Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

Models and Methods in Social Network Analysis presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s. Intended as a complement to Wasserman and Faust’s Social Network Analysis: Methods and Applications, it is a collection of original articles by leading methodologists reviewing recent advances in their particular areas of network methods. Reviewed are advances in network measurement, network sampling, the analysis of centrality, positional analysis or blockmodeling, the analysis of diffusion through networks, the analysis of affiliation or “two-mode” networks, the theory of random graphs, dependence graphs, exponential families of random graphs, the analysis of longitudinal network data, graphic techniques for exploring network data, and software for the analysis of social networks.

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Models and methods in social network analysis

Foundations of Economic Analysis

Dynamic optimization: The calculus of variations and optimal control in economics and management: Morton I. KAMIEN and Nancy L. SCHWARTZ Volume 4 in: Dynamic Economics: Theory and Applications, North-Holland, New York, 1981, xi + 331 pages, Dfl.90.00

We address some of the key questions that arise in forecasting the price of crude oil. What do applied forecasters need to know about the choice of sample period and about the tradeoffs between alternative oil price series and model specifications? Are real and nominal oil prices predictable based on macroeconomic aggregates? Does this predictability translate into gains in out-of-sample forecast accuracy compared with conventional no-change forecasts? How useful are oil futures prices in forecasting the spot price of oil? How useful are survey forecasts? How does one evaluate the sensitivity of a baseline oil price forecast to alternative assumptions about future oil demand and oil supply conditions? How does one quantify risks associated with oil price forecasts? Can joint forecasts of the price of oil and of U.S. real GDP growth be improved upon by allowing for asymmetries?

/pdf/forecasting-the-price-of-oil-4z7be0vnej.pdf

Forecasting the Price of Oil

Macroeconomic factors and oil futures prices: A data-rich model

We investigate how the relation between gold prices and the U.S. Dollar has been affected by the recent turmoil in financial markets. We use spot prices of gold and spot bilateral exchange rates against the Euro and the British Pound to study the pattern of volatility spillovers. We estimate the bivariate structural GARCH models proposed by Spargoli e Zagaglia (2008) to gauge the causal relations between volatility changes in the two assets. We also apply the tests for change of co-dependence of Cappiello, Gerard and Manganelli (2005). We document the ability of gold to generate stable comovements with the Dollar exchange rate that have survived the recent phases of market disruption. Our findings also show that exogenous increases in market uncertainty have tended to produce reactions of gold prices that are more stable than those of the U.S. Dollar.

Gold and the U.S. Dollar: Tales from the Turmoil

I study the dynamics of oil futures prices in the NYMEX using a large panel dataset that includes global macroeconomic indicators, financial market indices, quantities and prices of energy products. I extract common factors from these series and estimate a Factor-Augmented Vector Autoregression for the maturity structure of oil futures prices. I find that latent factors generate information that, once combined with that of the yields, improves the forecasting performance for oil prices. Furthermore, I show that a factor correlated to purely financial developments contributes to the model performance, in addition to factors related to energy quantities and prices.

Macroeconomic Factors and Oil Futures Prices: A Data-Rich Model

We investigate how the relation between gold prices and the U.S. Dollar has been aected by the recent turmoil in financial markets. We use spot prices of gold and spot bilateral exchange rates against the Euro and the British Pound to study the pattern of volatility spillovers. We estimate the bivariate structural GARCH models proposed by Spargoli e Zagaglia (2008) to gauge the causal relations between volatility changes in the two assets. We also apply the tests for change of co-dependence of Cappiello, Gerard and Manganelli (2005). We document the ability of gold to generate stable comovements with the Dollar exchange rate that have survived the recent phases of market disruption. Our findings also show that exogenous increases in market uncertainty have tended to produce reactions of gold prices that are more stable than those of the U.S. Dollar.

/pdf/gold-and-the-u-s-dollar-tales-from-the-turmoil-vl7mpis5w9.pdf

Gold and the U.S. dollar: tales from the turmoil

Asset liquidity in modern financial markets is a key but elusive concept. A market is often said to be liquid when the prevailing structure of transactions provides a prompt and secure link between the demand and supply of assets, thus delivering low costs of transaction. Providing a rigorous and empirically relevant definition of market liquidity has, however, provided to be a difficult task. This paper provides a critical review of the frameworks currently available for modelling and estimating the market liquidity of assets. We consider definitions that stress the role of the bid-ask spread and the estimation of its components that arise from alternative sources of market friction. In this case, intra-daily measures of liquidity appear relevant for capturing the core features of a market, and for their ability to describe the arrival of new information to market participants.

Paolo Zagaglia

Papers

Macroeconomic factors and oil futures prices: A data-rich model

Gold and the U.S. Dollar: Tales from the Turmoil

Macroeconomic Factors and Oil Futures Prices: A Data-Rich Model

Gold and the U.S. dollar: tales from the turmoil

Measuring market liquidity: An introductory survey