Australian Politics: Paul Keating vs Peter Dutton on Nuclear Power

Nuclear Debate and Personal Attacks

• Former Prime Minister Paul Keating has criticized Peter Dutton, calling him a "charlatan", "wicked and cynical", and "an inveterate climate change denialist" over his stance on nuclear power.
• Keating accused Dutton of trying to hide his denialism with an "industrial fantasy" and resorting to "the most dangerous and expensive energy source on the face of the earth – nuclear power".
• The debate over nuclear power has become personal, with Dutton launching a personal attack on Prime Minister Anthony Albanese, saying he is "a man with a mind still captured in his university years" and "a child in a man's body".
• Senior Labor figures, including Andrew Leigh and Jacinta Allan, responded with memes of three-eyed fish, which Albanese downplayed as a joke.
• Keating argued that Dutton's stance is part of his party's "manic denialism" of climate change, which began with Tony Abbott over a decade ago.
• Keating stated that Dutton's opposition to renewables is a "blight on an earnest community like Australia" and that it "calls into question and deprecates all the government has done to provide Australian business with a reliable and dependable framework for investment in renewables".

Stance on Climate Change and Renewables

• Keating believes that Dutton's stance on nuclear power undermines the efforts to address climate change and carbon sequestration.
• Keating emphasized that renewables are the solution to climate change, saying "nature has given us to underwrite our life on earth".
• He argued that Dutton's stance on nuclear power is a threat to the progress made in providing a framework for investment in renewables.
• Keating concluded that no one interested in public policy should endorse Dutton's "backwardness" and "unreal world view" that nuclear power is a substitute for nature's own remedy.

Chapter Structure

• Chapter 7 covers models used in micro-economics with discrete, partly discrete, or duration dependent variables, including probit, logit, and tobit models, as well as models for count data and duration models.
• Chapter 7 also discusses the sample selection problem and alternative approaches to estimate treatment effects.
• Chapters 8 and 9 discuss time series modelling, including unit roots, cointegration, and error-correction models.
• Chapter 8 focuses on univariate time series models, including ARIMA and GARCH models.
• Chapter 9 covers multivariate time series models, including vector autoregressive models, cointegration, and error-correction models.

Book Structure

• The first part of the book consists of Chapters 2, 3, and 4, covering the linear regression model and OLS estimation method.
• Chapter 2 presents the basics of OLS estimation, with emphasis on its validity under fairly weak conditions.
• Chapter 3 focuses on the interpretation of models and the comparison of alternative specifications.
• Chapter 4 considers deviations from the standard assumptions of the linear model, such as autocorrelation and heteroskedasticity of the error terms.
• Chapter 5 discusses another deviation from the standard assumptions of the linear model, which is fatal for the OLS estimator.

Additional Chapters

• Chapter 10 covers models based on panel data, including micro-economic panels of households and firms, and pooled time series of several countries.
• The book includes two short appendices discussing mathematical and statistical results used throughout the book, including matrix algebra and distribution theory.

Asymptotic Properties of the OLS Estimator

• Consistency of the OLS estimator is discussed in section 2.6.1
• Asymptotic normality of the OLS estimator is discussed in section 2.6.2
• Small samples and asymptotic theory are discussed in section 2.6.3

Illustration: The Capital Asset Pricing Model

• CAPM is presented as a regression model in section 2.7.1
• Estimating and testing the CAPM is discussed in section 2.7.2
• A real-world example of the largest hedge fund is given in section 2.7.3

Multicollinearity

• Multicollinearity is discussed in section 2.8
• An example of individual wages is used to illustrate multicollinearity in section 2.8.1

Missing Data, Outliers, and Influential Observations

• Outliers and influential observations are discussed in section 2.9.1
• Robust estimation methods are presented in section 2.9.2
• Missing observations are discussed in section 2.9.3

Prediction

• Prediction is discussed in section 2.10

Interpreting and Comparing Regression Models

• Interpreting the linear model is discussed in section 3.1
• Selecting the set of regressors is discussed in section 3.2
• Misspecifying the set of regressors is discussed in section 3.2.1
• Selecting regressors is discussed in section 3.2.2
• Comparing non-nested models is discussed in section 3.2.3

Misspecifying the Functional Form

• Nonlinear models are discussed in section 3.3.1
• Testing the functional form is discussed in section 3.3.2
• Testing for a structural break is discussed in section 3.3.3

Illustrations

• Explaining house prices is discussed in section 3.4
• Predicting stock index returns is discussed in section 3.5
• Explaining individual wages is discussed in section 3.6

Heteroskedasticity and Autocorrelation

• Consequences for the OLS estimator are discussed in section 4.1
• Deriving an alternative estimator is discussed in section 4.2
• Heteroskedasticity is discussed in section 4.3
• Testing for heteroskedasticity is discussed in section 4.4
• Autocorrelation is discussed in section 4.6
• Testing for first-order autocorrelation is discussed in section 4.7