NC461

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Univariate Time Series with Stata

Pelajari tentang analisis univariate time-series dengan penekanan pada aspek praktis yang paling dibutuhkan oleh praktisi dan pakar peneliti. Ditulis untuk beragam pengguna, termasuk ekonom, peramal (forecasters), analis keuangan, manajer, dan siapa saja yang ingin menganalisis data time-series. Menjadi ahli dalam menangani date and date – data waktu, operator time-series, grafik time-series, metode perkiraan dasar, ARIMA, ARMAX, dan seasonal models.

Kami menyediakan materi pelajaran, jawaban terperinci untuk pertanyaan yang diposting di akhir setiap pelajaran, dan akses ke papan diskusi tempat Anda dapat mengirim pertanyaan untuk siswa lain atau pengajar untuk dijawab.

Periode kursus berikutnya:

Oct 10, 2019

to

Nov 21, 2019

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Lesson 1: Introduction

• Course outline

• Follow along

• What is so special about time-series analysis?

• Time-series data in Stata

  • The basics

  • Clocktime data

• Time-series operators

  • The lag operator

  • The difference operator

  • The seasonal difference operator

  • Combining time-series operators

  • Working with time-series operators

  • Parentheses in time-series expressions

  • Percentage changes

• Drawing graphs

• Basic smoothing and forecasting techniques

  • Four components of a time series

  • Moving averages

  • Exponential smoothing

  • Holt–Winters forecasting

Lesson 2: Descriptive analysis of time series

• Course outline

• Follow along

• What is so special about time-series analysis?

• Time-series data in Stata

  • The basics

  • Clocktime data

• Time-series operators

  • The lag operator

  • The difference operator

  • The seasonal difference operator

  • Combining time-series operators

  • Working with time-series operators

  • Parentheses in time-series expressions

  • Percentage changes

• Drawing graphs

• Basic smoothing and forecasting techniques

  • Four components of a time series

  • Moving averages

  • Exponential smoothing

  • Holt–Winters forecasting

Lesson 3: Forecasting II: ARIMA and ARMAX models

• Basic ideas

  • Forecasting

  • Two goodness-of-fit criteria

  • More on choosing the number of AR and MA terms

• Seasonal ARIMA models

  • Additive seasonality

  • Multiplicative seasonality

• ARMAX models

• Intervention analysis and outliers

• Final remarks on ARIMA models

Lesson 4: Regression analysis of time-series data

• Basic regression analysis

• Autocorrelation

  • The Durbin–Watson test

  • Other tests for autocorrelation

• Estimation with autocorrelated errors

  • The Newey–West covariance matrix estimator

  • ARMAX estimation

  • Cochrane–Orcutt and Prais–Winsten methods

• Lagged dependent variables as regressors

• Dummy variables and additive seasonal effects

• Nonstationary series and OLS regression

  • Unit-root processes

• ARCH

  • A simple ARCH model

  • Testing for ARCH

  • GARCH models

  • Extensions

Bonus lesson: Overview of multivariate time-series analysis using Stata

• VARs

  • The VAR(p) model

  • Lag-order selection

  • Diagnostics

  • Granger causality

  • Forecasting

  • Impulse–response functions

  • Orthogonalized IRFs

  • VARX models

• VECMs

  • A basic VECM

  • Fitting a VECM in Stata

  • Impulse–response analysis

Course pre-requisites

• Stata 15 installed and working

• Course content of NetCourse 101 or equivalent knowledge

• Familiarity with basic cross-sectional summary statistics and linear regression

• Internet web browser, installed and working
  (course is platform independent)