[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-289734-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-289734-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","stat-gb6018-forecasting-time-series-data-course-syllabus","STAT-GB.6018 - Forecasting Time Series Data - Course Syllabus","","Course overview for STAT-GB.2302/STAT-GB.6018, focusing on practical time series forecasting. Emphasis is placed on the Box-Jenkins (ARIMA) framework and conditional volatility modeling using ARCH. Instruction combines hands-on computer-based data analyses with substantial discussion of the mathematical and statistical foundations. Grading is based mainly on data analysis homework and two projects (ARIMA mid-semester; ARCH end-of-semester), supported by weekly assignments, participation, and in-class attendance.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/exam/","Exam",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/stat-gb6018-forecasting-time-series-data-course-syllabus/289734/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/stat-gb6018-forecasting-time-series-data-course-syllabus/289734.png","ImageObject",300,407,{"name":42,"@type":43},"Maeve","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-20","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",8,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What forecasting methods are emphasized in the course?","Question",{"text":63,"@type":64},"The course emphasizes the Box-Jenkins (ARIMA) method and conditional volatility models using ARCH, with illustrative examples and applied data analyses.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How is the final grade determined?",{"text":68,"@type":64},"Weekly homework counts for 30%, and there are two data analysis projects counting for 30% each. 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Hurvich  \nOffice Hours: Wed, 12:30-1:30 (Zoom) .  \ne-Mail: [cmh1@stern.nyu.edu](cmh1@stern.nyu.edu)  \n[Course Website:](Course Website: people.stern.nyu.edu/churvich)[ people.stern.nyu.edu/churvich](Course Website: people.stern.nyu.edu/churvich) (click on \"Forecasting\").  \nTA Info: Wenbo Jing ([wj2093@stern.nyu.edu](wj2093@stern.nyu.edu)), TBA  \nCourse Philosophy: This course will cover practical time series forecasting techniques with particular emphasis on the Box-Jenkins (ARIMA) method, and conditional volatility (ARCH) models. Illustrative examples applying these techniques to actual data (primarily financial and economic time series) will be presented in class, and you will perform a variety of data analyses on the computer. To gain a deeper understanding of how the methods work, we will also spend a considerable amount of class time discussing their mathematical/statistical underpinnings. However, most of your grade will be based on data analysis homework problems and projects.  \nThe level of presentation will be somewhere between MBA and MS. MBA students should have no problem handling the data analysis aspects of the course, but will probably find the theory challenging at times. Ph.D. students should also be able to profit from this course, if they want to learn basic forecasting methodology presented at what (for them) should be a rather comfortable mathematical level.  \nThis is a statistics course, and therefore we will not attempt to delve deeply into economic issues. We are concerned here with the statistical analysis and forecasting of time series. No background in economics is required for this course. On the other hand, statistical analysis of economic data, which you will be doing, does form a part of econometrics, and hopefully will often lead to insights of an economic nature.  \n-2-  \nPrerequisites: One introductory level statistics course covering random variables, expected value, the normal distribution, conditional probability, hypothesis testing, confidence intervals, p-values, correlation and linear regression. It is particularly important that you have some previous exposure to linear regression, because much of the material presented in this course (including the ARMA and ARCH models) is essentially an outgrowth of linear regression. Furthermore, linear regression itself provides a method of forecasting, through trend-line fitting.  \nCourse Work:  \nThere will be weekly homework assignments (data analysis and theory), which count for 30% of the grade. Some of the problems will be demanding, and I will be happy to help you if you run into difficulties. You are free to work with others on homework problems, but it will be assumed that you understand what you have submitted. For teams working together on an entire homework assignment, the team should submit just one file with evereyone’s name on it. There will be two data analysis projects, each of which counts for 30% of the grade. In the first, which will be due in the middle of the semester, you will analyze a data set of your choice using ARIMA methods. In the second, due at the end of the semester, you will analyze a data set of your choice using ARCH methods. The TA and I will be happy to discuss your projects with you before you hand them in, but beyond this please keep in mind that the projects are individual work. You need to get your own data set and provide a web link to the data. In-class attendance counts for 5% of the course grade. The other 5% of the course grade is for in-class participation. To receive the full five points for participation you should typically contribute at least one question / comment per class meeting. All assignments (both homework and projects) must be in pdf format, and should be submitted on Brightspace.  \nAll handouts and other course materials are available online. Much of the material in the text will not be covered. On the other hand, some of the mate","cbCaiiaZhnp2Lnbf","https://ap.wps.com/l/cbCaiiaZhnp2Lnbf","pdf",14963,"English","# Course Philosophy\n## Presentation Level\n## Course Scope and Background\n# Prerequisites\n# Course Work and Grading\n## Homework Assignments\n## Data Analysis Projects\n## Participation and Attendance\n# Optional Textbooks","[{\"question\":\"What forecasting methods are emphasized in the course?\",\"answer\":\"The course emphasizes the Box-Jenkins (ARIMA) method and conditional volatility models using ARCH, with illustrative examples and applied data analyses.\"},{\"question\":\"How is the final grade determined?\",\"answer\":\"Weekly homework counts for 30%, and there are two data analysis projects counting for 30% each. In-class attendance and participation account for the remaining 10%.\"},{\"question\":\"What are the key prerequisites before taking this course?\",\"answer\":\"Students need an introductory statistics background covering random variables, expected value, the normal distribution, conditional probability, hypothesis testing, confidence intervals, p-values, correlation, and linear regression, with prior exposure to linear regression considered especially important.\"}]","STAT-GB.6018 - Forecasting Time Series Data - Course Syllabus | PDF",15]