| 000 | 00805camuu2200241 a 4500 | |
| 001 | 000045739035 | |
| 005 | 20130212213545 | |
| 008 | 130208s2013 txua b 001 0 eng | |
| 020 | ▼a 9781597181327 (pbk.) | |
| 020 | ▼a 1597181323 (pbk.) | |
| 035 | ▼a (KERIS)REF000017042958 | |
| 040 | ▼a DKDLA ▼b dan ▼c DKDLA ▼d OCLCO ▼d YDXCP ▼d 211009 | |
| 082 | 0 4 | ▼a 519.50285 ▼2 23 |
| 084 | ▼a 519.50285 ▼2 DDCK | |
| 090 | ▼a 519.50285 ▼b B396i | |
| 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 |
Holdings Information
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Main Library/Western Books/ | Call Number 519.50285 B396i | Accession No. 111687952 | Availability Available | Due Date | Make a Reservation | Service |
Contents information
Table of Contents
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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