Econometrics for Dummies by Roberto Pedace (John Wiley & Sons, Inc., New Jersey, 2013), pp xvi + 342 Hoa Thi Minh Nguyen1 February 2015 Econometrics for Dummies aims to provide students with a ‘short and simple version of a first-semester course in econometrics’ The book covers the basic parts of a standard undergraduate econometrics textbook, and is a good reference text in that sense But more so, and following the style made popular through the Wiley “for Dummies” series, this book gives priority to simplicity in presentation and explanation to make econometrics more manageable This feature alone should help draw the attention of students who are learning econometrics for the first time Econometrics for Dummies is organized much like a standard undergraduate econometrics textbook, with 19 chapters grouped into parts It begins with a review of key concepts in probability and statistics, and then discusses the classical linear regression model (CLRM), its assumptions and the use of the Ordinary Least Squares (OLS) estimator — the most popular estimation technique in econometrics Discussion and treatment of violations of CLRM is provided in part IV, followed by an introduction to the Maximum Likelihood Estimator (MLE) applied to qualitative and limited dependent variable models A brief introduction to models used for time series, pooled cross-section and panel data is provided in part VI before an useful conclusion that outlines good and bad habits in doing empirical research closes the book Crawford School of Public Policy, Crawford Building (132), Lennox Crossing, Australian National University, Canberra, ACT 2601, Australia Email: hoa.nguyen@anu.edu.au The main strength of the book is its ‘straightforward manner’ which simplifies econometrics so that it is accessible for beginners Padace achieved his stated purpose of the book by using simple language, a ‘busy readers–oriented’ writing style, coupled with stand-out icons such as “Remember”, “Warning”, “Tip”, etc to help readers manage and skim the book easily The guide on the use of STATA along the way is a useful bonus to facilitate application of econometric techniques The writing style and presentation of this book is in stark contrast to many typical econometrics textbooks which tend to exhaust students’ desire to learn econometrics through an emphasis on more sophisticated technique However, providing simple and straightfoward tips in the field like econometrics is a real challenge While Pedace’s effort is laudable, some substantive errors such as the ones below are a true drawback Confusion in key concepts: (a) Estimator versus estimate: The former is a rule for combining data to produce a numerical value for a population parameter while the later is a numerical value obtained by applying an estimator to a particular sample of data It is crucial that students can distinguish between the two concepts, which are typically explained in a thorough manner in standard econometrics textbooks However, in Econometrics for Dummies, the explanation here is simply wrong For example, ‘When you calculate descriptive measures using sample data, the values are called estimators (or statistics)’ (pg 40, under the icon “Remember”); ‘The calculus and result in easy-to-use formulas for calculating the regression coefficients (estimates of the slope and intercept)’ (pg 80, under the icon “Remember”) This confusion is not simply a typo and may explain why the same notation is used for estimators and estimates throughout in the book (for example, pg 77, 80-81) (b) Population regression models (PRM) versus sample regression models (SRM): Explanations on PRM and SRM are confusing For example, PRM is explained as: ‘ the relationship you’ve assumed in may contain errors when a specific observation is chosen at random from the population This is known as the stochastic population regression function ’ (pg 65); while SRM is explained as: ‘In most applications, you’ll need to work with sample data to estimate your PRM ’ (pg 67); ‘When using crosssectional data, you assume that the observations represent a random draw from your population of interest’ (pg 69) Lack of precision or potentially misleading materials: Durbin-Watson (DW) test versus Breusch-Godfrey (BG) test: Padace describes the key difference between the two tests such that the former is for AR(1) and the latter is for AR(q) where q is ‘some number greater than or equal to 1’ and ‘known as Durbin’s alternative statistic’ with q=1’ (pg 221) In fact, the BG test is superior to DW not only in the order of autocorrelation but also for its application in models where the lag(s) of the dependent variable is (are) used as an independent variable(s) [Breusch(1978)] Furthermore, the DW test requires satisfaction of CLM assumptions and can be used with small sample data while such satisfaction of CLM assumptions is not required for a BG test, an asymptotically justified test [Wooldridge(2012)] Given the substantial practical disadvantage of DW test, the suggestion that DW test is ‘the most popular test for autocorrelation’ can be misleading The least squares principle: ‘The least squares (LS) principle states that the SRF should be constructed (with the constant and slope values) so that the sum of squared distance is minimised.’ (pg 76) This strong statement could be misunderstood to imply that a model has to have both a constant and slope(s) While it is rare to have a model without a constant, the LS principle, which aims to minimise the squared residuals when fitting a model to data, is not violated in this case Justification of OLS technique: The author apparently over-emphasises the properties of OLS solutions in terms of its popularity in econometrics (pg 76) These properties include things such as the regression line always passes through the sample means; the mean of the residual is zero; the residuals are uncorrelated with the observed values of independent variable(s), etc While those properties are good, OLS is popular largely for meeting criteria that are considered important for most researchers in estimator selection including unbiasedness, consistency and efficiency when certain assumptions are satisfied, in addition to computational ease Failure to understand these estimator selection criteria would prevent students from understanding why there is a need for alternative estimators such as Method of Moments and MLE Estimate and interpret coefficients of logit and profit models: Coefficient estimates from logit and probit models might have been explained with more care due to non-linearity of these two models For example, results on coefficient estimates are shown in Econometrics for the Dummies using the Stata post-estimation command ‘mfx’ While this command is handy, it would be very useful for students to know that it calculates the marginal effect at sample means (the default option) and that results generated would be different if the effect is evaluated at different values of independent variables, all due to the non-linearity of the logit and probit models Lack of consistency in notation: Some rules in notation set at the beginning of the book are not followed For example, the rule set on page 77: ‘Notice how the mathematical representation of the SRF uses hats (ˆ) above the coefficients and error term I use this symbol to denote that these numbers are estimates of their true population values’ is not followed in page 227 Explanations on notation are also missing at times For example, what d stands for in the formula for Thiel’s estimator (pg 224)? Overall, Econometrics for the Dummies is a student-friendly reference book Padace has achieved his stated purpose of the book, namely to make a hard subject like Econometrics manageable for beginners Should more attention be paid to substance and the language used to give advice and suggestions to students? I have no doubt about this If done, hopefully in the next edition, I also have no doubt that this book would win the hearts and minds of many students who are starting to learn econometrics References Breusch(1978) Breusch, T S., 1978 Testing for autocorrelation in dynamic linear models Australian Economic Papers 17 (31), 334–355 Wooldridge(2012) Wooldridge, J., 2012 Introductory econometrics: A modern approach Fifth Edition Mason: South-Western Cengage Learning ... concepts, which are typically explained in a thorough manner in standard econometrics textbooks However, in Econometrics for Dummies, the explanation here is simply wrong For example, ‘When you... Explanations on notation are also missing at times For example, what d stands for in the formula for Thiel’s estimator (pg 224)? Overall, Econometrics for the Dummies is a student-friendly reference... models For example, results on coefficient estimates are shown in Econometrics for the Dummies using the Stata post-estimation command ‘mfx’ While this command is handy, it would be very useful for