Seasonal prediction of lake inflows and rainfall in a hydro-electricity catchment, Waitaki river, New Zealand

The Waitaki River is located in the centre of the South Island of New Zealand, and hydro-electricity generated on the river accounts for 35-40% of New Zealand's electricity. Low inflows in 1992 and 2001 resulted in the threat of power blackouts. Improved seasonal rainfall and inflow forecasts will result in the better management of the water used in hydro-generation on a seasonal basis. Researchers have stated that two key directions in the fields of seasonal rainfall and streamflow forecasting are to a) decrease the spatial scale of forecast products, and b) tailor forecast products to end-user needs, so as to provide more relevant and targeted forecasts. Several season-ahead lake inflow and rainfall forecast models were calibrated for the Waitaki river catchment using statistical techniques to quantify relationships between land-ocean-atmosphere state variables and seasonally lagged inflows and rainfall. Techniques included principal components analysis and multiple linear regression, with cross-validation techniques applied to estimate model error and randomization techniques used to establish the significance of the skill of the models. Many of the models calibrated predict rainfall and inflows better than random chance and better than the long-term mean as a predictor. When compared to the range of all probable inflow seasonal totals (based on the 80-year recorded history in the catchment), 95% confidence limits around most model predictions offer significant skill. These models explain up to 19% of the variance in season-ahead rainfall and inflows in this catchment. Seasonal rainfall and inflow forecasting on a single catchment scale and focussed to end-user needs is possible with some skill in the South Island of New Zealand

Extrapolation for Time-Series and Cross-Sectional Data

Extrapolation methods are reliable, objective, inexpensive, quick, and easily automated. As a result, they are widely used, especially for inventory and production forecasts, for operational planning for up to two years ahead, and for long-term forecasts in some situations, such as population forecasting. This paper provides principles for selecting and preparing data, making seasonal adjustments, extrapolating, assessing uncertainty, and identifying when to use extrapolation. The principles are based on received wisdom (i.e., experts’ commonly held opinions) and on empirical studies. Some of the more important principles are:• In selecting and preparing data, use all relevant data and adjust the data for important events that occurred in the past.• Make seasonal adjustments only when seasonal effects are expected and only if there is good evidence by which to measure them.• In extrapolating, use simple functional forms. Weight the most recent data heavily if there are small measurement errors, stable series, and short forecast horizons. Domain knowledge and forecasting expertise can help to select effective extrapolation procedures. When there is uncertainty, be conservative in forecasting trends. Update extrapolation models as new data are received.• To assess uncertainty, make empirical estimates to establish prediction intervals.• Use pure extrapolation when many forecasts are required, little is known about the situation, the situation is stable, and expert forecasts might be biased

Weather, climate, and hydrologic forecasting for the US Southwest: A survey

As part of a regional integrated assessment of climate vulnerability, a survey was conducted from June 1998 to May 2000 of weather, climate, and hydrologic forecasts with coverage of the US Southwest and an emphasis on the Colorado River Basin. The survey addresses the types of forecasts that were issued, the organizations that provided them, and techniques used in their generation. It reflects discussions with key personnel from organizations involved in producing or issuing forecasts, providing data for making forecasts, or serving as a link for communicating forecasts. During the survey period, users faced a complex and constantly changing mix of forecast products available from a variety of sources. The abundance of forecasts was not matched in the provision of corresponding interpretive materials, documentation about how the forecasts were generated, or reviews of past performance. Potential existed for confusing experimental and research products with others that had undergone a thorough review process, including official products issued by the National Weather Service. Contrasts between the state of meteorologic and hydrologic forecasting were notable, especially in the former's greater operational flexibility and more rapid incorporation of new observations and research products. Greater attention should be given to forecast content and communication, including visualization, expression of probabilistic forecasts and presentation of ancillary information. Regional climate models and use of climate forecasts in water supply forecasting offer rapid improvements in predictive capabilities for the Southwest. Forecasts and production details should be archived, and publicly available forecasts should be accompanied by performance evaluations that are relevant to users

MODELING U.S. BROILER SUPPLY RESPONSE: A STRUCTURAL TIME SERIES APPROACH

A structural time series model is used to estimate the supply response function for broiler production in the United States using quarterly data and a structural time series model. This model has the advantage of expressing trend and seasonal elements as stochastic components, allowing a dynamic interpretation of the results and improving the forecast capabilities of the model. The results of the estimation indicate the continued importance of feed cost to poultry production and of technology as expressed by the stochastic trend variable. However, seasonal influences appear to have become less important, since the seasonal component was not statistically significant.Livestock Production/Industries,

Long-run marketing inferences from scanner data.

Good marketing decisions require managers' understanding of the nature of the market-response function relating performance measures such as sales and market share to variations in the marketing mix (product, price, distribution and communications efforts). Our paper focuses on the dynamic aspect of market-response functions, i.e. how current marketing actions affect current and future market response. While conventional econometrics has been the dominant methodology in empirical market-response analyses, time-series analysis offers unique opportunities for pushing the frontier in dynamic research. This paper examines the contributions an d the future outlook of time-series analysis in market-response modeling. We conclude first, that time series analysis has made a relatively limited overall contribution to the discipline, and investigate reasons why that has been the case. However, major advances in data (transactions-based databases and in modeling technology (long-term time-series modeling) create new opportunities for time-series techniques in marketing, in particular for the study of long-run marketing effectiveness. We discuss four major aspects of long -term time-series modeling, relate them to substantive marketing problems, and describe some early applications. Combining the new data with the new methods, we then present original empirical results on the long-term behavior of brand sales and category sales for four consumer products. We discuss the implications of our findings for future research in market response. Our observations lead us to identify three areas where additional research could enhance the diffusion of the identified time-series concepts in marketing.Data; Marketing;

Inflation and Inflation Uncertainty in the Euro Area

This paper estimates a time-varying AR-GARCH model of inflation producing measures of inflation uncertainty for the euro area, and investigates the linkages between them in a VAR framework, also allowing for the possible impact of the policy regime change associated with the start of EMU in 1999. The main findings are as follows. Steady-state inflation and inflation uncertainty have declined steadily since the inception of EMU, whilst short-run uncertainty has increased, mainly owing to exogenous shocks. A sequential dummy procedure provides further evidence of a structural break coinciding with the introduction of the euro and resulting in lower long-run uncertainty. It also appears that the direction of causality has been reversed, and that in the euro period the Friedman-Ball link is empirically supported, implying that the ECB can achieve lower inflation uncertainty by lowering the inflation rate.Inflation, inflation uncertainty, time-varying parameters, GARCH models, ECB, EMU