__Ph.D. Econometrics Sequence__

For related Statistics and mathematics courses click here

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The following describes the Economics and Agricultural and
Resource Economics Ph.D. Econometrics courses**.
**In addition some field courses such as ECN 230A taught by
Marianne Bitler have a strong empirical econometrics component.

Fall: 239 Econometrics
Foundations

Winter: 240A Econometrics Methods I

Spring: 240B Econometrics Methods II

Second year:

Fall: 240D or 240C Cross-section
Econometrics or Time Series Econometrics

Winter: 240D or 240C Cross-section Econometrics or Time
Series Econometrics

Spring: 240E Topics in Time Series Econometrics

Winter: 240F Topics in Cross-Section Econometrics

[Note: 240D is often taught the quarter before 240C.]

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**

239 Shu Shen (Fall)

240A Aaron Smith (Winter)

240B Dalia Ghanem (Spring)

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240C Bulat Gafarov (Fall)

240D Takuya Ura (Winter)

240E Oscar Jorda (Spring)

240F Colin Cameron (Spring)

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**

Much of the second-year sequence is also necessary for applied thesis research,

even though the courses are not formally required.

Time-series is split into essentials (240C) and topics (240E).

Similarly cross-section is split into essentials (240D) and topics (240F).

For Economics students taking the econometrics field, the field
is 240C, 240D and one of 240E/240F with grades of B+ or higher.

Course Descriptions

**239 Econometric Foundations**

Lecture—3 hours; discussion—1 hour. Prerequisite: undergraduate
statistics.

Statistical foundations for econometrics.

**240A.** **Econometric Methods I**

Lecture—3 hours; discussion—1 hour. Prerequisite: Economics 290
and a course in linear algebra or the equivalent.

Least squares, instrumental variables, and maximum likelihood
estimation and inference for single equation linear regression
model; finite sample and large sample theory; linear restrictions;
heteroskedasticity; autocorrelation.

**240B**. **Econometric Methods II**

Lecture—3 hours; discussion—1 hour. Prerequisite: course 240A.

Maximum likelihood estimation and testing, nonlinear least
squares, systems of equations, simultaneous equation models,
introductory time series regression.

**240C. Time Series Econometrics **

Lecture—3 hours; discussion—1 hour. Prerequisite: course 240B.
(Usually taught after 240D).

Probability theory; estimation, inference and forecasting of time
series models; trends and non-standard asymptotic theory; vector
time series methods and cointegration; time series models for
higher order moments and transition data; state-space modeling and
the Kalman filter.

**240D. Cross Section Econometrics**

Lecture – 3 hours; discussion – 1 hour. Prerequisite: course 240B.
(Usually taught before 240C).

Estimation and inference for nonlinear regression models for
cross-section data; models for discrete data and for limited
dependent variables; models for panel data; additional topics such
as bootstrap and semiparametric regression.

**240E. Topics in Time Series Econometrics**

Lecture—3 hours; discussion—1 hour. Prerequisite: courses 240A and
240B.

Modern econometric techniques for time series data. Expand on
topics covered in Economics 240A, 240B, and 240C. Contents may
vary from year to year.

**240F. Topics in Cross Section Econometrics**

Lecture—3 hours; discussion—1 hour. Prerequisite: courses 240A,
240B and 240D.

Modern econometric techniques for cross-section data. Expand on
topics covered in Economics 240A, 240B, and 240D. Contents may
vary from year to year.

** **

A. Colin Cameron / UC-Davis Economics / http://cameron.econ.ucdavis.edu

** **