| 000 | 00000nam u2200205 a 4500 | |
| 001 | 000046025720 | |
| 005 | 20200423163648 | |
| 008 | 200423s2020 txua b 001 0 eng | |
| 020 | ▼a 9781597183062 (pbk.) | |
| 020 | ▼a 1597183067 (pbk.) | |
| 040 | ▼a 211009 ▼c 211009 ▼d 211009 | |
| 082 | 0 4 | ▼a 519.50285 ▼2 23 |
| 084 | ▼a 519.50285 ▼2 DDCK | |
| 090 | ▼a 519.50285 ▼b B396i1 | |
| 100 | 1 | ▼a Becketti, Sean. |
| 245 | 1 0 | ▼a Introduction to time series using Stata / ▼c Sean Becketti. |
| 250 | ▼a Rev. ed. | |
| 260 | ▼a College Station, Tex. : ▼b Stata Press, ▼c c2020. | |
| 300 | ▼a xxv, 446 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 과학도서관/Sci-Info(2층서고)/ | 청구기호 519.50285 B396i1 | 등록번호 121253007 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
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 models Time series Filtering time-series data Preparing to analyze a time series 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 prestidigitations? 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 Model 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 times series Trend and unit roots Testing for unit roots Cointegration: Looking for a long-term relationship Cointegrating relationships and VECM From intuition to VECM: An example Points to remember Looking ahead Closing observations Making sense of it all What did we miss? Farewell References
