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    Deirdre McCloskey, Distinguished Professor of Economics,...
    featured session posted May 24, 2012 by AAA HQ, tagged Home Page Announcement 
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    Deirdre McCloskey, Distinguished Professor of Economics, History, English, and Communication, University of Illinois at Chicago
    Monday Plenary, August 6, 2012 ~ 8:30am–9:45am

    Deirdre McCloskey teaches economics, history, English, and communication at the University of Illinois at Chicago. A well-known economist and historian and rhetorician, she has written sixteen books and around 400 scholarly pieces on topics ranging from technical economics and statistics to transgender advocacy and the ethics of the bourgeois virtues. She is known as a "conservative" economist, Chicago-School style (she taught for 12 years there), but protests that "I'm a literary, quantitative, postmodern, free-market, progressive Episcopalian, Midwestern woman from Boston who was once a man. Not 'conservative'! I'm a Christian libertarian."


    Her latest book, Bourgeois Dignity: Why Economics Can't Explain the Modern World (University of Chicago Press, 2010), which argues that an ideological change rather than saving or exploitation is what made us rich, is the second in a series of four on The Bourgeois Era. The first was The Bourgeois Virtues: Ethics for an Age of Commerce (2006), asking if a participant in a capitalist economy can still have an ethical life (briefly, yes). With Stephen Ziliak she wrote in 2008, The Cult of Statistical Significance (2008), which criticizes the proliferation of tests of "significance," and was in 2011 the basis of a Supreme Court decision



    • Robert E Jensen

      Essays on the State of Accounting Scholarship ---

      The Sad State of Economic Theory and Research --- 

      Acceptance Speech for the August 15, 2002 American Accounting Association's Outstanding Educator Award ---

      How Accountics Scientists Should Change: 
      "Frankly, Scarlett, after I get a hit for my resume in The Accounting Review I just don't give a damn"
      One more mission in what's left of my life will be to try to change this 

      The Cult of Statistical Significance:  How Standard Error Costs Us Jobs, Justice, and Lives, by Stephen T. Ziliak and Deirdre N. McCloskey (Ann Arbor:  University of Michigan Press, ISBN-13: 978-472-05007-9, 2007)

      Page 206
      Like scientists today in medical and economic and other sizeless sciences, Pearson mistook a large sample size for the definite, substantive significance---evidence s Hayek put it, of "wholes." But it was as Hayek said "just an illusion." Pearson's columns of sparkling asterisks, though quantitative in appearance and as appealing a is the simple truth of the sky, signified nothing.

      In Accountics Science R2 = 0.0004 = (-.02)(-.02) Can Be Deemed a Statistically Significant Linear Relationship ---


      "So you want to get a Ph.D.?" by David Wood, BYU ---

      Do You Want to Teach? ---

      Jensen Comment
      Here are some added positives and negatives to consider, especially if you are currently a practicing accountant considering becoming a professor.

      Accountancy Doctoral Program Information from Jim Hasselback --- 

      Why must all accounting doctoral programs be social science (particularly econometrics) "accountics" doctoral programs?

      What went wrong in accounting/accountics research?

      Bob Jensen's Codec Saga: How I Lost a Big Part of My Life's Work
      Until My Friend Rick Lillie Solved My Problem

      One of the most popular Excel spreadsheets that Bob Jensen ever provided to his students ---

    • Robert E Jensen

      Some Comments About Accountics Science Versus Real Science

      This is the lead article in the May 2013 edition of The Accounting Review
      "On Estimating Conditional Conservatism

      Ray Ball (The University of Chicago)
      S. P. Kothari )Massachusetts Institute of Technology)
      Valeri V. Nikolaev (The University of Chicago)

      The Accounting Review, Volume 88, No. 3, May 2013, pp. 755-788

      The concept of conditional conservatism (asymmetric earnings timeliness) has provided new insight into financial reporting and stimulated considerable research since Basu (1997). Patatoukas and Thomas (2011) report bias in firm-level cross-sectional asymmetry estimates that they attribute to scale effects. We do not agree with their advice that researchers should avoid conditional conservatism estimates and inferences from research based on such estimates. Our theoretical and empirical analyses suggest the explanation is a correlated omitted variables problem that can be addressed in a straightforward fashion, including fixed-effects regression. Correlation between the expected components of earnings and returns biases estimates of how earnings incorporate the information contained in returns. Further, the correlation varies with returns, biasing asymmetric timeliness estimates. When firm-specific effects are taken into account, estimates do not exhibit the bias, are statistically and economically significant, are consistent with priors, and behave as a predictable function of book-to-market, size, and leverage.

      . . .

      We build on and provide a different interpretation of the anomalous evidence reported by PT. We begin by replicating their [Basu (1997). Patatoukas and Thomas (2011)] results. We then provide evidence that scale-related effects are not the explanation. We control for scale by sorting observations into relatively narrow portfolios based on price, such that within each portfolio approximately 99 percent of the cross-sectional variation in scale is eliminated. If scale effects explain the anomalous evidence, then it would disappear within these portfolios, but the estimated asymmetric timeliness remains considerable. We conclude that the data do not support the scale-related explanation.4 It thus becomes necessary to look for a better explanation.

      Continued in article

      Jensen Comment
      The good news is that the earlier findings were replicated. This is not common in accountics science research. The bad news is that such replications took 16 years and two years respectively. And the probability that TAR will publish a one or more commentaries on these findings is virtually zero.

      How does this differ from real science?
      In real science most findings are replicated before or very quickly after publication of scientific findings. And interest is in the reproducible results without also requiring an extension of the research for publication of the replication outcomes.

      In accountics science there is little incentive to perform exact replications since top accountics science journals neither demand such replications nor will they publish (even in commentaries) replication outcomes. A necessary condition to publish replication outcomes in accountics science is the extend the research into new frontiers.

      How long will it take for somebody to replicate these May 2013 findings of Ball, Kothari, and Nikolaev? If the past is any indicator of the future the BKN findings will never be replicated. If they are replicated it will most likely take years before we receive notice of such replication in an extension of the BKN research published in 2013.

      Bob Jensen's threads on replication and commentaries in accountics science ---

      In statistics what is a "winsorized mean?"

      Answer in Wikipedia ---

      An analogy that takes me back to my early years of factor analysis is Procreates Analysis ---

      "The Role of Financial Reporting Quality in Mitigating the Constraining Effect of Dividend Policy on Investment Decisions"

      Santhosh Ramalingegowda (The University of Georgia
      Chuan-San Wang (National Taiwan University)
      Yong Yu (The University of Texas at Austin)

      The Accounting Review, Vol. 88, No. 3, May 2013, pp. 1007-1040

      Miller and Modigliani's (1961) dividend irrelevance theorem predicts that in perfect capital markets dividend policy should not affect investment decisions. Yet in imperfect markets, external funding constraints that stem from information asymmetry can force firms to forgo valuable investment projects in order to pay dividends. We find that high-quality financial reporting significantly mitigates the negative effect of dividends on investments, especially on R&D investments. Further, this mitigating role of financial reporting quality is particularly important among firms with a larger portion of firm value attributable to growth options. In addition, we show that the mitigating role of high-quality financial reporting is more pronounced among firms that have decreased dividends than among firms that have increased dividends. These results highlight the important role of financial reporting quality in mitigating the conflict between firms' investment and dividend decisions and thereby reducing the likelihood that firms forgo valuable investment projects in order to pay dividends.

      . . .

      Panel A of Table 1 reports the descriptive statistics of our main and control variables in Equation (1). To mitigate the influence of potential outliers, we winsorize all continuous variables at the 1 percent and 99 percent levels. The mean and median values of Total Investment are 0.14 and 0.09 respectively. The mean and median values of R&D Investment (Capital Investment) are 0.05 (0.06) and 0.00 (0.04), respectively. Because we multiply RQ−1 by −1 so that higher RQ−1 indicates higher reporting quality, RQ−1 has negative values with the mean and median of −0.05 and −0.04, respectively. The above distributions are similar to prior research (e.g., Biddle et al. 2009). The mean and median values of Dividend are 0.01 and 0.00, respectively, consistent with many sample firms not paying any dividends. The descriptive statistics of control variables are similar to prior research (e.g., Biddle et al. 2009). Panels B and C of Table 1 report the Pearson and Spearman correlations among our variables. Consistent with dividends having a constraining effect on investments (Brav et al. 2005; Daniel et al. 2010), we find that Total Investment and R&D Investment are significantly negatively correlated with Dividend.

      Continued in article

      Jensen Comment
      With statistical inference testing on such an enormous sample size this may be yet another accountics science illustration of misleading statistical inferences that Deirdre McCloskey warned about (The Cult of Statistical Significance) in a plenary session at the 2011 AAA annual meetings in 2012 ---
      I had the privilege to be one of the discussants of her amazing presentation.

      The basic problem of statistical inference testing on enormous samples is that the null hypothesis is almost always rejected even when departures from the null are infinitesimal.

      2012 AAA Meeting Plenary Speakers and Response Panel Videos ---
      I think you have to be a an AAA member and log into the AAA Commons to view these videos.
      Bob Jensen is an obscure speaker following the handsome Rob Bloomfield
      in the 1.02 Deirdre McCloskey Follow-up Panel—Video ---

      My threads on Deidre McCloskey and my own talk are at

    • Robert E Jensen

      Redefine Statistical Significance
      David Giles:  Econometrics Reading List for September 2017---


      A little belatedly, here is my September reading list:
      • Benjamin, D. J. et al., 2017. Redefine statistical significance. Pre-print.
      • Jiang, B., G. Athanasopoulos, R. J. Hyndman, A. Panagiotelis, and F. Vahid, 2017. Macroeconomic forecasting for Australia using a large number of predictors. Working Paper 2/17, Department of Econometrics and Business Statistics, Monash University.
      • Knaeble, D. and S. Dutter, 2017. Reversals of least-square estimates and model-invariant estimations for directions of unique effects. The American Statistician, 71, 97-105.
      • Moiseev, N. A., 2017. Forecasting time series of economic processes by model averaging across data frames of various lengths. Journal of Statistical Computation and Simulation, 87, 3111-3131.
      • Stewart, K. G., 2017. Normalized CES supply systems: Replication of Klump, McAdam and Willman (2007). Journal of Applied Econometrics, in press.
      • Tsai, A. C., M. Liou, M. Simak, and P. E. Cheng, 2017. On hyperbolic transformations to normality. Computational Statistics and Data Analysis, 115, 250-266,