9780312235130

An Introduction To Applied Econometrics

by
  • ISBN13:

    9780312235130

  • ISBN10:

    0312235135

  • Format: Paperback
  • Copyright: 2000-10-13
  • Publisher: Palgrave Macmillan

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Supplemental Materials

What is included with this book?

Summary

Covering the essential elements of the subject of econometrics, the author also introduces and explains techniques that are now widely used in applied work, although rarely introduced in detail in non-specialist texts, such as integrated time series, cointegration, simulation analysis, Johansen's Approach to multivariate co-integration and ARCH. The author explains the central distinction between stationary and nonstationary time series, which is of crucial importance in many areas of analysis, especially in macroeconomics and financial economics.

Author Biography

Kerry Patterson is Professor of Econometrics at the University of Reading.

Table of Contents

List of Figures
xviii
Preface xxiii
Acknowledgements xxvii
PART I Foundations 1(84)
Economics and quantitative economics
3(21)
Introduction
3(1)
Defining economics
3(1)
Description, construction and models in economics
4(1)
The scope of model building in quantitative economics
5(9)
A historical debate
5(3)
Present-day concerns
8(2)
Stylisations of methodology
10(4)
The structure and aims of this book
14(7)
General aims
14(3)
Parts and chapters
17(2)
General comments about the structure of this book
19(1)
Further reading
20(1)
Concluding remarks
21(3)
Review
21(2)
Review questions
23(1)
Some preliminaries
24(27)
Introduction
24(1)
Distinguishing characteristics of the data
24(8)
Series and cross-section data
24(1)
Time series graphs
25(1)
Frequency
26(1)
Dimension of a variable
26(1)
Some examples of time series data
27(3)
Nonexperimental data
30(1)
Experimental data
31(1)
Lagging and leading time series data
32(2)
Lagging time series data
32(1)
Leading time series data
33(1)
The lag operator
34(2)
Definition of the lag operator
34(1)
The lag polynomial
34(1)
Obtaining the sum of the lag coefficients
34(1)
A univariate dynamic model
35(1)
Bivariate relationships
36(6)
A deterministic bivariate model
36(1)
A stochastic bivariate model
36(1)
Visual representation of two variables
37(1)
Dynamic bivariate models
38(1)
Autoregressive distributed lag (ADL) models
39(1)
The distributed lag function
40(1)
More than one conditioning variable
41(1)
Notation in more complex models
42(1)
Several equations together
42(3)
Concluding remarks
45(6)
Review
46(3)
Review questions
49(2)
An introduction to stationary and nonstationary random variables
51(34)
Introduction
51(1)
Time series with a varying mean
52(4)
Some examples
52(4)
Random variables
56(6)
The expected value of a random variable
56(3)
The variance of a random variable
59(2)
Continuous random variables
61(1)
Joint events, covariance, autocovariance and autocorrelation
62(12)
Joint events
62(2)
Covariance and autocovariance
64(2)
Conditional expectation
66(1)
Autocovariances and second order stationarity
67(1)
Linear combinations of random variables
68(1)
An example of a nonstationary time series
69(1)
Correlation and the autocorrelation function
70(2)
The variance decomposition
72(1)
Iterating expectations
73(1)
A random walk
74(2)
The coin-tossing game
74(2)
Estimation
76(3)
Nonstationary processes
76(1)
Stationary processes
77(2)
Centred moving variance
79(1)
Concluding remarks
79(6)
Review
80(2)
Review questions
82(3)
PART II Estimation and simulation 85(328)
Estimation and hypothesis testing in simple regression models
87(61)
Introduction
87(1)
Statistical background
88(7)
Factorisation of the joint density
88(2)
The conditional expectation function, CEF, as the regression function
90(4)
Some important distributions
94(1)
Estimation, estimators and estimates
95(4)
The least squares principle
95(3)
Instrumental variables estimation
98(1)
Properties of estimators
99(4)
Bias
99(1)
Consistency
100(1)
Examples
100(1)
Speed of convergence
100(2)
Asymptotic bias
102(1)
Efficiency
103(1)
Linearity
103(1)
Properties of the OLS estimators β1 and β2
103(6)
Conditionally and unconditionally unbiased
103(1)
Minimum variance in the class of linear unbiased estimators
104(1)
The variance of β2
105(1)
The variance of β1
106(1)
The unconditional variances of β1 and β2
106(1)
The Gauss--Markov theorem
107(2)
A nonlinear CEF
109(1)
Goodness of fit
109(6)
Goodness of fit in the population, η2
110(1)
Goodness of fit in the sample, R2
111(3)
% σ as a measure of goodness of fit
114(1)
Estimation of dynamic models
115(1)
Structure and regression
116(5)
Weak exogeneity and the parameters of interest
117(1)
Instrumental variables estimation
118(3)
Tests and associated concepts
121(5)
Significance tests
121(2)
The alternative hypothesis
123(2)
Power
125(1)
Summary of OLS estimators and an empirical example
126(4)
Tabular summary
127(1)
Typical computer output
127(3)
Concluding remarks
130(6)
Review
130(4)
Review questions
134(2)
Appendices
136(1)
Maximum likelihood estimation
136(8)
The likelihood function
137(1)
The binomial distribution of probabilities
137(2)
Maximum likelihood estimation: the regression model
139(1)
Estimation in simultaneous models
140(3)
Hypothesis testing
143(1)
Computer output
144(4)
MICROFIT
144(1)
TSP
145(1)
RATS
146(1)
PCGIVE
146(2)
Extending estimation and model building to several regressors
148(60)
Introduction
148(1)
Extending the bivariate model: more than two regressors
148(4)
Multiple regressors: the basic set-up
149(2)
Deriving the OLS estimator
151(1)
The variance--covariance matrix of β
151(1)
Generalised least squares, GLS
152(3)
The GLS estimator
153(1)
The variance--covariance matrix of the GLS estimator, Var(β0)
154(1)
OLS or GLS?
154(1)
Testing hypotheses
155(6)
Testing principles: Lagrange-Multiplier, Wald, likelihood ratio
155(1)
Extension of multiple hypotheses
156(5)
Heteroscedasticity: implications for OLS estimation and tests
161(8)
Implications of heteroscedasticity
161(1)
Tests for heteroscedasticity
161(1)
White's (1980) test
161(4)
The Goldfeld--Quandt test
165(1)
The Breusch--Pagan/Godfrey test
165(2)
Interpretation of significant test statistics for heteroscedasticity
167(2)
Misspecification: diagnosis and effects
169(11)
Serial correlation of &epsis;t
170(1)
The Durbin--Watson, DW, statistic
171(2)
The Lagrange-Multiplier (LM) test for serial correlation
173(1)
The Box--Pearce and Ljung--Box tests
174(1)
An illustration of the DW and LM tests
174(1)
Interpretation of significant test statistics for serial correlation
175(3)
The Newey--West estimator of the variance(--covariance) matrix of β
178(2)
Normality and the Jarque--Bera test
180(3)
Normality of &epsis;t
181(1)
The Jarque--Bera test
181(2)
Functional form and the RESET test
183(2)
Developing a test for nonlinearity
183(1)
Ramsay's RESET test
184(1)
Stability of the regression coefficients
185(4)
Chow's (first) test
185(1)
Predictive/forecast failure tests
186(1)
Chow's (second) test: a test for predictive failure
186(2)
A forecast (deterioration) test
188(1)
Unknown breakpoint(s)
188(1)
Model building and evaluation
189(1)
An estimation regression model
190(11)
The basic model
190(2)
Estimation of the basic model
192(1)
Diagnostic and misspecification tests
192(1)
Serial correlation
192(1)
Heteroscedasticity
193(3)
Normality
196(1)
Functional form: the RESET test
196(1)
Chow tests
196(1)
Chow's first test
196(2)
Chow's second test: predictive failure
198(1)
An extended model
198(3)
Concluding remarks
201(7)
Review
202(2)
Review questions
204(4)
An introduction to nonstationary univariate time series models
208(45)
Introduction
208(1)
Nondeterministic time series
209(18)
A pure random walk
209(5)
A near random walk
214(2)
A random walk with drift
216(4)
Unit roots
220(2)
A near random walk with drift
222(1)
The persistence of shocks
223(1)
The mean, variance and autocorrelation of an AR(1) process
223(2)
Difference stationary and trend stationary series
225(2)
Testing for a unit root
227(14)
τ test
227(2)
Φ1 and τ test statistics
229(4)
Φ3 and τβ test statistics
233(3)
The empirical power of some Dickey--Fuller test statistics
236(1)
Distribution of the test statistics on the intercept and trend
237(1)
The augmented Dickey--Fuller, ADF, test
238(3)
A framework for testing
241(6)
Is the data series trended?
242(2)
The data is not obviously trended and the mean under the alternative is nonzero
244(1)
The data is not obviously trended and the mean under the alternative is zero
245(1)
Cumulation of type 1 error
246(1)
Concluding remarks
247(6)
Review
248(2)
Review questions
250(3)
Development of nonstationary univariate time series models
253(63)
Introduction
253(1)
ARIMA models
254(3)
Pre-testing, power and model selection strategies using ADF test statistics
257(3)
Other tests
260(5)
Dickey and Fuller's ρ, ρpμ and ρβ
260(2)
The weighted symmetric (WS) estimator, Pantula et al. (1994)
262(2)
Phillips and Perron versions of the DF tests
264(1)
Structural and reduced form univariate time series models
265(5)
Structural univariate time series models
265(3)
Stationarity as the null hypothesis
268(2)
Testing for 2 unit roots
270(2)
Seasonality and seasonal integration
272(5)
Integration in seasonal processes
272(1)
Testing for a unit root in a seasonal process
273(4)
Structural breaks
277(8)
The Perron (1989) approach to a single structural break
278(5)
Additive outliers, Franses and Haldrup (1994)
283(1)
Summary
284(1)
Applications to some economic time series
285(16)
UK consumers' expenditure on nondurables
285(7)
UK employment rate
292(3)
US unemployment rate
295(4)
Testing for seasonal nonstationarity: UK employees
299(2)
`Nearly' integrated and `nearly' stationary time series
301(4)
Concluding remarks
305(11)
Review
308(4)
Review questions
312(3)
Appendix
315(1)
Stationarity and nonstationarity in single-equation regression analysis
316(57)
Introduction
316(1)
Examining the properties of estimators by simulation
317(11)
Xt is fixed in repeated samples
317(3)
Xt is a stationary, stochastic variable
320(1)
Xt stationary: white noise
320(1)
Xt stationary: an AR(1) process
321(1)
Xt is a nonstationary, stochastic variable
322(2)
A spurious regression
324(2)
The distribution of R2
326(2)
Cointegration
328(2)
Cointegration: basic concepts
328(2)
Cointegrating versus spurious regressions
330(1)
Testing for noncointegration: the Engle--Granger (1987) approach
330(10)
The Engle--Granger (1987) approach (the bivariate case)
330(2)
Critical values for the test statistic τγ: simulation
332(1)
MacKinnon's response surface for critical values
333(1)
More than two variables
334(1)
An illustration of the testing procedure
335(3)
An illustration of a spurious regression
338(2)
Links between cointegration and error correction models
340(9)
Engle and Granger's two-stage estimation method
341(2)
Cointegration and error correction: an alternative test statistic for cointegration
343(1)
Known cointegration coefficients
344(2)
Unknown cointegration coefficients
346(3)
Alternative representations of the long-run relationship
349(6)
The ADL model and the ECM (the two variable case)
349(2)
The Bewley transformation
351(1)
A numerical example
352(1)
The more general ADL model: alternative representations
352(3)
Estimation, inference and simulation
355(11)
A comparison of alternative ways of estimating the cointegrating coefficients
356(1)
Simulation set-up
356(2)
Simulation results
358(1)
Estimating the coefficients
359(5)
Distribution of the `t' statistics
364(2)
Concluding remarks
366(7)
Review
368(1)
Review questions
369(3)
Appendix
372(1)
Endogeneity and the fully modified OLS estimator
373(40)
Introduction
373(1)
Distinguishing variance matrices
374(8)
Conditional, unconditional and long-run variance matrices
375(1)
Autoregressive processes
375(2)
First order moving average process
377(1)
Decomposition of the long-run variance matrix
378(1)
A general result
379(1)
MA(1) example
380(1)
AR(1) example
380(2)
Endogeneity
382(4)
Preliminaries
383(1)
Contemporaneity
383(1)
Weak exogeneity
384(1)
The regression (or conditional expectation) function and weak exogeneity
384(2)
The fully modified (Phillips--Hansen) OLS estimator
386(10)
Corrections for bias and endogeneity
386(1)
A bias correction
386(1)
An endogeneity correction
387(1)
A semi-parametric approach to estimating the corrections
388(1)
Variations on a theme: when OLS on the ADL model is optimal
389(3)
Examples of FMOLS estimation
392(1)
The consumption--income example
392(1)
Long and short interest rates
393(1)
Simulation findings
394(1)
Simulation results: Phillips and Hansen (1990)
394(1)
Simulation results: Hansen and Phillips (1990)
395(1)
Simulation results: Inder (1993)
396(1)
Complications: nearly integrated processes and endogeneity
396(10)
Xt integrated/nearly integrated, no endogeneity
397(2)
Xt integrated/nearly integrated and endogenous
399(1)
Contemporaneity
399(3)
Failure of weak exogeneity
402(2)
Summary
404(1)
Sensitivity to changes in the design parameters: slow adjustment
405(1)
Concluding remarks
406(7)
Review
408(3)
Review questions
411(2)
PART III Applications 413(184)
The demand for money
415(52)
Introduction
415(1)
The demand for money
415(10)
A definition of money
415(1)
The transactions motive
416(3)
The precautionary motive
419(2)
The speculative motive
421(2)
Bringing the motives together
423(1)
Some variations on a theme: the velocity of circulation
424(1)
The demand for money during the German hyperinflation
425(15)
Historical background
426(1)
Cagan's specification of the demand for money function: background
426(1)
Cagan's demand for money function: basic specification
427(2)
A graphical analysis of the data
429(1)
Testing for nonstationarity
430(4)
Cointegration
434(4)
Dynamic models
438(2)
The demand for M1: a study using recent US data
440(16)
Model specification
441(1)
Data definitions
441(2)
A graphical analysis of the data
443(1)
Testing for nonstationarity
444(2)
Cointegration
446(4)
Dynamic models
450(4)
Out of sample performance
454(1)
A brief comparison with Hoffman and Rasche (1991) and Baba, Hendry and Starr (1992)
455(1)
Concluding remarks
456(11)
Review
458(5)
Review questions
463(4)
The term structure of interest rates
467(41)
Introduction
467(1)
Term structure of interest rates
468(4)
Term to maturity
468(1)
The discount rate, the interest rate and continuous compounding
469(2)
The yield curve
471(1)
The expectations model of the term structure
472(5)
The yield to maturity and the forward rate
472(3)
The spread
475(1)
Implications for economic policy
476(1)
Assessing the expectations model
477(17)
Three implications of the expectations model
477(2)
The data
479(1)
A graphical analysis of the data: yields
480(1)
Unit root tests on the yields
481(2)
A graphical analysis of the data: spreads
483(1)
Unit root tests on the spreads
484(1)
Estimation of the spread equations
485(6)
Bivariate regressions: the perfect foresight spread
491(3)
Other studies and other methods of testing the expectations model
494(8)
Methods and results
494(5)
Why do tests of EH + REH tend to indicate rejection?
499(3)
Concluding remarks
502(6)
Review
503(3)
Review questions
506(2)
The Phillips curve
508(45)
Introduction
508(1)
The Phillips curve
509(9)
Basic ideas
509(1)
Phillips' original estimates and interpretation
510(1)
The Phillips curve: a menu of choice?
511(1)
The Phillips curve in the United States: an early view
512(1)
A graphical analysis of Phillips' data for 1861--1913
513(1)
Testing for nonstationarity
514(3)
Re-estimation of the Phillips curve, 1861--1913
517(1)
Is the Phillips curve misspecified?
518(7)
Fisher (1926) and Phillips (1958)
519(1)
Friedman's model
520(2)
Imperfect competition
522(1)
The Phillips, `Fisher' and `Friedman' curves
523(1)
Expectations and the reformulation of the Phillips curve
523(2)
A supply side interpretation of the importance of inflation expectations
525(1)
Estimation of the expectations augmented Phillips curve (EAPC)
525(11)
Timing of expectations
525(1)
The adaptive expectations hypothesis: formulation
526(1)
The AEH: estimation
527(2)
The Lucas/Sargent critique of the identifying assumption
529(2)
Estimation results: adaptive expectations augmented Phillips curve
531(2)
Rational expectations (RE): general principles
533(1)
Implementing rational expectations
533(2)
Estimation results with (weakly) rational expectations
535(1)
The Phillips correlation
536(9)
Granger-causation tests
537(2)
Estimation and hypothesis tests
539(1)
Practical problems
540(1)
Granger-causation tests: wage/price inflation and unemployment, United Kingdom
541(2)
Granger-causation tests: wage/price inflation and unemployment, United States
543(2)
Concluding remarks
545(8)
Review
548(3)
Review questions
551(2)
The exchange rate and purchasing power parity
553(44)
Introduction
553(1)
Purchasing power parity
554(5)
Complications for PPP
555(1)
Short-run and long-run considerations
556(1)
The nominal exchange rate, Et, and the real exchange rate, REt
557(1)
A strategy for testing RPPP
558(1)
Assessing the evidence for PPP
559(13)
The nature of the evidence
559(1)
Measuring the real exchange rate
560(1)
Visual impression of the data
561(5)
Dickey--Fuller unit root tests
566(6)
The real exchange rate: some more considerations and tests
572(7)
An example of the persistence of shocks
573(1)
Pooling observations: a panel unit root test
573(3)
Estimating the speed of response to a shock to the real exchange rate
576(3)
Simple tests for noncointegration
579(4)
Relaxing the (1, --1) cointegrating vector
579(1)
OLSEG estimation of the cointegrating regressions
580(2)
An illustration of the modified ADF test statistic
582(1)
(Very) `weak' form PPP
582(1)
Other models of the exchange rate
583(5)
The flexible price monetary model
583(2)
An illustration of the FPMM with US: UK quarterly data
585(3)
Concluding remarks
588(9)
Review
590(3)
Review questions
593(4)
PART IV Extensions 597(155)
Multivariate models and cointegration
599(54)
Introduction
599(1)
Some basic concepts
600(8)
The VAR
600(1)
Stability and stationarity in the VAR
601(3)
Stability and roots in the univariate model
604(1)
Eigenvalues and roots: the multivariate model
604(3)
What to do if there is a unit root
607(1)
Simple multivariate (vector) error correction models
608(7)
A bivariate model
608(2)
The eigenvalues of II and the existence of cointegrating vectors
610(1)
More than one cointegrating vector
611(1)
Longer Lags
612(1)
The multivariate model: the existence of a unit root and reduced rank of II
613(2)
Testing for cointegration
615(16)
Establishing a firm base for inference on the cointegrating rank
615(1)
Estimation of the UVAR and test statistics for testing the cointegrating rank (optional)
616(3)
Hypothesis tests on the cointegrating rank
619(4)
An alternative method of selecting the cointegrating rank
623(1)
Intercepts and trends in the VAR for the trace and λmax statistics
624(6)
Separating I(1) and I(0) variables
630(1)
Identification
631(13)
Structural and reduced form error correction models
632(3)
Identification of the cointegrating vectors
635(6)
Testing overidentifying restrictions on the cointegrating vectors
641(2)
Identification of the short-run structure
643(1)
Concluding remarks
644(9)
Review
646(4)
Review questions
650(3)
Applications of multivariate models involving cointegration
653(55)
Introduction
653(1)
Purchasing power parity and uncovered interest parity, Johansen and Juselius (1992)
654(5)
An outline of PPP and UIP
654(1)
Generic identification of the cointegrating relationships
655(1)
Estimating the cointegrating rank
656(2)
Interpreting the unrestricted cointegrating vectors
658(1)
Wage differentials in the United States, Dickey and Rossana (1994)
659(5)
Estimating the cointegrating rank
659(2)
Cointegration of real wages
661(1)
Identification of the cointegrating vectors
661(3)
The IS/LM model, Johansen and Juselius (1994)
664(3)
Identifying the short-run structure
665(1)
The simultaneous structure
666(1)
The demand for money in the United Kingdom, Hendry and Mizon (1993)
667(4)
Estimating the cointegrating rank
667(1)
Unrestricted estimates of the cointegrating vectors and adjustment coefficients
668(1)
Identification of the cointegrating vectors
668(1)
Estimating an SECM
669(1)
Identification of the short-run structure
670(1)
Weak exogeneity: when is it valid to model the partial system?
671(7)
Containing the number of variables in the VAR
673(1)
Closed or open systems?
674(2)
Joint, conditional and marginal models
676(1)
Hypothesis testing and weak exogeneity
677(1)
Examples of testing for weak exogeneity
677(1)
An extended illustration: Urban's (1995) study of the demand for imports in Belgium
678(6)
Estimating the cointegrating rank
678(2)
Identification of the cointegrating vectors
680(1)
Testing restrictions
681(1)
The parsimonious VAR, PVAR, and SECM
682(2)
Revisiting the demand for money in the United States
684(14)
A multivariate approach: choosing the lag length
685(1)
A multivariate approach: estimating the cointegrating rank by the Johansen method
686(4)
A multivariate approach: estimating the cointegrating rank by the Schwarz Information Criterion (SIC)
690(1)
Robustness of specification
691(2)
A comparison with OLS results
693(1)
A structural error correction model and parsimonious encompassing
693(2)
An estimated SECM for money, income and the interest rate
695(3)
Concluding remarks
698(10)
Review
700(3)
Review questions
703(5)
Autoregressive conditional heteroscedasticity: modelling volatility
708(44)
Introduction
708(1)
Basic concepts
709(6)
Conditional and unconditional variances: a crucial distinction
710(1)
ARCH(q)
711(2)
GARCH(p, q)
713(1)
What does data with an ARCH effect look like?
714(1)
Stationarity and persistence in some standard models
715(4)
ARCH(q)
716(1)
GARCH(p, q)
716(1)
IGARCH(1, 1)
717(1)
Nonnegativity constraints in GARCH models
718(1)
Estimation
719(2)
Specification
719(1)
A nonlinear estimator `beats' the linear OLS estimator
720(1)
Testing for ARCH/GARCH effects
721(1)
LM test for ARCH effects
721(1)
GARCH(p, q)
722(1)
Variations on an ARCH/GARCH theme
722(2)
ABSGARCH, EGARCH
722(1)
ABSGARCH
723(1)
EGARCH
723(1)
ARCH-M, GARCH-M, ABSGARCH-M, EGARCH-M
724(1)
The importance of asymmetry in ARCH models
724(6)
The news impact curve
725(1)
Examples of the news impact curve
726(1)
Asymmetry in more detail
727(1)
The AGARCH and GJR asymmetric models
727(2)
Tests for asymmetry
729(1)
Examples
730(9)
The US inflation rate
730(1)
The UK savings ratio
731(3)
ARCH-M applied to excess returns
734(2)
Testing for asymmetry in the returns for Standard and Poor's 500 index for the United States
736(3)
Concluding remarks
739(5)
Review
740(3)
Review questions
743(1)
Appendix
744(1)
The likelihood function for the ARCH model
744(1)
Nonnormality
745(1)
Properties of the maximum likelihood estimators in GARCH models
745(1)
Practical ARCH/GARCH
746(6)
Appendix -- Statistical tables 752(8)
A1 The normal distribution
753(1)
A2 The `t' distribution
754(1)
A3 The X2 distribution
755(1)
A4 The F Distribution
756(2)
A5 Critical values of the Durbin--Watson test
758(2)
References 760(15)
Index 775

Rewards Program

Reviews for An Introduction To Applied Econometrics (9780312235130)