Econometrics and data analysis for developing countries/ (Record no. 175884)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 05627cam a2200241 a 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 0415093996 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 0415094003 (pbk.) |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | CUS |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 30.015195 |
| Item number | MUK/E |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Mukherjee, Chandan. |
| 245 10 - TITLE STATEMENT | |
| Title | Econometrics and data analysis for developing countries/ |
| Statement of responsibility, etc. | Chandan Mukherjee, Howard White, and Marc Wuyts. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | London ; |
| -- | New York : |
| Name of publisher, distributor, etc. | Routledge, |
| Date of publication, distribution, etc. | 1998. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xviii, 496 p. ; |
| Dimensions | 24 cm. + |
| Accompanying material | 1 computer disk (3 1/2 in.) |
| 440 #0 - SERIES | |
| Title | Priorities for development economics |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical references and index. |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | ntroduction<br/>1 The purpose of this book<br/>2 The approach of this book: an example<br/>Part I Foundations of data analysis<br/>1 Model specification and applied research<br/>1.1 Introduction<br/>1.2 Model specification and statistical inference<br/>1.3 The role of data in model specification:<br/>traditional modelling<br/>1.4 The role of data in model specification:<br/>modern approaches<br/>1.5 The time dimension in data<br/>1.6 Summary of main points<br/>2 Modelling an average<br/>2.1 Introduction<br/>2.2 Kinds of averages<br/>2.3 The assumptions of the model<br/>2.4 The sample mean as best linear unbiased<br/>estimator (BLUE)<br/>2.5 Normality and the maximum likelihood principle<br/>2.6 Inference from a sample of a normal distribution<br/>2.7 Summary of main points<br/>Appendix 2.1: Properties of mean and variance<br/>Appendix 2.2: Standard sampling distributions<br/>3 Outliers, skewness and data transformations<br/>3.1 Introduction<br/>3.2 The least squares principle and the concept<br/>of resistance<br/>3.3 Mean-based versus order-based sample statistics<br/>3.4 Detecting non-normality in data<br/>3.5 Data transformations to eliminate skewness<br/>3.6 Summary of main points<br/>Part II Regression and data analysis<br/>4 Data analysis and simple regression<br/>4.1 Introduction<br/>4.2 Modelling simple regression<br/>4.3 Linear regression and the least squares principle<br/>4.4 Inference from classical normal linear<br/>regression model<br/>4.5 Regression with graphics: checking the model<br/>assumptions<br/>4.6 Regression through the origin<br/>4.7 Outliers, leverage and influence<br/>4.8 Transformation towards linearity<br/>4.9 Summary of main points =<br/>5 Partial regression: interpreting multiple regression coefficients<br/>5.1 Introduction<br/>5.2 The price of food and the demand for<br/>manufactured goods in India<br/>5.3 Least squares and the sample multiple regression line<br/>5.4 Partial regression and partial correlation<br/>5.5 The linear regression model<br/>5.6 The /-test in multiple regression<br/>5.7 Fragility analysis: making sense of<br/>regression coefficients<br/>5.8 Summary of main points<br/>6 Model selection and misspecification in multiple regression<br/>6.1 Introduction<br/>6.2 Griffin's aid versus savings model: the omitted<br/>variable bias<br/>6.3 Omitted variable bias: the theory<br/>6.4 Testing zero restrictions<br/>6.5 Testing non-zero linear restrictions<br/>6.6 Tests of parameter stability<br/>6.7 The use of dummy variables<br/>6.8 Summary of main points<br/>Part III Analysing cross-section data<br/>7 Dealing with heteroscedasticity<br/>7.1 Introduction<br/>7.2 Diagnostic plots: looking for heteroscedasticity<br/>7.3 Testing for heteroscedasticity<br/>7.4 Transformations towards homoscedasticity<br/>7.5 Dealing with genuine heteroscedasticity: weighted<br/>least squares and heteroscedastic standard errors<br/>7.6 Summary of main points<br/>8 Categories, counts and measurements<br/>8.1 Introduction<br/>8.2 Regression on a categorical variable: using<br/>dummy variables n<br/>8.3 Contingency tables: association between<br/>categorical variables<br/>8.4 Partial association and interaction<br/>8.5 Multiple regression on categorical variables<br/>8.6 Summary of main points<br/>9 Logit transformation, modelling and regression<br/>9.1 Introduction<br/>9.2 The logit transformation<br/>9.3 Logit modelling with contingency tables =><br/>9.4 The linear probability model versus logit regression<br/>9.5 Estimation and hypothesis testing in logit regression<br/>9.6 Graphics and residual analysis in logit regression<br/>9.7 /Summary of main points<br/>Part IV Regression with time-series data<br/>10 Trends, spurious regressions and transformations<br/>to stationarity<br/>10.1 Introduction<br/>10.2 Stationarity and non-stationarity<br/>10.3 Random walks and spurious regression<br/>10.4 Testing for stationarity<br/>10.5 Transformations to stationarity<br/>10.6 Summary of main points<br/>Appendix 10.1: Generated DSP and TSP series for exercises<br/>11 Misspecification and autocorrelation<br/>11.1 Introduction<br/>11.2 What is autocorrelation and why is it a problem?<br/>11.3 Why do we get autocorrelation?<br/>11.4 Detecting autocorrelation<br/>11.5 What to do about autocorrelation<br/>11.6 Summary of main points<br/>Appendix 11.1: Derivation of variance and covariance<br/>for AR(1) model<br/>12 Cointegration and the error correction model<br/>12.1 Introduction<br/>12.2 What is cointegration?<br/>12.3 Testing for cointegration<br/>12.4 The error correction model (ECM)<br/>12.5 Summary of main points<br/>Part V Simultaneous equation models<br/>13 Misspecification bias from single equation estimation<br/>13.1 Introduction<br/>13.2 Simultaneity bias in a supply and demand model<br/>13.3 Simultaneity bias: the theory<br/>13.4 The Granger and Sims tests for causality and<br/>concepts of exogeneity<br/>13.5 The identification problem<br/>13.6 Summary of main points<br/>14 Estimating simultaneous equation models<br/>14.1 Introduction<br/>14.2 Recursive models<br/>14.3 Indirect least squares<br/>14.4 Instrumental variable estimation and two-stage<br/>least squares<br/>14.5 Estimating the consumption function in a<br/>simultaneous system<br/>14.6 Full information estimation techniques<br/>14.7 Summary of main points |
| 650 #0 - SUBJECT | |
| Keyword | Econometrics. |
| 650 #0 - SUBJECT | |
| Keyword | Econometric models. |
| 650 #0 - SUBJECT | |
| Keyword | Social sciences |
| General subdivision | Statistical methods. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | White, Howard, |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Wuyts, Marc. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | General Books |
| Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Full call number | Accession number | Date last seen | Date last checked out | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Central Library, Sikkim University | Central Library, Sikkim University | General Book Section | 29/08/2016 | 330.015195 MUK/E | P30881 | 03/07/2023 | 02/06/2023 | General Books |