Analysis of economic data

"This revised and updated second edition of Analysis of Economic Data builds upon the success of the first edition in teaching methods of data analysis to students whose primary interest is not in econometrics, statistics or mathematics, as well as those who are facing economic data analysis for the first time. It shows students how to apply econometric techniques in the context of real-world empirical problems."--BOOK JACKET. Read more... Content: Preface to First Edition -- Preface to Second Edition -- Chapter 1. Introduction -- Organization of the book -- Useful background -- Appendix: Mathematical concepts used in this book -- Chapter 2. Basic data handling -- Types of economic data -- Obtaining data -- Working with data: graphical methods -- Working with data: descriptive statistics -- Chapter summary -- Appendix: Index numbers -- Appendix: Advanced descriptive statistics -- Endnotes- -- Chapter 3. Correlation -- Understanding correlation -- Understanding correlation through verbal reasoning -- Understanding why variables are correlated -- Understanding correlation through XY-plots -- Correlation between several variables -- Chapter summary -- Appendix: Mathematical details -- Endnotes -- Chapter 4. An Introduction to simple regression -- Regression as a best fitting line -- Interpreting OLS estimates -- Fitted values and R2: measuring the fit of a regression model -- Nonlinearity in regression -- Chapter summary -- Appendix: Mathematical details -- Endnotes -- Chapter 5. Statistical aspects of regression -- Which factors affect the accuracy of the estimate? -- Calculating a confidence interval for Testing whether =0 -- Hypothesis testing involving R2: the F-statistic -- Chapter summary -- Appendix: Using statistical tables for testing whether =0 -- Endnotes -- Chapter 6. Multiple regression -- Regression as a best fitting line -- Ordinary least squares estimation of the multiple regression model -- Statistical aspects of multiple regression -- Interpreting OLS estimates -- Pitfalls of using simple regression in a multiple regression context -- Omitted variables bias -- Multicollinearity -- Chapter summary -- Appendix: Mathematical interpretation of regression coefficients -- Endnotes -- Chapter 7. Regression with dummy variables -- Simple regression with a dummy variable -- Multiple regression with dummy variables -- Multiple regression with both dummy and non-dummy explanatory variables -- Interacting dummy and non-dummy variables -- What if the dependent variable is a dummy? -- Chapter summary -- Endnote -- Chapter 8. Regression with time lags: distributed lag models Aside on lagged variables -- Aside on notation -- Selection of lag order -- Chapter summary -- Appendix: Other distributed lag models -- Endnotes -- Chapter 9. Univariate time series analysis -- The autocorrelation function -- The autoregressive model for univariate time series -- Nonstationary versus stationary time series -- Extensions of the AR(1) model -- Testing the AR(p) with deterministic trend model -- Chapter summary -- Appendix: Mathematical intuition for the AR(1) model -- Endnotes -- Chapter 10. Regression with time series variables -- Time series regression when X and Y are stationary -- Time series regression when Y and X have unit roots: spurious regression -- Time series regression when Y and X have unit roots: cointegration -- Time series regression when X and Y are cointegrated: the error correction model -- Time series regression when X and Y have unit roots but are not cointegrated -- Chapter summary -- Endnotes -- Chapter 11. Applications of time series methods in macroeconomics and finance -- Volatility in asset prices -- Granger causality -- Vector autoregressions Chapter summary -- Appendix: Hypothesis tests involving more than one coefficient -- Endnotes -- Chapter 12. Limitations and extensions -- Problems that occur when the dependent variable has particular forms -- Problems that occur when the errors have particular forms -- Problems that call for the use of multiple equation models -- Chapter summary -- Endnotes -- Appendix A Writing an empirical project -- Description of a typical empirical project -- General considerations -- Project topics -- Appendix B -- Data directory -- Index. Abstract: "This revised and updated second edition of Analysis of Economic Data builds upon the success of the first edition in teaching methods of data analysis to students whose primary interest is not in econometrics, statistics or mathematics, as well as those who are facing economic data analysis for the first time. It shows students how to apply econometric techniques in the context of real-world empirical problems."--BOOK JACKET Analysis of Economic Data teaches methods of data analysis to students whose primary interest is not in econometrics, statistics or mathematics. It shows students how to apply econometric techniques in the context of real-world empirical problems. It adopts a largely non-mathematical approach relying on verbal and graphical intuition and covers most of the tools used in modern econometrics research e.g. correlation, regression and extensions for time-series methods. It contains extensive use of real data examples and involves readers in hands-on computer work. The new edition includes new material on the mathematical background required by students and, for those readers unfamiliar with this background, a brief explanation of the relevant mathematics. Topics covered include: the equation of a straight line, the summation operator, and logarithms. The author also includes a much greater discussion of data transformations such as growth rates and index numbers. More material will also be added on data sources, largely focusing on internet data sources. Gary Koop has a very high international profile in the field of econometrics and is well known for his books and numerous journal publications. The second edition provides stronger coverage of the relevant introductory mathematics, including: the equation of a straight line, the summation operator, and logarithms. This will make the book more accessible for those students who have limited mathematical skills. Greater discussion is also provided of data transformations such as growth rate and index numbers. Index numbers are becoming increasingly important and are frequently used in economics courses. More material will also be provided on data sources, especially internet data sources which are becoming extremely important as a means of gathering data. Some students have difficulty with the collection of data and the inclusion of this material will help those students. Introducing readers to the basic techniques of economic data analysis, this book takes a non-mathematical approach and relies mainly on verbal and graphical methods There are several types of professional economists working in the world today. Title from ebook title screen (viewed on May 23, 2005).