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Introduction to time series using Stata

Introduction to time series using Stata (21회 대출)

자료유형
단행본
개인저자
Becketti, Sean.
서명 / 저자사항
Introduction to time series using Stata / Sean Becketti.
발행사항
College Station, Tex. :   Stata Press,   2013.  
형태사항
443 p. : ill. ; 24 cm.
ISBN
9781597181327 (pbk.) 1597181323 (pbk.)
서지주기
Includes bibliographical references and index.
일반주제명
Mathematical statistics -- Data processing.
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020 ▼a 9781597181327 (pbk.)
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100 1 ▼a Becketti, Sean.
245 1 0 ▼a Introduction to time series using Stata / ▼c Sean Becketti.
260 ▼a College Station, Tex. : ▼b Stata Press, ▼c 2013.
300 ▼a 443 p. : ▼b ill. ; ▼c 24 cm.
504 ▼a Includes bibliographical references and index.
650 0 ▼a Mathematical statistics ▼x Data processing.
945 ▼a KLPA

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고7층/ 청구기호 519.50285 B396i 등록번호 111687952 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차

Just enough Stata
Getting started
All about data
Looking at data
Statistics
Odds and ends
Making a date
Typing dates and date variables
Looking ahead

Just enough statistics
Random variables and their moments
Hypothesis tests
Linear regression
Multiple-equation modelsTime series

Filtering time-series data
Preparing to analyze a time series
Questions for all types of data
The four components of a time series
Some simple filters
Additional filters
Points to remember

A first pass at forecasting
Forecast fundamentals
Filters that forecast
Points to remember
Looking ahead

Autocorrelated disturbances
Autocorrelation
Regression models with autocorrelated disturbances
Testing for autocorrelation
Estimation with first-order autocorrelated data
Estimating the mortgage rate equation
Points to remember

Univariate time-series models
The general linear process
Lag polynomials: Notation or prestidigitation?
The ARMA model
Stationarity and invertibility
What can ARMA models do?
Points to remember
Looking ahead

Modeling a real-world time series
Getting ready to model a time series
The Box?Jenkins approach
Specifying an ARMA model
Estimation
Looking for trouble: Model diagnostic checking
Forecasting with ARIMA models
Comparing forecasts
Points to remember
What have we learned so far?
Looking ahead

Time-varying volatility
Examples of time-varying volatility
ARCH: A model of time-varying volatility
Extensions to the ARCH model
Points to remember

Models of multiple time series
Vector autoregressions
A VAR of the U.S. macroeconomy
Who’s on first?
SVARs
Points to remember
Looking ahead

Models of nonstationary time series
Trends and unit roots
Testing for unit roots
Cointegration: Looking for a long-term relationship
Cointegrating relationships and VECMs
Deterministic components in the VECM
From intuition to VECM: An example
Points to remember
Looking ahead

Closing observations
Making sense of it all
What did we miss?
Farewell


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