Course Time Series using STATA (3 days)

Description

This course reviews methods for Time Series Analysis and shows how to perform the analysis using Stata. The course covers methods for data management, estimation, model selection, hypothesis testing, and interpretation. Exercises will supplement the lectures and Stata examples.

Participants profile

The course is open to graduate students, postdoctoral fellows as well as practising researchers a.o. economists, forecasters, financial analysts, managers, and anyone who encounters time series data.
Prior to the course, participants should have working knowledge of regression models and should be familiar with STATA. The course is open to a maximum of 20 participants.

Program content

Day 1 – Introduction: Basic Stata manipulations (Overview, very brief)

Time series data in Stata
Date/time in Stata
Setting data as time series data: tsset
User-written command (Wiggins and Baum, 2000): tsmktim
Subsetting periods: t-in/t-within
Merge and time series append: merge & tsappend
Time series operators
Lag and lead operators
Difference operator
Seasonal difference operator
Fill in gaps in time series variable: tsfill

Filters (tssmooth)

Intro: econometric theory on filters
Moving average filter: ma
Single exponential smoothing: exponential
Double exponential smoothing: dexponential
Holt-Winters (seasonal and non-seasonal) filters: hwinters & shwinters
Non-linear filters: nl

Regression analysis with time series data

Intro: econometric theory on regression analysis with time series data and autocorrelation
Basic regression analysis
Autocorrelation
The Durbin–Watson test: estat dwatson
Durbin’s alternative test for serial correlation: estat durbinalt
Breusch-Godfrey test for higher-order serial correlation: estat bgodfrey
Estimation with autocorrelated errors
The Newey-West covariance matrix estimator: newey
Cochrane-Orcutt and Prais-Winsten methods: prais
Lagged dependent variables as regressors
Dummy variables and additive seasonal effects

ARIMA Processes

Intro: econometric theory on ARIMA processes
Correlogram and partial correlogram: ac, pac & corrgram
Fitting ARIMA model: arima
ARIMA postestimation: predict
AIC and SBIC information criteria: estatic
Errors white noise?
Graphing the errors: tsline, scatter
Breusch-Godfrey test: estat bgodfrey
White noise test: wntestq

How to simulate an AR & MA process in Stata (optional)

Day 2

Unit-root test

Intro: econometric theory on unit-root tests
ADF test: dfuller
Phillips and Perron test: pperron
DFGLS test: dfgls

User-written unit-root tests (optional)

KPSS test (H0: series is stationary): kpss
In the presence of one structural break: zandrews
In the presence of more than one structural break: clemao2 & clemio2
Seasonal unit root test in Stata (HEGY test): sroot

Cointegration

Intro: econometric theory on cointegration
Engle and Granger
Johansen (see VAR)
Granger causality test (see VAR)

VAR (multivariate)

Intro: econometric theory on VAR
Lag order selection (lr test or information criteria): varsoc
Estimate var: var & varbasic
VAR postestimation: predict
Test whiteness of residuals
Graphing the errors: tsline, scatter
Ljung-Box Portmanteau (Q) test: wntstmvq
LM test for residual autocorrelation: varlmar
Test normality of residuals (the multivariate generalization of the Jarque-Bera test): varnorm
Test VAR stability: varstable
VAR forecasts and graphics: fcast compute & fcast graph
Granger causality test: vargranger

Day 3

VECM (multivariate)

Intro: econometric theory on VECM
Lag order selection (lr test or information criteria): varsoc
Rank of cointegration (Johansen’s test): vecrank
Estimate VECM: vec
VECM postestimation: predict
Test whiteness of residuals
Graphing the errors: tsline, scatter
Ljung-Box Portmanteau (Q) test: wntstmvq
LM test for residual autocorrelation: veclmar
Test normality of residuals (the multivariate generalization of the Jarque-Bera test): vecnorm
Test VECM stability: vecstable
VECM forecasts and graphics: fcast compute & fcast graph

Structural VAR (SVAR)

Intro: econometric theory on SVAR
Setting matrices: matrix
Estimate SVAR: svar
Constraints: constraint define

Impulse response functions (irf)

Intro: econometric theory on irfs
Create and analyze IRFs: irf create
Set the active IRF file: irf set
Describe an IRF file: irf describe
Graph IRFs: irf graph
Combine graphs of IRFs: irf cgraph
Graph overlaid IRFs: irf ograph
Create tables of IRFs: irf table
Combine tables of IRFs: irf ctable
Rename an IRF result in an IRF file: irf rename
Drop IRF results from the active IRF file: irf drop
Add results from an IRF file to the active IRF file: irf add

Schedule

This course is thaugt in-company only. Please contact us for more info

Course fee

Course fee: €2350, VAT-free

Location

Europalaan 400 | 3526 KS Utrecht

Language

English language is the working language of the course.

About the lecturer

Suncica Vujic joined the Department of Economics at the University of Bath as a Lecturer (Assistant Professor) in Economics in summer 2011. Suncica received a BSc degree from the Faculty of Economics, University of Belgrade, an MSc degree from the Economics Department at the Central European University (CEU) in Budapest, an MPhil degree from the Tinbergen Institute in Amsterdam, and a PhD degree from the Economics and Econometrics Departments at the VU University in Amsterdam.
Before joining the Department of Economics at the University of Bath, Suncica held positions at:

Department of Management at the London School of Economics (LSE)
Centre for Economic Performance (CEP) at the London School of Economics (LSE)
Netherlands Bureau for Economic Policy Analysis (CPB)
ORTEC Finance Consultancy in Amsterdam & Rotterdam
She also held lectureship positions at the VU University in Amsterdam and the University of Groningen in the Netherlands, and the University of Freiburg in Germany, where she taught applied econometrics, economics of education, and statistics courses.

She is affiliated with Tridata as a lecturer for STATA software, with applications to Time Series Analysis, Survival Analysis, and Panel Data Analysis.