Production and Use of Fuel Ethanol from Corn or Wheat

This archival publication may not reflect current scientific knowledge or recommendations. Current information available from the University of Minnesota Extension: https://www.extension.umn.edu

Evidence on a Real Business Cycle Model with Neutral and Investment-Specific Technology Shocks Using Bayesian Model Averaging

The empirical support for a real business cycle model with two technology shocks is evaluated using a Bayesian model averaging procedure. This procedure makes use of a finite mixture of many models within the class ofvector autoregressive (VAR) processes. The linear VAR model is extendedto permit cointegration, a range of deterministic processes, equilibrium restrictions and restrictions on long-run responses to technology shocks. Wefind support for a number of the features implied by the real business cyclemodel. For example, restricting long run responses to identify technologyshocks has reasonable support and important implications for the short runresponses to these shocks. Further, there is evidence that savings and investment ratios form stable relationships, but technology shocks do not accountfor all stochastic trends in our system. There is uncertainty as to the mostappropriate model for our data, with thirteen models receiving similar support, and the model or model set used has signficant implications for theresults obtained

Treatment of rising damp in historical buildings: wall base ventilation

Intervention in older buildings increasingly requires extensive and objective knowledge of what one will be working with. The multifaceted aspect of work carried out on buildings tends to encompass a growing number of specialities, with marked emphasis on learning the causes of many of the problems that affect these buildings and the possible treatments that can solve them. Moisture transfer in walls of old buildings, which are in direct contact with the ground, leads to a migration of soluble salts responsible for many building pathologies.http://www.sciencedirect.com/science/article/B6V23-4H7T0H7-1/1/f5e8a4ec173c5dadf120770678facf4

Optimal dynamic portfolio selection with earnings-at-risk

In this paper we investigate a continuous-time portfolio selection problem. Instead of using the classical variance as usual, we use earnings-at-risk (EaR) of terminal wealth as a measure of risk. In the settings of Black-Scholes type financial markets and constantly-rebalanced portfolio (CRP) investment strategies, we obtain closed-form expressions for the best CRP investment strategy and the efficient frontier of the mean-EaR problem, and compare our mean-EaR analysis to the classical mean-variance analysis and to the mean-CaR (capital-at-risk) analysis. We also examine some economic implications arising from using the mean-EaR model. © 2007 Springer Science+Business Media, LLC.postprin

Financial diversification strategies before World War I: Buy-and-hold versus naïve portfolio selection

This study contributes to a growing volume of scholarship that highlights the importance of financial diversification in business history. It shows that, pre-WWI, financial advice for equal portfolio weighting, the so-called naïve diversification, then called scientific investment or geographical distribution of risk, was a sophisticated strategy for Victorian investors and not suboptimal to Markowitz optimization. Drawing upon a unique dataset of 507 individual portfolios at death, this study shows that, although Victorian investors, in particular wealthy investors, did diversify investment risk across a number of securities, they did not hold equally weighted portfolios. It explores possible reasons for the unbalanced nature of investor portfolios and dismisses socio economic factors, illiquidity, passive ‘buy the market’ and market timing strategies as possible explanatory factors. The results rather point to a strategy of naïve diversification spread over time, a ‘buy as you go and hold strategy’, buying new securities as savings allowed and holding them until death

The Dynamic Striated Metropolis-Hastings Sampler for High-Dimensional Models

Having efficient and accurate samplers for simulating the posterior distribution is crucial for Bayesian analysis. We develop a generic posterior simulator called the "dynamic striated Metropolis-Hastings (DSMH)" sampler. Grounded in the Metropolis-Hastings algorithm, it draws its strengths from both the equienergy sampler and the sequential Monte Carlo sampler by avoiding the weaknesses of the straight Metropolis-Hastings algorithm as well as those of importance sampling. In particular, the DSMH sampler possesses the capacity to cope with incredibly irregular distributions that are full of winding ridges and multiple peaks and has the flexibility to take full advantage of parallelism on either desktop computers or clusters. The high-dimensional application studied in this paper provides a natural platform to put to the test generic samplers such as the DSMH sampler