Econometric Models, Techniques, and Applications

by Intriligator, Michael D.; Bodkin, Ronald G.; Hsiao, Cheng

• ISBN13: 9780132247757
• ISBN10: 0132247755
• Edition: 2nd
• Format: Paperback
• Copyright: 1995-12-19
• Publisher: Pearson

Summary

This book surveys the theories, techniques (model- building and data collection), and applications of econometrics.KEY TOPICS:It focuses on those aspects of econometrics that are of major importance to readers and researchers interested in performing, evaluating, or understanding econometric studies in a variety of areas. It reviews matrix notation and the use of multivariate statistics; discusses the specification of the model and the development of data for its estimation; covers recent developments in econometric models, techniques, and applications; explains the estimation of single-equation models; and provides case studies of the applications of econometrics to a wide array of areas including traditional areas such as the estimation of demand functions and production functions, and macroeconometric models.

Table of Contents

Preface	xiii
PART I INTRODUCTION: OVERVIEW, MODELS, AND DATA
The Econometric Approach	1	(12)
What is econometrics?	1	(1)
The nature of the econometric approach	2	(2)
The purposes of econometrics	4	(1)
An example: The demand curve and the price elasticity of demand	5	(2)
Second example: The consumption function	7	(1)
Third example: The growth of science	8	(1)
Traditional econometrics under criticism	9	(2)
Learning by doing in econometrics	11	(2)
Bibliography	11	(2)
Models, Econometric Models, and Econometric Models	13	(34)
What is a model?	13	(2)
Types of models: Verbal/logical, physical, and geometric	15	(2)
Algebraic models	17	(2)
Econometric models	19	(5)
The prototype micro model	24	(5)
The prototype macro model	29	(4)
Related approaches to econometric modeling	33	(2)
The general econometric model: Structural form and reduced form	35	(5)
The final form	40	(2)
Identification of the general econometric model; concluding remarks	42	(5)
Problems	45	(2)
Bibliography	47	(23)
Data and Refined Data	50	(1)
What are data?	50	(1)
Quantitative versus qualitative data; dummy variables	51	(3)
Time-series versus cross-section data; pooling; microdata	54	(3)
Nonexperimental versus experimental data: Social experimentation	57	(1)
Problems with the data	58	(1)
Refining the data	59	(3)
Accuracy of economic data	62	(2)
Sources of economic data	64	(6)
Problems	64	(4)
Bibliography	68	(2)
PART II SINGLE-EQUATION ESTIMATION
The Basic Linear Regression Model	70	(55)
Introduction	70	(1)
The linear regression model	70	(4)
Estimation using the method of least squares	74	(5)
The Gauss-Markov theorem and properties of the least squares estimator	79	(5)
Maximum likelihood and the method of moments estimators	84	(2)
Linear restrictions on the coefficients	86	(2)
Statistical inference	88	(10)
Prediction	98	(1)
Examples	99	(5)
Nonnested hypothesis testing and model selection criteria	104	(5)
Bayesian analysis of the linear regression model	109	(16)
Problems	117	(5)
Bibliography	122	(3)
Extensions of the Simple Linear Regression Model	125	(62)
Problems: Their diagnosis and treatment	125	(1)
Multicollinearity	126	(7)
Generalized least squares method and seemingly unrelated regression models	133	(3)
Heteroskedasticity	136	(3)
Serial correlation	139	(7)
Error components models	146	(5)
Specification error	151	(4)
Errors in variables and the method of instrumental variables	155	(4)
Nonlinear least squares estimator: Gauss-Newton and Newton-Raphson methods for solving nonlinear equations	159	(2)
Discrete response models: Linear probability, probit, and logit models	161	(3)
Censored or truncated regression models	164	(3)
Robust methods	167	(2)
Parametric and semiparametric estimation	169	(5)
Approaches to analyzing economic data	174	(13)
Problems	176	(5)
Bibliography	181	(6)
Introduction to Time-Series Analysis and Dynamic Specification	187	(51)
Introduction	187	(2)
Some basic tools	189	(2)
Stationary processes	191	(7)
Box-Jenkins procedure for building linear (stationary) time-series models	198	(9)
Nonstationary time series	207	(9)
Example	216	(3)
Minimum mean square error forecast	219	(3)
Transfer function models	222	(16)
Problems	232	(3)
Bibliography	235	(3)
PART III APPLICATIONS OF SINGLE-EQUATION ESTIMATION
Application to Households; Demand Analysis	238	(37)
Introduction	238	(1)
The theory of the household	239	(9)
Single demand equations versus systems of demand equations	248	(1)
Single demand equations	249	(5)
Systems of demand equations	254	(4)
Identification	258	(3)
Aggregation	261	(2)
Dynamic demand analysis	263	(12)
Problems	268	(5)
Bibliography	273	(2)
Applications to Firms; Production Functions and Cost Functions	275	(43)
Introduction	275	(1)
The theory of the firm	275	(9)
Estimation of production functions	284	(15)
Estimation of cost curves and cost functions	299	(3)
Estimation of factor demand equations	302	(4)
Technical change	306	(12)
Problems	309	(4)
Bibliography	313	(5)
PART IV SIMULTANEOUS EQUATIONS AND DYNAMIC SYSTEMS
The Simultaneous-Equations System and Its Identification	318	(29)
The simultaneous-equations system	318	(5)
The Problem of identification	323	(3)
Identification by zero restrictions in the nonstochastic case	326	(6)
Identification by general linear restrictions	332	(4)
Recursive systems	336	(3)
Identification of nonlinear models	339	(8)
Problems	341	(4)
Bibliography	345	(2)
Estimation of Simultaneous-Equations Systems	347	(53)
Introduction	347	(4)
Naive, limited-information, and full-information approaches	351	(2)
Ordinary least squares and least squares bias	353	(3)
Indirect least squares	356	(4)
Two-stage least squares and k-class estimators	360	(9)
Instrumental variables	369	(5)
Three-stage least squares	374	(8)
Full-information maximum likelihood	382	(4)
Monte Carlo studies of small-sample properties of estimators	386	(4)
Nonlinear simultaneous equations models	390	(10)
Problems	393	(3)
Bibliography	396	(4)
Dynamic Systems	400	(30)
Introduction	400	(1)
Dynamic simultaneous-equations models	400	(3)
Modeling unrestricted multiple time series: The approach of Tiao and Box	403	(6)
Granger casuality	409	(3)
Cointegration	412	(9)
Relations between time-series models and structural econometric models	421	(9)
Problems	425	(1)
Bibliography	426	(4)
PART V APPLICATIONS OF SIMULTANEOUS-EQUATIONS ESTIMATION
Applications to Macroeconometric Models	430	(35)
The nature of macroeconometric models	430	(2)
The Klein interwar model	432	(4)
The Klein-Goldberger model	436	(2)
The Wharton model	438	(3)
The MPS model	441	(2)
The DRI model	443	(4)
Survey of some macroeconometric models of the U.S. economy	447	(6)
Some international experience; the CANDIDE model of the Canadian economy	453	(3)
Trends in macroeconometric model construction	456	(9)
Problems	458	(2)
Bibliography	460	(5)
Other Applications of Simultaneous-Equations Estimation	465	(25)
Introduction	465	(1)
Simultaneous-equations model of money demand and supply	466	(4)
Simultaneous-equations model of industrial-organization relationships	470	(4)
Simultaneous-equations model in labor economics	474	(1)
Simultaneous-equations model of the health system	475	(4)
Simultaneous-equations model of alcoholism	479	(4)
Economic history; cliometrics	483	(7)
Problems	484	(2)
Bibliography	486	(4)
PART VI THE USES AND EVALUATION OF ECONOMETRIC MODELS
Structural Analysis	490	(23)
The uses of econometric models	490	(1)
The nature of structural analysis	491	(1)
Comparative statics	492	(3)
Elasticities	495	(3)
Multipliers: Impact, interim, and long-run (the linear case)	498	(2)
Multipliers: Impact, interim, and long-run (the nonlinear case)	500	(2)
Example of multiplier analysis: The Suits study	502	(2)
Second example of multiplier analysis: The Goldberger study	504	(1)
Third example of multiplier analysis: Study of comparative multipliers of U.S. macroeconometric models from a models comparisons project	505	(2)
Fourth example of multiplier analysis: Comparative interim multipliers with Canadian macroeconometric models	507	(1)
Final example of multiplier analysis: The DRI model of the early 1980s	508	(5)
Problems	510	(1)
Bibliography	511	(2)
Forecasting	513	(32)
The nature of forecasting	513	(2)
Alternative approaches to forecasting	515	(3)
The econometric approach to forecasting; short-term forecasts	518	(5)
Long-term forecasts	523	(1)
Forecast accuracy	523	(4)
Forecasting experience with macroeconometric models: Specific Studies	527	(5)
Forecasting experience with macroeconometric models: General lessons	532	(3)
Combining forecasts and forecasting methods	535	(10)
Problems	537	(3)
Bibliography	540	(5)
Policy Evaluation	545	(24)
The nature of policy evaluation	545	(1)
Alternative approaches to policy evaluation	546	(2)
Policy evaluation using an econometric model	548	(1)
The instruments-targets approach	549	(2)
The social-welfare-function approach; optimal control	551	(4)
The simulation approach	555	(4)
The options for Canadian policymakers at the beginning of the 1980s and two other examples	559	(3)
The econometric approach to policy evaluation	562	(7)
Problems	564	(1)
Bibliography	565	(4)
Validation of Econometric Models and Managerial Aspects of the Uses of Econometric Models	569	(17)
Introduction	569	(1)
Are the a priori constraints respected?	570	(2)
Parametric tests prior to the release of the model	572	(2)
Parametric tests after release	574	(2)
Nonparametric tests of an econometric model	576	(2)
The model as a system or gestalt	578	(1)
The problem of a possible break in structure; the ``Lucas critique''	579	(1)
Managerial aspects of model uses: Four examples	580	(2)
Model management and the role of judgment	582	(4)
Problems	583	(1)
Bibliography	584	(2)
Appendix A An Econometric Project	586	(13)
A.1 The nature of the econometric project	586	(1)
A.2 The model	586	(3)
A.3 The data	589	(2)
A.4 Estimation of the model	591	(1)
A.5 What to include in the write-up	591	(2)
A.6 A bibliography of econometric applications	593	(6)
Appendix B Matrices	599	(26)
B.1 Basic definitions and examples	599	(1)
B.2 Some special matrices	600	(2)
B.3 Matrix relations and operations	602	(5)
B.4 Scalar-valued functions defined on matrices	607	(3)
B.5 Inverse and generalized inverse matrices	610	(2)
B.6 Systems of linear equations; solutions and least squares fits	612	(4)
B.7 Linear transformations and characteristic roots and vectors	616	(2)
B.8 Quadratic forms	618	(1)
B.9 Matrix derivatives	619	(2)
B.10 Mathematical programming	621	(4)
Bibliography	624	(1)
Appendix C Probability and Statistics	625	(16)
C.1 Probability	625	(2)
C.2 Random variables; distribution and density functions	627	(5)
C.3 Mean, variance, covariance, and other moments; sample measures	632	(3)
C.4 Some specific distributions	635	(4)
C.5 Additional results on distribution theory	639	(2)
Bibliography	639	(2)
Indexes	641