Introduction to Time Series Using Stata

ISBN-10: 1597181323

ISBN-13: 9781597181327

Edition: 2013

Authors: Sean Becketti
List price: $82.95
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Description: Recent decades have witnessed explosive growth in new and powerful tools for timeseries analysis. These innovations have overturned older approaches to forecasting, macroeconomic policy analysis, the study of productivity and long-run economic growth, and the trading of financial assets. Familiarity with these new tools on time series is an essential skill for statisticians, econometricians, and applied researchers.Introduction to Time Series Using Stataprovides a step-by-step guide to essential timeseries 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.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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Book details

List price: $82.95
Copyright year: 2013
Publisher: StataCorp LP
Publication date: 1/2/2013
Binding: Paperback
Pages: 741
Size: 7.25" wide x 9.00" long x 1.00" tall
Weight: 1.848
Language: English

List of tables
List of figures
Just enough Stata
Getting started
Action first, explanation later
Now some explanation
Navigating the interface
The gestalt of Stata
The parts of Stata speech
All about data
Looking at data
Odds and ends
Making a date
How to look good
Typing dates and date variables
Looking ahead
Just enough statistics
Random variables and their moments
Hypothesis tests
Linear regression
Ordinary least squares
Instrumental variables
Multiple-equation models
Time series
White noise, autocorrelation, and stationarity
ARMA models
Filtering time-series data
Preparing to analyze a time series
Questions for all types of data
How are the variables defined?
What is the relationship between the data and the phenomenon of interest?
Who compiled the data?
What processes generated the data?
Questions specifically for time-series data
What is the frequency of measurement?
Are the data seasonally adjusted?
Are the data revised?
The four components of a time series
Some simple filters
Smoothing a trend
Smoothing a cycle
Smoothing a seasonal pattern
Smoothing real data
Additional filters
ma: Weighted moving averages
exponential: EWMAs
dexponential: Double-exponential moving averages
Holt-Winters smoothers
hwinters: Holt-Winters smoothers without a seasonal component
shwinters: Holt-Winters smoothers including a seasonal component
Points to remember
A first pass at forecasting
Forecast fundamentals
Types of forecasts
Measuring the quality of a forecast
Elements of a forecast
Filters that forecast
Forecasts based on EWMAs
Forecasting a trending series with a seasonal component
Points to remember
Looking ahead
Autocorrelated disturbances
Example: Mortgage rates
Regression models with autocorrelated disturbances
First-order autocorrelation
Example: Mortgage rates (cont.)
Testing for autocorrelation
Other tests
Estimation with first-order autocorrelated data
Model 1: Strictly exogenous regressors and autocorrelated disturbances
The OLS strategy
The transformation strategy
The FGLS strategy
Comparison of estimates of model 1
Model 2: A lagged dependent variable and i.i.d. errors
Model 3: A lagged dependent variable with AR(1) errors
The transformation strategy
The IV strategy
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
Step 1: Induce stationarity (ARMA becomes ARIMA)
Step 2: Mind your p's and q's
Looking for trouble: Model diagnostic checking
Tests of the residuals
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
GARCH: Limiting the order of the model
Other extensions
Asymmetric responses to "news"
Variations in volatility affect the mean of the observable series
Nonnormal errors
Odds and ends
Points to remember
Models of multiple time series
Vector autoregressions
Three types of VARs
A VAR of the U.S. macroeconomy
Using Stata to estimate a reduced-form VAR
Testing a VAR for stationarity
Other tests
Evaluating a VAR forecast
Who's on first?
Cross correlations
Summarizing temporal relationships in a VAR
Granger causality
How to impose order
Using Stata to calculate TRFs and FEVDs
Examples of a short-run SVAR
Examples of a long-run SVAR
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
Confirm the unit root
Identify the number of lags
Identify the number of cointegrating relationships
Fit a VECM
Test for stability and white-noise residuals
Review the model implications for reasonableness
Points to remember
Looking ahead
Closing observations
Making sense of it all
What did we miss?
Advanced time-series topics
Additional Stata time-series features
Data management tools and utilities
Univariate models
Multivariate models
Author index
Subject index
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