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Time Series Analysis and Modelling Course
November 27 @ 8:00 am - December 1 @ 5:00 pm| $1100
Venue: Indepth Research Services, Nairobi, Kenya
Event Date: 27th – 01st December 2017.
The course will show how economic and financial time series can be modeled and analyzed. The aim is to provide understanding and insight into the methods used, as well as explaining the technical details. Statistical modeling will be demonstrated using the Stata Software and participants will be given the opportunity to use Stata in class. Statistical modeling will be demonstrated using the Stata Software.
WHO SHOULD ATTEND?
Participants are expected to have attended the previous course on Data Management, Graphics and Statistical analysis using Stata or to be familiar with Stata software.
- Understand the definitions, features and objectives of time series modeling.
- Understand descriptive analysis of time series, plots, aggregation, smoothing and regression techniques.
- Understand and conduct periodic regression and ARIMA modeling using stationary time series.
- Using ARIMA modeling (Box & Jenkins), understand and use auto-correlation functions and partial auto-correlation functions to study how much an observation at a given time is related to observation at previous lags.
TOPICS TO BE COVERED
- Stationary time series
- Unobserved components and signal extraction.
- Time Series Models
- ARIMA models
- Structural time series models
- Explanatory variables and intervention analysis
- State space models and the Kalman filter.
- Signal extraction.
This training can also be customized for your institution upon request to a minimum of 4 participants. You can have it delivered in our training center or at a convenient location.
How to participate
View related courses
- Data Management and Analysis for Surveys using Stata Course
- Processing and Analysis of Data for Surveys/Assessment (Methodology and Software) Course
- Panel Data Analysis using Stata
- Advanced Excel Course
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For further inquiries, please contact us on Tel: +254 715 077 817, +254 (020) 211 3814, +254 731240802, +254 735331020.