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

Time Series Analysis & Forecasting Using Stata

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Course Code:
13-16 January 2015
Manhattan GMAT Center Location
138 West 25th St. (b/w 6th & 7th Ave)
New York, NY 10001 (View map)
4-days
Stata
Forecasting / Macroeconomics / Finance (Introductory)
Prof. Robert A. Yaffee, New York University (View profile)
ST-132-USA
  Stata  Stata 13    Timberlake Consultants | Statistics | Econometrics | Forecasting
Overview Agenda Prerequisites Testimonials Prices Registration Terms & Conditions  

Overview

This four-day course, taking place in New York on 13-16 January 2015, is combined with the Introduction to Time Series Analysis using Stata course, allowing the 1-day introductory part of the course to be taken by course delegates separately to the core Time Series Analysis and Forecasting using Stata course.

The course 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;
  • Outlier modeling;
  • Dynamic regression analysis with Stata;
  • GARCH modeling with Stata;
  • Forecasting evaluation;
  • Policy and impact analysis wish Stata;
  • Financial risk analysis with Stata.

 

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Agenda

(Subject to minor changes)

Day 1

Morning Session

Basic Time Series Analysis Concepts

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

Break

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.

Lunch

Afternoon Session

Stationarity

  • Covariance stationarity;
  • Strict stationarity;
  • Dickey Fuller tests:
    • Theory;
    • Programming dfuller tests.
  • Augmented Dickey-Fuller tests:
    • Theory;
    • Programming.
  • Phillips-Perron tests.

Autocorrelation

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

Break

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 2

Morning Session

ARIMA modeling

  • Estimation;
  • Estimation algorithms:
    • full maximum likelihood;
    • conditional maximum likelihood.
  • Diagnosis;
  • Intervention modeling;
  • Model fitting.

Break

Seasonal ARIMA models

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

Lunch

Afternoon Session

Forecasting theory

  • sample segmentation;
  • segment lengths;
  • in-sample vs. 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.

Break

Forecasting Evaluation

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

Forecasting Graphics

Hands on ARIMA modeling and forecasting

Day 3

Morning Session

Intervention (Impact) Analysis

  • Pulse interventions;
  • Level Shifts;
  • Testing for them.

Break

Outliers

  • Additive;
  • Periodic Pulses;
  • Innovational;
  • Patches;
  • Modeling outliers.

Intervention modeling with Arimacheck

Hands-on programming

Lunch

Afternoon Session

Dynamic Regression Models with Stata

  • Impulse Response functions:
    • Deterministic inputs;
    • Stochastic inputs.
  • Dynamic Regression Analysis Linear Transfer Function methodology;
  • Dynamic Regression modeling with Arimacheck;
  • Forecasting with Dynamic Regression Models:
    • Out-of-sample;
    • Ex ante.

Break

Cointegration

  • Exogeneity;
  • Granger causality;
  • Tests for exogeneity;
  • Error Correction models.

Q and A

Day 4

Morning Session

Autoregressive Error Models

  • First order correction theory: Cochran-Orcutt:
    • Prais-Winston models;
    • Newey-West robust models;
    • Regression diagnostics:
      • Autocorrelation tests;
      • Heteroskedasticity tests;
      • Parameter constancy tests.

Q and A

Hands-on programming

Robust time series analysis

  • Semi-robust time series analysis;
  • Robust time series models;
  • Robust time series with Arimacheck.

Lunch

GARCH models: Theory and programming

  • ARCH;
  • Forecasting with ARCH;
  • GARCH;
  • Forecasting with GARCH;
  • Forecast Evaluation with GARCH Forecasts.

Out-of-sample

  • ex ante
    • EGARCH;
    • modeling leverage effects;
    • Volatility smiles and skews;
    • Graphing.
  • Modeling
    • GJR Threshold GARCH;
    • APGARCH.

Break

Recapitulation

Q and A

Hands-on programming

Subjects to be covered if time permits

Vector Autoregression

  • Definition;
  • Assumptions;
  • Tests for assumptions;
  • Estimation;
  • Forecasting;
  • Structural VAR;
  • Orthogonal impulse response functions;
  • Forecast error variance decomposition;
  • Constraints;
  • Varbasic, var, and svar programming.

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

  • High School Algebra;
  • Basic Statistics.

 

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Feedback & Testimonials

Click here to view Prof. Robert A. Yaffee’s full profile »

 

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Prices

Cost (per participant):

Registration type Price
Commercial / Government $2,900.00
Commercial / Government - Attend all 5-days
(Includes attendance at Introduction to Time Series Analysis)
$3,625.00
Academic / Non-profit research $2,260.00
Academic / Non-profit research - Attend all 5 days $2,825.00
Student Registrations $980.00
Student Registrations - Attend all 5 days $1,225.00
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: 2014-12-12 14:55:47
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