Essays in the Application of Linear and Non-linear Bayesian Var Models to the Macroeconomic Impacts of Energy Price Shocks


Book Description

This thesis is a collection of five self contained empirical macroeconomic papers on the asymmetric effects of energy price shocks on various economies. Chapter 1 formally determines the number of regime changes in the US natural gas market by employing a MS-VAR model. Estimated using Bayesian methods, three regimes are identified for the period 1980 - 2016, namely, before the Decontrol Act, after the Decontrol Act and the Recession. The results show that the natural gas market tends to be much more sensitive to market fundamental shocks occurring in a Recession regime than in the other regimes. Augmenting the model by incorporating the price of crude oil, the results reveal that the impacts of oil price shocks on natural gas prices are relatively small. Chapter 2 provides new empirical evidence on the asymmetric reactions of the U.S. natural gas market and the U.S. economy to its market fundamental shocks in different phases of the business cycle. To this end, we employ a ST-VAR model to capture the asymmetric responses depending on economic conditions. Our results indicate that in contrast to the prediction made by a linear VAR model, the STVAR model provides a plausible explanation to the behavior of the U.S. natural gas market, which asymmetrically reacts in bad times and good times. Chapter 3 examines the relationship between China's economic growth and global oil market fluctuations between 1992Q1 and 2015Q3. We find that: (1) the time varying parameter VAR with stochastic volatility provides a better fit as compared to it's constant counterparts; (2) the impacts of intertemporal global oil price shocks on China's output are often small and temporary in nature; (3) oil supply and specific oil demand shocks generally produce negative movements in China's GDP growth whilst oil demand shocks tend to have positive effects; (4) domestic output shocks have no significant impact on price or quantity movements within the global oil market. Chapter 4 examines the effects of world energy price shocks on China's macroeconomy. We propose a new index of primary commodity energy prices which accurately reflects both the structure of China's energy expenditure shares, as well as intertemporal fluctuations in international energy prices. The index is then in employed a sufficiently rich set of time varying BVARs, identified by a new set of agnostic sign restrictions. Uniformly sized positive energy price shocks are shown to consistently generate economic stagflation over the past two decades. Chapter 5 compares the macroeconomic effects of global oil and iron ore price shocks on the Australian economy. The main results suggest that, over the period 1990Q1 to 2014Q4, the oil shock has a relative larger impact than that of the iron ore shock on output and inflation while the iron ore shock is the dominant source of interest and exchange rate movements. The effects crucially depend on the underlying sources of oil or iron ore price shifts.










Bayesian Econometric Methods


Book Description

Illustrates Bayesian theory and application through a series of exercises in question and answer format.




Bayesian Estimation of DSGE Models


Book Description

Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. Bayesian Estimation of DSGE Models is essential reading for graduate students, academic researchers, and practitioners at policy institutions.




Recent Econometric Techniques for Macroeconomic and Financial Data


Book Description

The book provides a comprehensive overview of the latest econometric methods for studying the dynamics of macroeconomic and financial time series. It examines alternative methodological approaches and concepts, including quantile spectra and co-spectra, and explores topics such as non-linear and non-stationary behavior, stochastic volatility models, and the econometrics of commodity markets and globalization. Furthermore, it demonstrates the application of recent techniques in various fields: in the frequency domain, in the analysis of persistent dynamics, in the estimation of state space models and new classes of volatility models. The book is divided into two parts: The first part applies econometrics to the field of macroeconomics, discussing trend/cycle decomposition, growth analysis, monetary policy and international trade. The second part applies econometrics to a wide range of topics in financial economics, including price dynamics in equity, commodity and foreign exchange markets and portfolio analysis. The book is essential reading for scholars, students, and practitioners in government and financial institutions interested in applying recent econometric time series methods to financial and economic data.




Bayesian Data Analysis, Third Edition


Book Description

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.




Dynamic Linear Models with R


Book Description

State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.