A spatial taxonomy of broadband regions in the United States

The steady growth of broadband penetration in the United States is indicative of a major shift in advanced data services and last-mile infrastructure in the deregulated telecommunication environment. Although there are concerns with the equitable provision of broadband services in urban, rural and remote areas, the diffusion process has also created a unique landscape of broadband availability that reflects elements of competition, federal policy, local government initiatives, technological limitations and location. This paper explores the dynamic and diverse spatial landscape of broadband availability in the United States at the zip code level, for 2004. In addition, this study provides a multivariate, spatial taxonomy of broadband regions, highlighting their socioeconomic and demographic differences.     

THE VALUE OF IN‐STREAM WATER TEMPERATURE FORECASTS FOR FISHERIES MANAGEMENT

Water temperature is an important factor affecting aquatic life within the stream environment. Cold water species, such as salmonids, are particularly susceptible to elevated water temperatures. This paper examines the economic value of short‐term water temperature forecasts for salmonid management. Forecasts may have economic value if they allow the water resource manager to make better water allocation decisions. This study considers two applications: water releases for management of Chinook salmon in the Klamath River and leasing water from agriculture for management of steelhead trout in the John Day River. We incorporate biophysical models and water temperature distribution data into a Bayesian framework to simulate changes in fish populations and the corresponding benefit from recreational fishing and opportunity cost of water under different temperature forecast accuracies. Simulation results indicate that use of the forecasts results in increased fish production and that marginal costs decline and net benefits increase as forecast accuracy increases, suggesting that provision and use of such stream temperature forecasts would have value to society. (JEL Q22, Q25, Q28, Q50)     

Futures markets,private storage,and price stabilization

This paper considers several issues pertaining to the role of an efficient futures market for price stabilization. The main aspect which is emphasized is the provision of information by such a market. It provides efficient forecasts which facilitates both production and storage decisions, as a result of which the stability of spot prices is generally increased. The allocation of the benefits from a futures market to the various groups in the economy is discussed andthe present results related to those of the more traditional buffer stock literature. Finally, the degree of stability provided by a futures market is compared with that obtained by active market intervention by a stabilization authority.     

Simulating the spatial distribution of population and emissions to 2100

Urbanization and economic development have important implications for many environmental processes including global climate change. Although there is evidence that urbanization depends endogenously on economic variables, long-term forecasts of the spatial distribution of population are often made exogenously and independent of economic conditions. It is common for research concerning long-run projections of global environmental change to use population density as the primary means to spatially distribute emissions projections. However, researchers typically utilize year 1990 cross-sectional population data to distribute their emissions projections for both the short- and long-term, without projecting any changes in population density. Thus, a beta distribution for individual countries/regions is estimated to describe the geographical distribution of population using a one-degree-by-one-degree latitude–longitude global population data set. Cross-sectional country/regional data are then used to estimate an empirical relationship between parameters of the beta distribution and macroeconomic variables as they vary among countries/regions. This conditional beta distribution allows the simulation of a changing distribution of population, including the growth of urban areas, driven by economic forecasts until the year 2100.     

Stabilization rules and the benefits from price stabilization

This paper develops two models analyzing the distribution of benefits from price stabilization where the stabilization authorities follow some fixed rule, The first is an extension of the static Waugh-Oi-Massell model in which the rule followed by the authorities is also static. This is then generalized to a dynamic model in which both producers and the authorities base their decisions on price expectations. Differential quality of information between producers and the authorities in forming their respective expectations is stressed. The stability properties and profitability of such a scheme are also discussed. The paper concludes by discussing an alternative means of public intervention, through the provision of forecasts to producers.     

Using newspapers for tracking the business cycle: a comparative study for Germany and Switzerland

The use of news-based data for tracking the real economy has gained popularity recently as newspapers archives have become accessible and the need for timely information has soared. In this article, on the basis of keyword searches in newspaper articles we construct several versions of the so-called Recession-word Index (RWI) for Germany and Switzerland and exploit its use for forecasting. Our main findings are the following. First, we show that augmenting benchmark autoregressive models with the RWI leads to improvement in accuracy of one-step-ahead forecasts of GDP growth compared with those obtained by benchmark models. Second, the accuracy of out-of-sample forecasts obtained with models augmented with the RWI is comparable to that of models augmented with established economic indicators, such as the Ifo Business Climate Index and the ZEW Indicator of Economic Sentiment for Germany, and the KOF Economic Barometer and the Purchasing Managers Index in manufacturing for Switzerland. Our results are robust to changes in estimation/forecast samples, the use of rolling versus expanding estimation windows and the inclusion of a web-based recession indicator from Google Trends. As our indices are timely and simple to construct, they could be replicated in countries or regions where no reliable economic indicators exist or their provision is very costly.     

The Spatial Range of Public Goods Revealed Through Referendum Voting

Billions of dollars are now spent annually in the United States and Europe for spatially delineated environmental services such as agricultural landscape management and river restoration programs, yet little is known about the spatial distribution of the benefits from these policies. This paper develops a framework for recovering information on this question from the spatial pattern of votes cast for referenda on the provision of spatially delineated public goods. We specify a model linking voter support for environmental improvement to the distance at which such improvements are expected to occur. The empirical application is to a river restoration referendum in the Swiss canton of Bern. Our results indicate that the benefits from river restoration have a strong local component, sufficiently strong that voter approval would not occur if only canton-wide benefits were at stake. Surprisingly, support for river restoration is no greater, and in some specifications is actually lower, in locations where rivers are a prominent feature in the environment.     

Forecasting demand for a newly introduced product using reservation price data and Bayesian updating

Forecasting demand during the early stages of a product's life cycle is a difficult but essential task for the purposes of marketing and policymaking. This paper introduces a procedure to derive accurate forecasts for newly introduced products for which limited data are available. We begin with the assumption that the consumer reservation price is related to the timing with which the consumer adopts the product. The model is estimated using reservation price data derived through a consumer survey, and the forecast is updated with sales data as they become available using Bayes's rule. The proposed model's forecasting performance is compared with that of benchmark models (i.e., Bass model, logistic growth model, and a Bayesian model based on analogy) using 23 quarters' worth of data on South Korea's broadband Internet services market. The proposed model outperforms all benchmark models in both prelaunch and postlaunch forecasting tests, supporting the thesis that consumer reservation price can be used to forecast demand for a new product before or shortly after product launch.     

Forecasting with limited data: Combining ARIMA and diffusion models

Forecasting diffusion of new technologies is usually performed by the means of aggregate diffusion models, which tend to monopolize this area of research and practice, making the alternative approaches, like the Box-Jenkins, less favourable choices due to their lack of providing accurate long-term predictions. This paper presents a new methodology focusing on the improvement of the short-term prediction that combines the advantages of both approaches and that can be applied in the early stages of a diffusion process. An application of the methodology is also illustrated, providing short-term forecasts for the world broadband and mobile telecommunications' penetration. The results reveal that the methodology is capable of producing improved one-year-ahead predictions, after a certain level of penetration, as compared to the results of both methods individually. This methodology can find applications to all cases of the high-technology market, where a diffusion model is usually used for obtaining future forecasts. The paper concludes with the limitations of the methodology, the discussion on the application's results and the proposals for further research.     

On the combination of exponential smoothing and diffusion forecasts: An application to broadband diffusion in the OECD area

The innovation diffusion literature has established that the spread of a successful innovation over time typically follows a sigmoid curve. Therefore, the forecasting in this area has been monopolized by the use of well known aggregate diffusion models. Time series forecasting has been surprisingly neglected, as it provides mainly accurate short term forecasts. In this work, a method of exponential smoothing, the Holt's damped trend with a modification, is applied in recent broadband diffusion data of two large regions after the reach of the inflection point. As validated with holdback sample data ranging from 6 up to 30 months, the key for successful forecasting is the use of the estimated saturation level calculated from a diffusion model, in order to specify the appropriate trend. The results indicate improved predictions compared to two popular diffusion models, the Gompertz and the Linear Logistic model. The paper concludes with the application of the proposed method in a 48-month forecasting horizon, as well as the suggestions for further research.     

Do the OECD 24 month horizon growth forecasts for the G7–countries contain information?

The OECD produces two–year–ahead growth forecasts for the G7–countries since 1987; these forecasts have never been evaluated. A regression is developed that tests for the information content of the forecasts. The idea is that this content is the added value forecasters incorporate in their forecasts. The information content is defined relative to the forecast for the previous year. In the end, the added value contained in the current year forecast is calculated relative to the last observation. The test consists in checking whether the information content reduces the forecasts error.

The study begins with a calculation of the usual accuracy statistics. These indicate an extreme low quality for the forecasts. The regression tests support this conclusion although the forecasts for Japan do possess some information. Alarming for users of forecasts is that there are no obvious alternatives.     

FORECASTING AUSTRALIAN MACROECONOMIC VARIABLES USING A LARGE DATASET

This paper investigates the forecasting performance of the diffusion index approach for the Australian economy, and considers the forecasting performance of the diffusion index approach relative to composite forecasts. Weighted and unweighted factor forecasts are benchmarked against composite forecasts, and forecasts derived from individual forecasting models. The results suggest that diffusion index forecasts tend to improve on the benchmark AR forecasts. We also observe that weighted factors tend to produce better forecasts than their unweighted counterparts. We find, however, that the size of the forecasting improvement is less marked than previous research, with the diffusion index forecasts typically producing mean square errors of a similar magnitude to the VAR and BVAR approaches.     

Evaluating strange forecasts: The curious case of football match scorelines

This study analyses point forecasts of exact scoreline outcomes for football matches in the English Premier League. These forecasts were made for distinct competitions and originally judged differently. We compare these with implied probability forecasts using bookmaker odds and a crowd of tipsters, as well as point and probability forecasts generated from a statistical model. From evaluating these sources and types of forecast, using various methods, we argue that regression encompassing is the most appropriate way to compare point and probability forecasts, and find that both these types of forecasts for football match scorelines generally add information to one another.     

The Accuracy of OECD Forecasts for Japan

A variety of accuracy measures, error diagnostics and rationality tests are applied to the OECD's macroeconomic forecasts for Japan of aggregate demand and output, inflation and the balance of payments. It is found that the OECD forecasts are superior to naive no-change predictions and forecasts generated by simple autoregressive time-series models. Most forecasting error is nonsystematic. As predictors of direction the OECD's six-month ahead forecasts should be considered valuable; this cannot be said for forecasts which look ahead a year and 18 months. Many forecasts fail bias, efficiency and consistency tests so that the rational expectations hypothesis is not generally supported.     

Geographically explicit global modeling of land-use change, carbon sequestration, and biomass supply

This study aims to determine whether carbon sequestration policies could present a significant contribution to the global portfolio of climate change mitigation options. The objective is to model the effects of policies designed to induce landowners to change land use and management patterns with a view to sequester carbon or to reduce deforestation. The approach uses the spatially explicit Dynamic Integrated Model of Forestry and Alternative Land Use (DIMA) to quantify the economic potential of global forests. The model chooses which of the land-use processes (afforestation, reforestation, deforestation, or conservation and management options) would be applied in a specific location, based on land prices, cost of forest production and harvesting, site productivity, population density, and estimates of economic growth. The approach is relevant in that it (1) couples a revised and updated version of the Special Report on Emissions Scenarios with the dynamic development of climate policy implications through integration with the Model for Energy Supply Strategy Alternatives and their General Environmental Impact (MESSAGE); (2) is spatially explicit on a 0.5° grid; and (3) is constrained by guaranteeing food security and land for urban development. As outputs, DIMA produces 100-year forecasts of land-use change, carbon sequestration, impacts of carbon incentives (e.g., avoided deforestation), biomass for bioenergy, and climate policy impacts. The modeling results indicate that carbon sequestration policies could contribute to a significant part of the global portfolio of efficient climate mitigation policies, dependent upon carbon prices.     

Forecast performance in times of terrorism

Governments, central banks, and private companies make extensive use of expert and market-based forecasts in their decision-making processes. These forecasts can be affected by terrorism, a factor that should be considered by decision-makers. We focus on terrorism as a mostly endogenously driven form of political uncertainty and assess the forecasting performance of market-based and professional inflation and exchange rate forecasts in Israel. We show that expert forecasts are better than market-based forecasts, particularly during periods of terrorism. However, the performance of both market-based and expert forecasts is significantly worse during such periods. Thus, policymakers should be particularly attentive to terrorism when considering inflation and exchange rate forecasts.     

Is there a Core of Macroeconomics that Euro Area Forecasters Believe In?

The quantity theory of money, Okun's law, and the Phillips curve are cornerstones of macroeconomic theory. But are they also of practical relevance? Using survey data for the euro area, we found that professional economists’ forecasts are consistent with a version of the quantity theory in which forecasts of the growth rate of money supply correlate in a proportional way with forecasts of the inflation rate. We also found that forecasts of changes in the unemployment rate and forecasts of the growth rate of real output are consistent with Okun's law. Evidence of a systematic link between forecasts of the inflation rate and forecasts of the unemployment rate, however, is not strong.     

Explanations of the inconsistencies in survey respondents’ forecasts

A comparison of the point forecasts and the probability distributions of inflation and output growth made by individual respondents to the US Survey of Professional Forecasters indicates that the two sets of forecasts are sometimes inconsistent. We evaluate a number of possible explanations, and find that not all forecasters update their histogram forecasts as new information arrives. This is supported by the finding that the point forecasts are more accurate than the histograms in terms of first-moment prediction.     

How (Over) Confident Are Financial Analysts?

Extensive research has been devoted to the quality of analysts' earnings forecasts. The common finding is that analysts' forecasts are not very accurate. Prior studies have tended to focus on the mean of forecasts and measure accuracy using various summaries of forecast errors. The present study sheds new light on the accuracy of analysts' forecasts, by measuring how well calibrated these forecasts are. The authors follow the tradition of calibration studies in psychological literature and measure the degree of calibration by the hit rate. They analyze a year's worth of data from the Institutional Brokers Estimate System database, which includes over 200,000 annual earnings forecasts made by over 6,000 analysts for over 5,000 companies. By using different ways to convert analysts' point estimates of earnings into a range of values, the authors establish the bounds that are necessary to determine the hit rates, and examine to what extent the actual earnings announced by the companies are bracketed by these intervals. These hit rates provide a more complete picture of the accuracy of the forecasts.