Introduction to Time Series Using Stata

1. Auflage 2013
ISBN: 978-1-59718-132-7
Verlag: Stata Press

Buch, Englisch, 446 Seiten, Format (B × H): 184 mm x 238 mm, Gewicht: 934 g

Introduction to Time Series Using Stata, Revised Edition provides a step-by-step guide to essential time-series techniques–from the incredibly simple to the quite complex– and, at the same time, demonstrates how these techniques can be applied in the Stata statistical package. The emphasis is on an understanding of the intuition underlying theoretical innovations and an ability to apply them. Real-world examples illustrate the application of each concept as it is introduced, and care is taken to highlight the pitfalls, as well as the power, of each new tool. The Revised Edition has been updated for Stata 16.

Autoren/Hrsg.

Becketti, Sean

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Mathematik | Informatik Mathematik Mathematik Interdisziplinär Finanz- und Versicherungsmathematik

Inhaltsverzeichnis

Just enough Stata Getting startedAll about dataLooking at dataStatisticsOdds and endsMaking a dateTyping dates and date variablesLooking ahead

Just enough statistics Random variables and their momentsHypothesis testsLinear regressionMultiple-equation modelsTime series

Filtering time-series dataPreparing to analyze a time seriesThe four components of a time seriesSome simple filtersAdditional filtersPoints to remember

A first pass at forecastingForecast fundamentalsFilters that forecastPoints to rememberLooking ahead

Autocorrelated disturbancesAutocorrelationRegression models with autocorrelated disturbancesTesting for autocorrelationEstimation with first-order autocorrelated dataEstimating the mortgage rate equation Points to remember

Univariate time-series modelsThe general linear processLag polynomials: Notation or prestidigitations?The ARMA modelStationarity and invertibilityWhat can ARMA models do?Points to rememberLooking ahead

Modeling a real-world time seriesGetting ready to model a time seriesThe Box-Jenkins approachSpecifying an ARMA modelEstimationLooking for trouble: Model diagnostic checkingForecasting with ARIMA modelsComparing forecastsPoints to rememberWhat have we learned so far?Looking ahead

Time-varying volatilityExamples of time-varying volatilityARCH: A model of time-varying volatility Extensions to the ARCH modelPoints to remember

Model of multiple time seriesVector autoregressionsA VAR of the U.S. macroeconomyWho’s on first?SVARsPoints to rememberLooking ahead

Models of nonstationary times seriesTrend and unit rootsTesting for unit rootsCointegration: Looking for a long-term relationshipCointegrating relationships and VECMFrom intuition to VECM: An examplePoints to rememberLooking ahead

Closing observationsMaking sense of it allWhat did we miss?Farewell

References

Über Autor(innen)

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.