Honoring Gregory Chow, Econometrics Pioneer

Wonderful to see this.  So many massive contributions. Structural change, optimal control, development, and much more.

New Issue for Frontiers of Economics in China (FEC)

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Frontiers of Economics in China (FEC)

CONTENTS (Vol. 11, No. 1, 2016)

Special Issue in Honor of Gregory Chow: Part І

Preface for Special Issue Honoring Gregory Chow

Dwight H. Perkins

Front. Econ. China. 2016, 11 (1): 1-6.   DOI: 10.3868/s060-005-016-0001-5

Abstract   PDF (71KB)

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The Chow Program and Ford Classes: An Introduction to Special Issue in Honor of Gregory Chow

Zhiqi Chen

Front. Econ. China. 2016, 11 (1): 7-8.   DOI: 10.3868/s060-005-016-0002-2

Abstract   PDF (56KB)

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Structural Changes in High Dimensional Factor Models

Jushan Bai,Xu Han

Front. Econ. China. 2016, 11 (1): 9-39.   DOI: 10.3868/s060-005-016-0003-9

Abstract   PDF (311KB)

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Inference for Optimal Split Point in Conditional Quantiles

Yanqin Fan,Ruixuan Liu,Dongming Zhu

Front. Econ. China. 2016, 11 (1): 40-59.   DOI: 10.3868/s060-005-016-0004-6

Abstract   PDF (325KB)

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The Dynamic Process of Economic Takeoff and Industrial Transformation

Ming-Jen Chang,Ping Wang,Danyang Xie

Front. Econ. China. 2016, 11 (1): 60-87.   DOI: 10.3868/s060-005-016-0005-3

Abstract   PDF (3137KB)

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Transfers and the Samuelson Rule in Stock Externality Provision—Why Do We Need Them Both?

Zili Yang

Front. Econ. China. 2016, 11 (1): 88-103.   DOI: 10.3868/s060-005-016-0006-0

Abstract   PDF (461KB)

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Freedom in Welfare Economics

Yongsheng Xu

Front. Econ. China. 2016, 11 (1): 104-122.   DOI: 10.3868/s060-005-016-0007-7

Abstract   PDF (375KB)

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An Analysis of Energy and Environment Efficiency of China’s Iron and Steel Industry

Ming Lei,Zihan Yin

Front. Econ. China. 2016, 11 (1): 123-141.   DOI: 10.3868/s060-005-016-0008-4

Abstract   PDF (186KB)

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Applications of an IS-MP Model with Yield Curve

X. Henry Wang,Bill Z. Yang

Front. Econ. China. 2016, 11 (1): 142-155.   DOI: 10.3868/s060-005-016-0009-1

Abstract   PDF (327KB)

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China’s Capital Stock Series by Region and Sector

Yanrui Wu

Front. Econ. China. 2016, 11 (1): 156-172.   DOI: 10.3868/s060-005-016-0010-5

Abstract   PDF (283KB)

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With warmest regards,

Frank H. Liu

Frontiers of Economics in China (FEC), Higher Education Press

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The Amazing Rise of Economic Measurement

Economic measurement, empirical economics, evidence-based economics -- call it what you want, but lately it's been trouncing the competition.  The table below, which speaks for itself, is from Bentley MacLeod's interesting new paper, "The Human Capital Approach to Inference" (more on it in a later post).  One can only wonder how its 2023 row will look.   (What a tectonic shift from the 70's and 80's.  I was a grad student in the 80's, when all the big profs were theorists and all the big students were aspiring theorists...)

Central Bank Forecast Accuracy

[Sorry for being AWOL.  Like everyone else, I'm generally three feet underwater and breathing through a straw, but in March and April it seems that even the straw goes under.  Anyway, lots of stuff in the pipeline, so let's try to get going again.]

Rummaging around in the basement I just found this Bloomberg piece on comparative central bank forecasting performance.  A friend emailed it a little more than six months ago (ouch), but it's still interesting.  Maybe you missed it.

Bloomberg's first-ever ranking of central bank forecasting, which is relied on by business and finance, turns up winners and losersBloomberg's first-ever ranking of central bank forecasting, which is relied on by business and finance, turns up winners and losers

Miserable Teaching Evaluations

I have always disliked teaching evaluations, feeling that they fail to measure true teaching effectiveness. And it's not just sour grapes -- really, I swear, I generally do fine and have won several teaching awards. Rather, I simply think that teaching evaluations create bad incentives. Ask yourself: Is the behavior that maximizes teaching evaluations the same behavior that maximizes true teaching effectiveness? No way.

But it may be much worse than that. Check out the abstract below for a seminar to be presented in Penn Statistics next week by Philip Stark, a Berkeley statistician (and Associate Dean of the Division of Mathematical and Physical Sciences). Paper here.

TEACHING EVALUATIONS (MOSTLY) DO NOT MEASURE TEACHING EFFECTIVENESS

PHILIP STARK - UNIVERSITY OF CALIFORNIA, BERKELEY

Joint work with Anne Boring (SciencesPo) and Kellie Ottoboni (UC Berkeley)

Student evaluations of teaching (SET) are widely used in academic personnel decisions as a measure of teaching effectiveness. We show:

·         SET are biased against female instructors by an amount that is large and statistically significant

·         the bias affects how students rate even putatively objective aspects of teaching, such as how promptly assignments are graded

·         the bias varies by discipline and by student gender, among other things

·         it is not possible to adjust for the bias, because it depends on so many factors

·         SET are more sensitive to students' gender bias and grade expectations than they are to teaching effectiveness

·         gender biases can be large enough to cause more effective instructors to get lower SET than less effective instructors.

These findings are based on permutation tests applied to two datasets: 23,001 SET of 379 instructors by 4,423 students in six mandatory first-year courses in a five-year natural experiment at a French university, and 43 SET for four sections of an online course in a randomized, controlled, blind experiment at a US university. 

Georgetown Center for Econometric Practice

Check out the Georgetown Center for Econometric Practice (GCEP).  Web page here.  Facebook page here.  Screenshot below.

GEORGETOWN CENTER FOR ECONOMETRIC PRACTICE (GCEP)

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CONTACT GCEP

Georgetown Center for Econometric Practice
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gcep@georgetown.edu

GCEP PARTNERS

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GCEP TRAINING COURSES

Our training courses are designed to be of particular benefit to economists and social scientists in the public and private sectors wanting to know how to use econometric methods and a variety of data to inform policy making.

They run over 1 or 2 days in the new facilities of Georgetown University School of Continuing Studies located in downtown Washington, D.C. at 640 Massachusets Avenue N.W.