An experiment to evaluate methods for estimating fossil fuel resources

Estimates of petroleum and natural gas resources vary substantially, both over time and across estimation methods. This paper develops a simulation model of global oil resources to evaluate different resource estimation techniques. Protocols for the Hubbert life cycle and USGS geological analogy methods are developed and applied to synthetic data generated by the model. It is shown that the Hubbert method can generate an accurate estimate as early as twenty years before the peak of global production, but the geological analogy approach overestimates the true resource base over the life cycle of the resource. The results show the applicability of simulation and the synthetic data approach to the problem of evaluating forecasting methods.     

A comparison of adaptive structural forecasting methods for electricity sales

This paper presents the results of a study to determine whether new forecasting technologies might be of use to electric utilities for sales forecasting up to 3 years into the future. The methods considered included ordinary least squares on dynamic structural models, autocorrelated error models, adaptive variance and adaptive parameter models. Overall, the more adaptive models performed best, but most of the methods proved vastly superior to simple least squares models which do not take dynamics into account.     

Structural,VAR and BVAR models of exchange rate determination: A comparison of their forecasting performance

This paper compares the out-of-sample forecasting accuracy of a wide class of structural, BVAR and VAR models for major sterling exchange rates over different forecast horizons. As representative structural models we employ a portfolio balance model and a modified uncovered interest parity model, with the latter producing the more accurate forecasts. Proper attention to the long-run properties and the short-run dynamics of structural models can improve on the forecasting performance of the random walk model. The structural model shows substantial improvement in medium-term forecasting accuracy, whereas the BVAR model is the more accurate in the short term. BVAR and VAR models in levels strongly out predict these models formulated in difference form at all forecast horizons.     

Short term load forecasting in electric power systems: A comparison of ARMA models and extended wiener filtering

On-line prediction of electric load in the buses of the EHV grid of a power generation and transmission system is basic information required by on-line procedures for centralized advanced dispatching of power generation. This paper presents two alternative approaches to on-line short term forecasting of the residual component of the load obtained after the removal of the base load from a time series of total load. The first approach involves the use of stochastic ARMA models with time-varying coefficients. The second consists in the use of an extension of Wiener filtering due to Zadeh and Ragazzini. Real data representing a load process measured in an area of Northern Italy and simulated data reproducing a non-stationary process with known characteristics constitute the basis of a numerical comparison allowing one to determine under which conditions each method is more appropriate.     

Linear regression forecasting in the presence of ar(1) disturbances

This paper is concerned with time-series forecasting based on the linear regression model in the presence of AR(1) disturbances. The standard approach is to estimate the AR(1) parameter, ρ, and then construct forecasts assuming the estimated value is the true value. We introduce a new approach which can be viewed as a weighted average of predictions assuming different values of ρ. The weights are proportional to the marginal likelihood of ρ. A Monte Carlo experiment was conducted to compare the new method with five more conventional predictors. Its results suggest that the new approach has a distinct edge over existing procedures.     

A comparison of two models of scientific progress

Does science progress toward some goal or merely away from primitive beginnings? Two agent-based models are built to explain how possibly both kinds of progressive scientific change can result from the interactions of individuals exploring an epistemic landscape. These models are shown to result in qualitatively different predictions about what the resulting system of science should be like.     

Google Trends and the forecasting performance of exchange rate models

In this paper, we use Google Trends data for exchange rate forecasting in the context of a broad literature review that ties the exchange rate movements with macroeconomic fundamentals. The sample covers 11 OECD countries’ exchange rates for the period from January 2004 to June 2014. In out‐of‐sample forecasting of monthly returns on exchange rates, our findings indicate that the Google Trends search query data do a better job than the structural models in predicting the true direction of changes in nominal exchange rates. We also observed that Google Trends‐based forecasts are better at picking up the direction of the changes in the monthly nominal exchange rates after the Great Recession era (2008–2009). Based on the Clark and West inference procedure of equal predictive accuracy testing, we found that the relative performance of Google Trends‐based exchange rate predictions against the null of a random walk model is no worse than the purchasing power parity model. On the other hand, although the monetary model fundamentals could beat the random walk null only in one out of 11 currency pairs, with Google Trends predictors we found evidence of better performance for five currency pairs. We believe that these findings necessitate further research in this area to investigate the extravalue one can get from Google search query data.     

A comparison between linear and nonlinear forecasts for nonlinear AR models

In this paper the relative forecast performance of nonlinear models to linear models is assessed by the conditional probability that the absolute forecast error of the nonlinear forecast is smaller than that of the linear forecast. The comparison probability is explicitly expressed and is shown to be an increasing function of the distance between nonlinear and linear forecasts under certain conditions. This expression of the comparison probability may not only be useful in determining the predictor, which is either a more accurate or a simpler forecast, to be used but also provides a good explanation for an odd phenomenon discussed by Pemberton. The relative forecast performance of a nonlinear model to a linear model is demonstrated to be sensitive to its forecast origins. A new forecast is thus proposed to improve the relative forecast performance of nonlinear models based on forecast origins. © 1997 John Wiley & Sons, Ltd.     

On the usefulness of macroeconomic forecasts as inputs to forecasting models

A forecasting model for y_t based on its relationship to exogenous variables (e.g. x?_t) must use x?_t, the forecast of x?_t. An example is given where commercially available x?_t's are sufficiently inaccurate that a univariate model for y_t appears preferable. For a variety of types of models inclusion of an exogenous variable x?_t is shown to worsen the y_t forecasts whenever x?_t must itself be forecast by x?_t and MSE (x?_t) > Var (x?_t). Tests with forecasts from a variety of sources indicate that, with a few notable exceptions, MSE (x?_t) > Var (x?_t) is common for macroeconomic forecasts more than a quarter or two ahead. Thus, either:

• (a) available medium range forecasts for many macroeconomic variables (e.g. the GNP growth rate) are not an improvement over the sample mean (so that such variables are not useful explanatory variables in forecasting models), and/or
• (b) the suboptimization involved in directly replacing x?_t by x?_t is a luxury that we cannot afford.

     

Inflation forecasting for aggregates of the EU-7 and EU-14 with Bayesian VAR models

This article applies the Bayesian Vector Auto-Regressive (BVAR) model to key economic aggregates of the EU-7, consisting of the former narrow-band ERM members plus Austria, and the EU-14. This model appears to be useful as an additional forecasting tool besides structural macroeconomic models, as is shown both by absolute forecasting performance and by a comparison of ex-post BVAR forecasts with forecasts by the OECD. A comparison of the aggregate models to single-country models reveals that pooling has a strong impact on forecast errors. If forecast errors are interpreted as shocks, shocks appear to be—at least in part—asymmetric, or countries react differently to shocks. © 1998 John Wiley & Sons, Ltd.     

An exploration of some practical issues in the use of quantitative forecasting models

Extrapolative forecasting models have been available for many years and as most organizations have the need to regularly develop forecasts one might anticipate the widespread use of these models. The evidence in Australia indicates that computer based forecasting systems are not being widely used and in fact a number of established systems have been discarded, with the issue of forecast accuracy often being mentioned as a problem area. Two experiments are carried out to examine this issue by comparing judgemental and quantitative forecasts. Other problem areas mentioned as contributing to the abandonment of forecasting systems include the difficulty of manually reviewing the computer forecasts and the effort required to carefully massage the forecast database to remove extraordinary events.     

A threshold model for the French franc/deutschmark exchange rate

The behaviour of the French franc/deutschmark exchange rate is examined in this paper. During the time period studied, these currencies were constrained to lie within prescribed bands relative to one another and the usual random walk explanation of the exchange rate may not be appropriate. The data are examined for evidence of non-linear structure and it is shown that a piecewise linear SETAR model provides a better explanation and superior forecasting performance than a random walk.     

The role of macroeconometric models in forecasting and policy analysis in the united states

This article stresses how little is known about the quality, particularly the relative quality, of macroeconometric models. Most economists make a strict distinction between the quality of a model per se and the accuracy of solutions based on that model. While this distinction is valid, it leaves unanswered how to compare the‘validity’of conditional models. The standard test, the accuracy of ex post simulations, is not definitive when models with differing degrees of exogeneity are compared. In addition, it is extremely difficult to estimate the relative quantitative importance of conceptual problems of models, such as parameter instability across‘policy regimes’ In light of the difficulty in comparisons of conditional macroeconometric models, many model-builders and users assume that the best models are those that have been used to make the most accurate forecasts are those made with the best models. Forecasting experience indicates that forecasters using macroeconometric models have produced more accurate macroeconomic forecasts than either naive or sophisticated unconditional statistical models. It also suggests that judgementally adjusted forecasts have been more accurate than model-based forecasts generated mechanically. The influence of econometrically-based forecasts is now so pervasive that it is difficult to find examples of‘purely judgemental’forecasts.     

Inductive reasoning in the context of discovery: Analogy as an experimental stratagem in the history and philosophy of science

Building on Norton's “material theory of induction,” this paper shows through careful historical analysis that analogy can act as a methodological principle or stratagem, providing experimentalists with a useful framework to assess data and devise novel experiments. Although this particular case study focuses on late eighteenth and early nineteenth-century experiments on the properties and composition of acids, the results of this investigation may be extended and applied to other research programs. A stage in-between what Steinle calls “exploratory experimentation” and robust theory, I argue that analogy encouraged research to substantiate why the likenesses should outweigh the differences (or vice versa) when evaluating results and designing experiments.     

Correspondence on the selection of error measures for comparisons among forecasting methods

Clements and Hendry (1993) proposed the Generalized Forecast Error Second Moment (GFESM) as an improvement to the Mean Square Error in comparing forecasting performance across data series. They based their conclusion on the fact that rankings based on GFESM remain unaltered if the series are linearly transformed. In this paper, we argue that this evaluation ignores other important criteria. Also, their conclusions were illustrated by a simulation study whose relationship to real data was not obvious. Thirdly, prior empirical studies show that the mean square error is an inappropriate measure to serve as a basis for comparison. This undermines the claims made for the GFESM.     

A comparison of methods for the inactivation of third component of guinea-pig complement

Riassunto Sono stati esaminati comparativamente tre reagenti per la titolazione di C₃, rispettivamente allestiti con siero di cavia inattivato con zymosan, formalina e «Liquoid». Le differenze esistenti tra essi sono state analizzate e discusse in base alle attuali conoscenze sulla natura del terzo componente complementare.

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Supported in part by Contract DA-49-007-MD-696 with the Office of the Surgeon General, Department of the Army.     

A comparison of methods for the extraction of amino acids from fungal mycelium

Résumé En comparant plusieurs techniques pour extraire l'amino-acide libre de la mycose fongique on a observé de grandes différences entre elles. L'eau bouillante et la méthyléthylcétone HCl se sont montrés les solvents les plus efficaces et donnant chromatogrammes les plus clairs.     

Seven components of judgmental forecasting skill: Implications for research and the improvement of forecasts

A decomposition of the Brier skill score shows that the performance of judgmental forecasts depends on seven components: environmental predictability, fidelity of the information system, match between environment and forecaster, reliability of information acquisition, reliability of information processing, conditional bias, and unconditional bias. These components provide a framework for research on the forecasting process. Selected literature addressing each component is reviewed, and implications for improving judgmental forecasting are discussed.