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Adonis Yatchew Contact
information
Department of Economics University of Toronto 150 St. George St., Room 278 Toronto M5S 3G7 Editor-in-Chief, The Energy Journal Tel:
(416) 978-7128 Teaching
ENV282F Big Ideas in Energy 1 – Technology and
Society ENV282 takes an
historical perspective on the development of energy technologies, and how
energy has influenced the development of human societies. The course analyses
historical energy transitions and explores their relevance to current energy
and environmental issues facing humankind. It is team taught by Ben Akrigg
(Classics), Adonis Yatchew (Economics) and Stephen Morris (Physics). ENV382S Big Ideas in Energy 2 – Economics,
Politics and Security. In ENV382, students
will be introduced to the central ideas in economics, politics and security
that are essential to understanding today’s complex energy and environmental
decisions. Security issues will cover regional and geopolitical aspects. The
course is team taught by Ben Akrigg (Classics), Adonis Yatchew (Economics)
and Stephen Morris (Physics). Eco 314F Energy and the Environment This course surveys
important features of energy markets and related environmental challenges.
One of the central objectives is to provide an understanding of the key
economic tools needed to analyse these markets. A
related objective is the development of a framework for understanding the
public discourse on energy and the environment. Topics include the
hydrocarbon economy (oil, natural gas and coal), electricity markets, global
warming and other externalities, renewable energy, conservation, carbon taxes
and 'cap-and-trade'. ECO 414S/3502S Energy and Regulation This course provides
a general treatment of the economics of energy markets and the use of
regulation in addressing environmental and other issues. A central theme is
the search for an appropriate balance between market forces and
regulatory/government intervention. Topics include oil, natural gas, coal and
electricity markets, global warming and other externalities, networks,
feed-in-tariffs, carbon taxes, ‘cap-and-trade’ and incentive regulation. Eco 2401S Econometrics II, Ph.D. This course develops
the conventional econometric tools of the applied economist. Subjects include
asymptotic and bootstrap inference methods, general least squares and its
applications (e.g. heteroscedasticity, autocorrelation, multivariate
regression, mixed estimation, panel data), models where right hand side
variables are correlated with residuals (e.g. errors in variables,
simultaneity), GMM estimation, basic elements of time series analysis
(stationary models, unit roots and co-integration, spectral analysis) and
nonparametric and semiparametric methods. Time permitting, additional topics
which may be covered include duration data and hazard function models, and
limited dependent variable models. Eco 2403S Topics in Econometrics, Ph.D. This a special topics
course, team taught by econometricians in the Economics Department. Energy and Carbon
Flow Diagrams
An
especially useful visual representation of the supply of, and demand for energy
is depicted in Energy Flow diagrams, (also known as Sankey Diagrams). ‘Pipe’
diameters are intended to be roughly proportional to energy flows. The boxes
on the left margin depict supplies of energy from various sources such as
hydrocarbons (natural gas, coal, petroleum), renewables (hydraulic, wind,
solar, geothermal and biomass) and nuclear. Next, consider the demand side
which is divided into residential, commercial, industrial and transportation
uses (pink boxes). The energy in each sector either produces ‘energy
services’ or is lost in the form of ‘rejected energy’ (gray boxes). The least efficient sector is
transportation where almost 80% of the energy is ‘rejected’. The most efficient is the industrial sector
where only 20% is ‘rejected’. Overall,
it might appear that humans are very inefficient, ‘wasting’ well over half of
the energy we produce, but this is primarily a reflection of the state of
technology and the Second Law of Thermodynamics which we will discuss below.
In fact, we have already come a long way.
Fires used to heat and cook in the pre-industrial era ‘wasted’ 95% or
more of the energy embodied in the wood they burned. Carbon Flow diagrams
depict carbon dioxide emissions resulting from the various types of energy
sources. Selected Papers
§
Rational
vs. ‘Feel-Good’ Carbon Policy – Transferability, Subsidiarity and Separation,
A. Yatchew, 2016, Energy Regulation Quarterly, 4:3, 31-40. §
Integration of Renewables into the Ontario
Electricity System, B. Rivard and A. Yatchew, 2016, The Energy Journal, vol 37, Special
Issue 2, pp. 221-242. §
Discerning Trends in Commodity Prices, 2015, D.
Dimitropoulos and A. Yatchew, forthcoming, Macroconomic Dynamics. §
Is Productivity Growth in Electricity
Distribution Negative? An Empirical Analysis Using Ontario Data, 2015, D. Dimitropoulos and A.
Yatchew, May 2015, forthcoming, The
Energy Journal. §
Economics
of Energy: Big Ideas for the
Non-Economist, A. Yatchew, (2014) Energy
Research and Social Science, 1(1), 74-82. § Support
Schemes for Renewable Energy: An Economic Analysis, R. Green and A. Yatchew
(2012), Economics of Energy &
Environmental Policy, 1, 83-98. §
Ontario Feed-in-Tariff Programs, A. Yatchew and
A. Baziliauskas (2011), Energy Policy,
39, 3885-3893. §
Nonparametric Least Squares Estimation in
Derivative Families, Peter Hall and A. Yatchew (2010), Journal of Econometrics, 157, 362-374. §
Nonparametric
Estimation When Data on Derivatives are Available, Peter Hall and A.
Yatchew (2007), Annals of Statistics,
35:1, 300-323. §
International
Welfare Comparisons and Nonparametric Testing of Multivariate Stochastic
Dominance, A. Yatchew and B. McCaig (2007), Journal of Applied Econometrics, 22:5, 951-969. §
Nonparametric
State Price Density Estimation Using Constrained Least Squares and the
Bootstrap A. Yatchew and W. Haerdle (2006), Journal
of Econometrics, 133:2, 579-599. §
Unified
Approach to Testing Functional Hypotheses In Semiparametric Contexts,
with Peter Hall and A. Yatchew, (2005), Journal of Econometrics, 127,
225-252. §
Efficient Estimation of Semi-parametric
Equivalence Scales With Evidence From South Africa , A. Yatchew, Y. Sun and
C. Deri, Journal of Economic and
Business Statistics, (2003), 21, 247-257, Working
Paper Version §
Hydro One
Transmission and Distribution: Should They Remain Combined Or Be
Separated , with Stephen C. Littlechild and A. Yatchew (2002). §
Household Gasoline Demand in Canada, A. Yatchew
and J.A. No, (2001) Econometrica, 69, 1697-1710. §
Incentive Regulation of
Distributing Utilities Using Yardstick Competition, A. Yatchew, (2001), Electricity Journal,
56-60. §
Scale Economies in Electricity Distribution: A
Semiparametric Analysis, (2000) A. Yatchew, Journal of Applied
Econometrics, 15, 187-210. §
Differencing
Methods in Nonparametric Regression : Simple Techniques for the Applied
Econometrician, A. Yatchew (1999) §
An Elementary Nonparametric Differencing Test of
Equality of Regression Functions, (1999), A.
Yatchew, Economics Letters, 271-8. §
Nonparametric Regression Techniques in
Economics, (1998), A. Yatchew, Journal of Economic
Literature, 36, 669-721. §
An Elementary Estimator of the Partial Linear
Model, (1997), Economics Letters, A. Yatchew, 57, 135-43.
See also Vol. 59, 1998 403-5. §
Nonparametric
Least Squares Estimation and Testing of Economic Models A. Yatchew
and L. Bos (1997), Journal of Quantitative
Economics, 13, 81-131. |
Monograph:
Semiparametric
Regression for the Applied Econometrician, A. Yatchew (2003),in Themes in Modern Econometrics,
ed. P.C.B. Phillips, Cambridge
University Cambridge
University Press web at www.cup.org in North America
and www.cambridge.org elsewhere. The paperback
and hardback ISBN numbers are 0-521-01226-0 and 0-521-81283-6 respectively.
There has been an explosion in nonparametric regression techniques in
statistics and econometrics, yet the use of these tools by applied economists
has been much more limited. The motivation and purpose of this book is to
provide an accessible collection of techniques for analyzing nonparametric and
semiparametric models. We focus on nonparametric regression, partial
linear and index models which collectively capture the dominant share in the
applied semiparametric literature.
One of the themes is the idea of
differencing which permits the removal of nonparametric effects from the data
in order to estimate parametric effects. The estimated parametric effects
are in turn removed from the original data and the nonparametric effects are
analyzed. The differencing device allows one to draw not only on the
reservoir of parametric human capital, but also to make use of existing software.
A variety of testing procedures are
covered including simple goodness of fit tests and residual regression
tests. These procedures can be used to test hypotheses such as parametric
and semiparametric specification, significance, monotonicity and additive separability. Other topics include endogeneity of parametric and nonparametric effects, as
well as heteroskedasticity and autocorrelation in the
residuals. Bootstrap procedures are provided.
Worked examples include estimation of
Engel curves and equivalence scales, household gasoline consumption, scale
economies, semiparametric Cobb-Douglas, translog and
CES cost functions, hedonic housing prices, option prices and state price
density estimation. The book should be of interest to a broad range
of economists including those working in industrial organization, labor,
development, urban, energy and financial economics.
Programs (most of which are in S-Plus) and data for
all exercises and examples in the book are available below. The data sets and programs are offered in
conjunction with the title and are for private use only. Reposting, republishing or other usage or
circulation is not permitted without the express written consent of the author.
Programs
- Chapter
1 Programs ©
- Chapter
2 Programs ©
- Chapter
3 Programs ©
- Chapter
4 Programs ©
- Chapter
5 Programs ©
- Chapter
6 Programs ©
- Chapter
7 Programs ©
- Chapter
8 Programs ©
·
Data
- Electricity
Distribution Data ©
- South
African Household Expenditure Data©
- Household
Gasoline Consumption Data©
- Housing
Prices ©
- Demand
for Electricity©