Introduction to Time Series Using Stata, Revised Edition

Author:   Sean Becketti
Publisher:   Stata Press
Edition:   2nd New edition
ISBN:  

9781597183062


Pages:   446
Publication Date:   02 March 2020
Format:   Paperback
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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Introduction to Time Series Using Stata, Revised Edition


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Full Product Details

Author:   Sean Becketti
Publisher:   Stata Press
Imprint:   Stata Press
Edition:   2nd New edition
Weight:   0.952kg
ISBN:  

9781597183062


ISBN 10:   1597183067
Pages:   446
Publication Date:   02 March 2020
Audience:   College/higher education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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 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

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Author Information

Sean Becketti is a financial industry veteran with three decades of experience in academics, government, and private industry. Over the last two decades, Becketti has led proprietary research teams at several leading financial firms, responsible for the models underlying the valuation, hedging, and relative value analysis of some of the largest fixed-income portfolios in the world.

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