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New York USA » Jan 2015 » Stata » Macroeconomics » Forecasting » Finance » Bookmark and Share

Introduction to Time Series Analysis & Forecasting using Stata

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Course Code:
12-14 January 2015
New York, USA
3-days
Stata
Forecasting / Macroeconomics / Finance (Introductory)
Prof. Robert A. Yaffee, New York University (View profile)
ST-131-USA
Baruch College - The City University of New York (CUNY) Stata  Stata 13    Timberlake Consultants | Statistics | Econometrics | Forecasting
Overview Agenda Prerequisites Testimonials Prices Registration Terms & Conditions  

Overview

This course, taking place in New York on 12-14 January 2015, assumes little mathematical background on the part of the participants. The course shows how to apply these techniques to real-life social science, economic, business, financial, and medical data, with many examples on the reporting and interpreting of the results. Participants are welcome to bring their own data.

The course is aimed at students, researchers, and forecasters interested in:

  • Basic Stata;
  • Basic cross-sectional statistics with Stata;
  • Longitudinal analysis with Stata;
  • Box-Jenkins Time Series Analysis with Stata:
    • Seasonal Box-Jenkins Models;
    • Forecasting with time series models;
    • Forecasting evaluation.

 

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Agenda

Day 1

8:30am coffee and Registration

Introduction to Stata

  • Configuration of Stata;
  • Free data sources;
  • Variable construction ( including date and time variables, etc.);
  • Variable transformations;
  • Missing value management;
  • Codebooks;
  • Dataset construction: cross-sectional, time series;
  • File management (appending and merging).

Item analysis and Scale construction

  • Reliability and validity analysis.

Data cleaning

  • Range and consistency checks;
  • File comparison.

Exploratory graphical visualization

  • Histograms, stem-and-leaf plots, bar graphs, dot-plots, line graphs, scatterplots, pie charts, reference lines, and annotation.

Research Project planning

  • Power and sample size analysis;
  • Hypothesis testing.

Summary statistics for sample description
Categorical data analysis

  • Tabulations;
  • Cross-tabulations.

T-tests
ANOVA

  • Assumptions and tests for them;
  • One-way ANOVA;
  • Two-way ANOVA.

Regression analysis

  • OLS;
  • Assumptions and tests for them;
  • Modeling strategies and critiques;
  • General-to-specific, Hierarchical, All possible subsets and Bootstrapping.

Robust regression

  • Heteroskedastically consistent estimation;
  • Outlier down-weighting.

Day 2

Basic Time Series Analysis Concepts

  • Definition of a time series;
  • Cycles;
  • Trends;
  • Seasonality;
  • Lags, leads, differences;
  • Nomenclature;
  • Expectation notation;
  • Summation notation.

Time Series Setup with Stata

  • Inputting time series data;
  • Time-date functions and applications;
  • Importing and exporting time series data;
  • Graphing Time Series with Stata;
  • Preliminary analysis of time series with Stata.

Stationarity

  • Covariance stationarity;
  • Strict stationarity;
  • Dickey Fuller tests theory;
  • Augmented Dickey-Fuller tests:
    • Theory programming.
  • Phillips-Perron test.

Autocorrelation

  • Theory;
  • Types;
  • Characteristic ACF and PACF patterns;
  • Programming the correlograms;
  • Box-Ljung significance tests.

Moving averages

  • Theory;
  • Types;
  • Characteristic ACF and PACF patterns;
  • Programming the ACF and PACF;
  • White noise Significance tests.

Hands-On - Experience and Programming practice

  • Stationarity diagnosis and transformations;
  • ARIMA identification:
    • Integrated processes;
    • AR processes;
    • MA processes;
    • ARMA processes.

Day 3

ARIMA modeling

  • Estimation;
  • Estimation algorithms:
    • Full maximum likelihood;
    • Conditional maximum likelihood.
  • Diagnosis;
  • Intervention modeling;
  • Model fitting.

Seasonal ARIMA models

  • Identification;
  • Estimation;
  • Diagnosis;
  • Model fitting.

Forecasting theory

  • Sample segmentation;
  • Segment lengths;
  • In-sample v. post-sample forecasting;
  • Point forecasts;
  • Interval forecasts;
  • Forecast profiles;
  • Out-of-sample forecasts;
  • Ex ante forecasts;
  • One-step ahead forecasts;
  • Dynamic forecasts;
  • Structural forecasts;
  • Combining forecasts.

Forecasting Evaluation

  • Tests of forecast bias;
  • Tests of forecast accuracy: out-of-sample and ex-ante;
  • MSFE;
  • MAE;
  • MAPE;
  • MdAPE;
  • Theils U;
  • Diebold-Mariano test of comparative forecast evaluation.

Forecasting Graphics

Hands on ARIMA modeling and forecasting

Learning Ratio

This course has a learning ratio of approximately 50% practical to 50% theory, dependent on the knowledge of attending delegates.

 

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Prerequisites

  • Required: High School Algebra; Basic Statistics
  • Recommended: Linear or Matrix Algebra; Basic differential & integral calculus

 

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Feedback & Testimonials

Coming soon.

 

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Prices

Cost (per participant):

Registration type Price
Commercial / Government $2,175.00
Academic / Non-profit research $1,695.00
Student registrations $735.00
Click here for Pricing FAQs »
  • All costs exclude local taxes, where applicable.
  • Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrollment).
  • Additional discounts are available for multiple registrations.
  • Cost includes course materials, lunch and refreshments.
  • If you need assistance in locating hotel accommodation in the region, please notify us at the time of booking.

The number of delegates is restricted. Please register early to guarantee your place.

 

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Registration

We welcome delegates to find out more and register for the course by contacting our sales and training team either by email: info@timberlake-consultancy.com, phone: +1 908 686 1251 or by filling out an online registration form.

 

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Terms & Conditions

For full Training Courses Terms & Conditions please click here.

Payment of course fees required prior to the course start date.

Registration closes 5-calendar days prior to the start of the course.

  • 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
  • 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
  • No fee returned for cancellations made less than 14-calendar days prior to the start of the course.



 

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Last modified: 2014-06-12 16:24:32
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