Department of Agricultural and Resource Economics
University of California, Davis, CA
ARE106
Richard Green
Spring Quarter
April 26, 2013
FIRST MIDTERM
Show all your work in order to receive full credit.
1. (25 pts.) Consider the computer printout on the n

Chapter 2: Properties of the regression coefficients and hypothesis testing
Chapter 2: Properties of the regression
coefficients and hypothesis testing
Overview
Chapter 1 introduced least squares regression analysis, a mathematical
technique for fitting a

Chapter 4: Transformations of variables
Chapter 4: Transformations of variables
Overview
This chapter shows how least squares regression analysis can be extended
to fit nonlinear models. Sometimes an apparently nonlinear model can be
linearised by taking

Chapter 5: Dummy variables
Chapter 5: Dummy variables
Overview
This chapter explains the definition and use of a dummy variable, a device
for allowing qualitative characteristics to be introduced into the regression
specification. Although the intercept d

Chapter 6: Specification of regression variables
Chapter 6: Specification of regression
variables
Overview
This chapter treats a variety of topics relating to the specification of the
variables in a regression model. First there are the consequences for t

Chapter 8: Stochastic regressors and measurement errors
Chapter 8: Stochastic regressors and
measurement errors
Overview
Until this point it has been assumed that the only random element in a
regression model is the disturbance term. This chapter extends

Chapter 7: Heteroscedasticity
Chapter 7: Heteroscedasticity
Overview
This chapter begins with a general discussion of homoscedasticity and
heteroscedasticity: the meanings of the terms, the reasons why the
distribution of a disturbance term may be subject

Chapter 11: Models using time series data
Chapter 11: Models using time series
data
Overview
This chapter introduces the application of regression analysis to time
series data, beginning with static models and then proceeding to dynamic
models with lagged

Chapter 10: Binary choice
Chapter 10: Binary choice and limited
dependent variable models, and
maximum likelihood estimation
Overview
The first part of this chapter describes the linear probability model,
logit analysis, and probit analysis, three techniq

Chapter 9: Simultaneous equations estimation
Chapter 9: Simultaneous equations
estimation
Overview
Until this point the analysis has been confined to the fitting of a single
regression equation on its own. In practice, most economic relationships
interact

Chapter 14: Introduction to panel data
Chapter 14: Introduction to panel data
Overview
Increasingly, researchers are now using panel data where possible in
preference to cross-sectional data. One major reason is that dynamics may
be explored with panel da

Chapter 13: Introduction to nonstationary time series
Chapter 13: Introduction to nonstationary
time series
Overview
This chapter begins by defining the concepts of stationarity and
nonstationarity as applied to univariate time series and, in the case of

Review: Random variables and sampling theory
Review: Random variables and sampling
theory
Overview
The textbook and this guide assume that you have previously studied basic
statistical theory and have a sound understanding of the following topics:
descr

Chapter 1: Simple regression analysis
Chapter 1: Simple regression analysis
Overview
This chapter introduces the least squares criterion of goodness of fit and
demonstrates, first through examples and then in the general case, how
it may be used to develo

Chapter 4: Transformations of variables
Chapter 4: Transformations of variables
Overview
This chapter shows how least squares regression analysis can be extended
to fit nonlinear models. Sometimes an apparently nonlinear model can be
linearised by taking

Department of Agricultural and Resource Economics
University of California, Davis, CA
ARE106
Richard Green
Spring Quarter
June 10, 2013
FINAL
Show all your work in order to receive full credit. Each problem is worth 25
points.
1. Consider the attached gre

KEY
Department of Agricultural and Resource Economics
University of California, Davis, CA
ARE106 Winter Quarter
Richard Green Feb. 28, 2014
MIDTERM 2
Show all your work in order to receive full credit. Each problem is worth 25
points.
1. Consider the foll

Answer Key to HW #4
GRETL
(A)
(a)All of the coefficients are significant because their p-value is smaller than the
significant level of .05.
(b) The test for the significance of the goo

Department of Agricultural and Resource Economics
University of Caliimia, Davis, CA
ARE106 Fall Quarter
Richard Green Dec. 14, 2012
FINAL
' ,Show all your work in order to receive lll credit. Each problem is worth 25
points.
1. Consider the attached gretl

Department of Agricultural and Resource Economics
University of California, Davis, CA
ARE106
Richard Green
Fall Quarter
Dec. 5, 2011
FINAL
Show all your work in order to receive full credit. Each problem is worth 20
points.
1. Which of the following are

Department of Agricultural and Resource Economics
University of California, Davis, CA
Fall Quarter
Dec. 11,2013
AREl06
Richard Green
FI1\AL
Show all your work in order to receive full credit
1. (25 pts.) Given the model
IuGALARY) :3.809 +o.044YEAR,S
(e2.1

Department of Agricultural and Resource Economics
University of California, Davis, CA
ARE106
Richard Green
Fall Quarter
Dec. 11, 2013
FINAL
Show all your work in order to receive full credit
1. (25 pts.) Given the model
ln( SALARY ) = 3.809 + 0.044YEARS 0

Department of Agricultural and Resource Economics
University of California, Davis, CA
ARE 1 06 Fall Quarter
Richard Green Dec. 5, 2011
FINAL
Show all your work in order to receive full credit. Each problem is worth 20
points.
1. Which of the following are

Department of Agricultural and Resource Economics
University of California, Davis, CA
ARE106
Richard Green
Fall Quarter
Oct. 24, 2011
MIDTERM 1
Show all your work in order to receive full credit. Formulas are at the end of
the exam. Each problem is worth

Department of Agricultural and Resource Economics
University of California, Davis, CA
ARE106
Richard Green
Fall Quarter
Nov. 19, 2013
MIDTERM II
Show all your work in order to receive full credit.
1. a. (5 pts.) Define consistency and illustrate the conce

Applied Econometrics
AppliedEconometrics
Second edition
Dimitrios Asteriou and
Stephen G. Hall
Applied Econometrics: A Modern Approach using Eviews and Microfit Dr
D Asteriou
Applied Econometrics
AUTOCORRELATION
2
1. What is Autocorrelation
2. What Causes