theo quy mô * Hàm sản xuất Cobb-Douglas của mô hình: Sản lượng mía thu họach được từ việc sản xuất phụ thuộc...

... kinh tế là một họat động sản xuất kinh doanh chủ yếu đề cập đến lợi ích kinh tế sẽ thu được trong hoạt động đó. Hiệu quả kinh tế là một phạm trù phản ánh chất lượng của các hoạt động kinh tế. ... thuyết H0 nghĩa là mô hình hồi qui xây dựng có tác dụng trong việc dự đoán, mô tả biến phụ thuộc (năng suất mía) 4. Tính sai số chuẩn của ước lượng Sai số chuẩn của ước lượng đo lường sự biến ... lý kinh tế, đánh giá nhằm nâng cao chất lượng của các hoạt động kinh tế đã làm xuất hiện phạm trù hiệu quả kinh tế 4. Lợi nhuận là khoản chênh lệch giữa doanh thu và chi phí cơ hội được sử dụng...

... – P Trong đó: Y - độ sâu dòng chảy; X - lượng mưa; P - tổn thất - Các mô hình tính lưu lượng dòng chảy mặt. - Các mô hình tính lưu lượng dòng chảy ngầm. - Các mô hình truyền lũ trong ... dự báo lưu lượng lũ xảy ra ở thượng nguồn để làm cơ sở tính toán cho các mô hình thủy lực là rất cần thiết. Trong bài viết nầy chúng tôi đã nghiên cứu ứng dụng phần mền Hec-Hms để mô hình ... chọn mô hình này để áp dụng tính toán dòng chảy tại các biên của mô hình thuỷ lực. 2. Nội dung 2.1. Cơ sở lí thuyết của mô hình Hec-hms Trình tự tính toán theo các bước: - Các mô hình...

... và dự báo bằng các mô hình số trị, bao gồm cả mô hình khí hậu toàn cầu và mô hình khí hậu khu vực. Trước khi các mô hình số trị được ứng dụng rộng rãi, phương pháp thống kê đã được sử dụng ... kỷ 20. ý tưởng hình thành những mô hình này bắt nguồn từ việc cải tiến các mô hình dự báo thời tiết qui mô vừa cho mục đích mô phỏng các trường khí hậu quá khứ, trong đó mô hình khu vực được ... (nest) vào một mô hình toàn cầu nào đó [5-7]. Trong số các mô hình khí hậu toàn cầu dự báo hạn mùa đáng chú ý là mô hình CFS (The NCEP Climate Forecast System) [8]. Đây là hệ thống mô hình kết...

... Tuy vậy, mô hình ARIMA có thể dùng để dự báo, song chưa phải là tối ưu, bởi vì sự phụ thuộc trong mô hình được giả định là tuyến tính. Từ khóa: ARIMA, dự báo, khách du lịch quốc tế. SUMMARY ... sử dụng phương pháp Box-Jenkins để xây dựng mô hình ARIMA cho dự báo lượng khách quốc tế đến Việt Nam dựa trên số liệu công bố hàng tháng của Tổng cục Du lịch Việt Nam. Kết quả cho thấy trong ... DTOURISTARRIVAL là chuỗi dừng. 3.2. Xây dựng mô hình ARIMA cho chuỗi biến động lượng khách quốc tế đến Việt Nam Để xây dựng mô hình ARIMA chúng tôi sử dụng chuỗi dữ liệu gồm 192 quan sát từ...

... )3312)()(+njjjnjjtSetSejChơng3 :Mô hình hồi qui bội 1. Hồi qui bội 2. Ước l ợng các tham số trong mô hình hồi qui ba biến 3. Hệ số xác định trong mô hình hồi quy bội4. Khoảng tin cậy và kiểm định giả thuyết trong mô ... hiệu:RSSR: tổng bình ph ơng phần d từ mô hình hồi qui đã thu hẹp (mô hình có điều kiện ràng buộc - R)RSSUR : là tổng bình ph ơng phần d từ mô hình hồi qui ban đầu (mô hình không có điều kiện ràng ... quy bội4. Khoảng tin cậy và kiểm định giả thuyết trong mô hình hồi qui ba biến5. Một số dạng của hàm hồi qui6. Dự đoán với mô hình hồi qui bội KiÓm ®Þnh gi¶ thuyÕt ®èi víi •§Ó kiÓm ®Þnh...