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Introduction to Time Series Using Stata, Revised Edition [Pehme köide]

(University of Aarhus, Denmark)
  • Formaat: Paperback / softback, 446 pages, kõrgus x laius: 246x189 mm, kaal: 960 g, 111 Illustrations, black and white
  • Ilmumisaeg: 02-Mar-2020
  • Kirjastus: Stata Press
  • ISBN-10: 1597183067
  • ISBN-13: 9781597183062
Teised raamatud teemal:
Introduction to Time Series Using Stata, Revised EditionSuurem pilt
  • Formaat: Paperback / softback, 446 pages, kõrgus x laius: 246x189 mm, kaal: 960 g, 111 Illustrations, black and white
  • Ilmumisaeg: 02-Mar-2020
  • Kirjastus: Stata Press
  • ISBN-10: 1597183067
  • ISBN-13: 9781597183062
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781597183062.html
Märksõnad:

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.

List of tables
xiii
List of figures
xv
Preface xxi
Acknowledgments xxvii
1 Just Enough Stata
1(70)
1.1 Getting started
2(18)
1.1.1 Action first, explanation later
2(4)
1.1.2 Now some explanation
6(1)
1.1.3 Navigating the interface
7(6)
1.1.4 The gestalt of Stata
13(2)
1.1.5 The parts of Stata speech
15(5)
1.2 All about data
20(9)
1.3 Looking at data
29(20)
1.4 Statistics
49(11)
1.4.1 Basics
49(4)
1.4.2 Estimation
53(7)
1.5 Odds and ends
60(2)
1.6 Making a date
62(6)
1.6.1 How to look good
63(2)
1.6.2 Transformers
65(3)
1.7 Typing dates and date variables
68(1)
1.8 Looking ahead
69(2)
2 Just Enough Statistics
71(14)
2.1 Random variables and their moments
72(1)
2.2 Hypothesis tests
73(1)
2.3 Linear regression
74(4)
2.3.1 Ordinary least squares
74(3)
2.3.2 Instrumental variables
77(1)
2.3.3 FGLS
77(1)
2.4 Multiple-equation models
78(1)
2.5 Time series
79(6)
2.5.1 White noise, autocorrelation, and stationarity
80(2)
2.5.2 ARMA models
82(3)
3 Filtering Time-Series Data
85(56)
3.1 Preparing to analyze a time series
87(5)
3.1.1 Questions for all types of data
87(1)
How are the variables defined?
87(1)
What is the relationship between the data and the phenomenon of interest?
88(2)
Who compiled the data?
90(1)
What processes generated the data?
90(1)
3.1.2 Questions specifically for time-series data
91(1)
What is the frequency of measurement?
91(1)
Are the data seasonally adjusted?
91(1)
Are the data revised?
92(1)
3.2 The four components of a time series
92(8)
Trend
93(2)
Cycle
95(3)
Seasonal
98(2)
3.3 Some simple filters
100(21)
3.3.1 Smoothing a trend
103(6)
3.3.2 Smoothing a cycle
109(5)
3.3.3 Smoothing a seasonal pattern
114(1)
3.3.4 Smoothing real data
115(6)
3.4 Additional niters
121(17)
3.4.1 ma: Weighted moving averages
123(2)
3.4.2 Ewmas
125(1)
Exponential: Ewmas
126(4)
Dexponential: Double-Exponential Moving Averages
130(1)
3.4.3 Holt-Winters smoothers
131(1)
Hwinters: Holt--Winters smoothers without a seasonal component
131(6)
Shwinters: Holt--Winters smoothers including a seasonal component
137(1)
3.5 Points to remember
138(3)
4 A First Pass At Forecasting
141(26)
4.1 Forecast fundamentals
141(5)
4.1.1 Types of forecasts
142(2)
4.1.2 Measuring the quality of a forecast
144(1)
4.1.3 Elements of a forecast
144(2)
4.2 Filters that forecast
146(19)
4.2.1 Forecasts based on EWMAs
148(11)
4.2.2 Forecasting a trending series with a seasonal component
159(6)
4.3 Points to remember
165(1)
4.4 Looking ahead
166(1)
5 Autocorrelated Disturbances
167(34)
5.1 Autocorrelation
168(4)
5.1.1 Example: Mortgage rates
169(3)
5.2 Regression models with autocorrelated disturbances
172(4)
5.2.1 First-order autocorrelation
173(2)
5.2.2 Example: Mortgage rates (cont.)
175(1)
5.3 Testing for autocorrelation
176(2)
5.3.1 Other tests
177(1)
5.4 Estimation with first-order autocorrelated data
178(19)
5.4.1 Model 1: Strictly exogenous regressors and autocorrelated disturbances
179(3)
The OLS strategy
182(1)
The transformation strategy
183(3)
The FGLS strategy
186(2)
Comparison of estimates of model 1
188(1)
5.4.2 Model 2: A lagged dependent variable and i.i.d. errors
189(4)
5.4.3 Model 3: A lagged dependent variable with AR(1) errors
193(1)
The transformation strategy
194(2)
The IV strategy
196(1)
5.5 Estimating the mortgage rate equation
197(2)
5.6 Points to remember
199(2)
6 Univariate Time-Series Models
201(16)
6.1 The general linear process
202(1)
6.2 Lag polynomials: Notation or prestidigitation?
203(2)
6.3 The ARMA model
205(3)
6.4 Stationarity and invertibility
208(2)
6.5 What can ARMA models do?
210(4)
6.6 Points to remember
214(1)
6.7 Looking ahead
215(2)
7 Modeling A Real-World Time Series
217(54)
7.1 Getting ready to model a time series
218(8)
7.2 The Box-Jenkins approach
226(2)
7.3 Specifying an ARMA model
228(15)
7.3.1 Step 1: Induce stationarity (ARMA becomes ARIMA)
228(5)
7.3.2 Step 2: Mind your p's and q's
233(10)
7.4 Estimation
243(10)
7.5 Looking for trouble: Model diagnostic checking
253(4)
7.5.1 Overfitting
253(1)
7.5.2 Tests of the residuals
254(3)
7.6 Forecasting with ARIMA models
257(5)
7.7 Comparing forecasts
262(4)
7.8 Points to remember
266(1)
7.9 What have we learned so far?
267(2)
7.10 Looking ahead
269(2)
8 Time-Varying Volatility
271(28)
8.1 Examples of time-varying volatility
272(5)
8.2 ARCH: A model of time-varying volatility
277(8)
8.3 Extensions to the ARCH model
285(13)
8.3.1 GARCH: Limiting the order of the model
286(6)
8.3.2 Other extensions
292(1)
Asymmetric responses to "news"
293(2)
Variations in volatility affect the mean of the observable series
295(1)
Nonnormal errors
296(1)
Odds and ends
296(2)
8.4 Points to remember
298(1)
9 Models Of Multiple Time Series
299(78)
9.1 Vector autoregressions
300(3)
9.1.1 Three types of VARs
302(1)
9.2 A VAR of the U.S. macroeconomy
303(26)
9.2.1 Using Stata to estimate a reduced-form VAR
305(4)
9.2.2 Testing a VAR for stationarity
309(3)
Other tests
312(4)
9.2.3 Forecasting
316(9)
Evaluating a VAR forecast
325(4)
9.3 Who's on first?
329(29)
9.3.1 Cross correlations
330(5)
9.3.2 Summarizing temporal relationships in a VAR
335(1)
Granger causality
336(3)
How to impose order
339(4)
FEVDs
343(1)
Using Stata to calculate IRFs and FEVDs
344(14)
9.4 SVARs
358(15)
9.4.1 Examples of a short-run SVAR
361(9)
9.4.2 Examples of a long-run SVAR
370(3)
9.5 Points to remember
373(1)
9.6 Looking ahead
374(3)
10 Models Of Nonstationary Time Series
377(50)
10.1 Trends and unit roots
378(4)
10.2 Testing for unit roots
382(5)
10.3 Cointegration: Looking for a long-term relationship
387(2)
10.4 Cointegrating relationships and VECMs
389(5)
10.4.1 Deterministic components in the VECM
393(1)
10.5 From intuition to VECM: An example
394(30)
Step 1 Confirm the unit root
399(2)
Step 2 Identify the number of lags
401(1)
Step 3 Identify the number of cointegrating relationships
402(4)
Step 4 Fit a VECM
406(10)
Step 5 Test for stability and white-noise residuals
416(1)
Step 6 Review the model implications for reasonableness
417(7)
10.6 Points to remember
424(1)
10.7 Looking ahead
424(3)
11 Closing Observations
427(8)
11.1 Making sense of it all
427(1)
11.2 What did we miss?
428(5)
11.2.1 Advanced time-series topics
429(2)
11.2.2 Additional Stata time-series features
431(1)
Data management tools and utilities
431(1)
Univariate models
432(1)
Multivariate models
433(1)
11.3 Farewell
433(2)
References 435(4)
Author index 439(2)
Subject index 441
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.