Forecasting and Analyzing Economic Activity with Coincident and Leading Indexes: The Case of Connecticut

We develop coincident and leading employment indexes for the Connecticut economy. Four employment-related variables enter the coincident index while five employment-related variables enter the leading index. The peaks and troughs in the leading index lead the peaks and troughs in the coincident index by an average of 3 and 9 months. Finally, we use the leading index in vector-autoregressive (VAR) and Bayesian vector-autoregressive (BVAR) models to forecast the coincident index, nonfarm employment, and the unemployment rate.

A comparison of techniques for forecasting utilization of Veterans Affairs hospitals

This study demonstrated that accurate, short-term forecasts of Veterans Affairs (VA) hospital utilization can be made using the Patient Treatment File (PTF), the inpatient discharge database of the VA. Accurate, short-term forecasts of two years or less can reduce required inventory levels, improve allocation of resources, and are essential for better financial management. These are all necessary achievements in an era of cost-containment.^ Six years of non-psychiatric discharge records were extracted from the PTF and used to calculate four indicators of VA hospital utilization: average length of stay, discharge rate, multi-stay rate (a measure of readmissions) and days of care provided. National and regional levels of these indicators were described and compared for fiscal year 1984 (FY84) to FY89 inclusive.^ Using the observed levels of utilization for the 48 months between FY84 and FY87, five techniques were used to forecast monthly levels of utilization for FY88 and FY89. Forecasts were compared to the observed levels of utilization for these years. Monthly forecasts were also produced for FY90 and FY91.^ Forecasts for days of care provided were not produced. Current inpatients with very long lengths of stay contribute a substantial amount of this indicator and it cannot be accurately calculated.^ During the six year period between FY84 and FY89, average length of stay declined substantially, nationally and regionally. The discharge rate was relatively stable, while the multi-stay rate increased slightly during this period. FY90 and FY91 forecasts show a continued decline in the average length of stay, while the discharge rate is forecast to decline slightly and the multi-stay rate is forecast to increase very slightly.^ Over a 24 month ahead period, all three indicators were forecast within a 10 percent average monthly error. The 12-month ahead forecast errors were slightly lower. Average length of stay was less easily forecast, while the multi-stay rate was the easiest indicator to forecast.^ No single technique performed significantly better as determined by the Mean Absolute Percent Error, a standard measure of error. However, Autoregressive Integrated Moving Average (ARIMA) models performed well overall and are recommended for short-term forecasting of VA hospital utilization. ^

RUNOFF FROM COAL PILES: EFFECTS ON THE ENVIRONMENT AND THE PUBLIC'S HEALTH

The use of coal for fuel in place of oil and natural gas has been increasing in the United States. Typically, users store their reserves of coal outdoors in large piles and rainfall on the coal creates runoffs which may contain materials hazardous to the environment and the public's health. To study this hazard, rainfall on model coal piles was simulated, using deionized water and four coals of varying sulfur content. The simulated surface runoffs were collected during 9 rainfall simulations spaced 15 days apart. The runoffs were analyzed for 13 standard water quality parameters, extracted with organic solvents and then analyzed with capillary column GC/MS, and the extracts were tested for mutagenicity with the Ames Salmonella microsomal assay and for clastogenicity with Chinese hamster ovary cells.^ The runoffs from the high-sulfur coals and the lignite exhibited extremes of pH (acidity), specific conductance, chemical oxygen demand, and total suspended solids; the low-sulfur coal runoffs did not exhibit these extremes. Without treatment, effluents from these high-sulfur coals and lignite would not comply with federal water quality guidelines.^ Most extracts of the simulated surface runoffs contained at least 10 organic compounds including polycyclic aromatic hydrocarbons, their methyl and ethyl homologs, olefins, paraffins, and some terpenes. The concentrations of these compounds were generally less than 50 (mu)g/l in most extracts.^ Some of the extracts were weakly mutagenic and affected both a DNA-repair proficient and deficient Salmonella strain. The addition of S9 decreased the effect significantly. Extracts of runoffs from the low-sulfur coal were not mutagenic.^ All extracts were clastogenic. Extracts of runoffs from the high-sulfur coals were both clastogenic and cytotoxic; those from the low-sulfur coal and the lignite were less clastogenic and not cytotoxic. Clastogenicity occurred with and without S9 activation. Chromosomal lesions included gaps, breaks and exchanges. These data suggest a relationship between the sulfur content of a coal, its mutagenicity and also its clastogenicity.^ The runoffs from actual coal piles should be investigated for possible genotoxic effects in view of the data presented in this study.^

Predicting the Past and Forecasting the Future

A commentary on Santos' article, "Explaining Scholarship Addressing Hispanic Children’s Issues."

(Table 1) Basin area, average late-summer speedup, number of sinks and runoff of West Greenland Ice Sheet catchments

We use interferometric synthetic aperture radar observations recorded in a land-terminating sector of western Greenland to characterise the ice sheet surface hydrology and to quantify spatial variations in the seasonality of ice sheet flow. Our data reveal a non-uniform pattern of late-summer ice speedup that, in places, extends over 100 km inland. We show that the degree of late-summer speedup is positively correlated with modelled runoff within the 10 glacier catchments of our survey, and that the pattern of late-summer speedup follows that of water routed at the ice sheet surface. In late-summer, ice within the largest catchment flows on average 48% faster than during winter, whereas changes in smaller catchments are less pronounced. Our observations show that the routing of seasonal runoff at the ice sheet surface plays an important role in shaping the magnitude and extent of seasonal ice sheet speedup.

Freshwater runoff for Taylor Slough to Florida Bay for 1965 to 2000 for the FATHOM model

The Florida Bay ecosystem supports a number of economically important ecosystem services, including several recreational fisheries, which may be affected by changing salinity and temperature due to climate change. In this paper, we use a combination of physical models and habitat suitability index models to quantify the effects of potential climate change scenarios on a variety of juvenile fish and lobster species in Florida Bay. The climate scenarios include alterations in sea level, evaporation and precipitation rates, coastal runoff, and water temperature. We find that the changes in habitat suitability vary in both magnitude and direction across the scenarios and species, but are on average small. Only one of the seven species we investigate (Lagodon rhomboides, i.e., pinfish) sees a sizable decrease in optimal habitat under any of the scenarios. This suggests that the estuarine fauna of Florida Bay may not be as vulnerable to climate change as other components of the ecosystem, such as those in the marine/terrestrial ecotone. However, these models are relatively simplistic, looking only at single species effects of physical drivers without considering the many interspecific interactions that may play a key role in the adjustment of the ecosystem as a whole. More complex models that capture the mechanistic links between physics and biology, as well as the complex dynamics of the estuarine food web, may be necessary to further understand the potential effects of climate change on the Florida Bay ecosystem.

Forecasting inbound tourists in Cambodia

Forecasting tourism demand is crucial for management decisions in the tourism sector. Estimating a vector autoregressive (VAR) model for monthly visitor arrivals disaggregated by three entry points in Cambodia for the years 2006–2015, I forecast the number of arrivals for years 2016 and 2017. The results show that the VAR model fits well with the data on visitor arrivals for each entry point. Ex post forecasting shows that the forecasts closely match the observed data for visitor arrivals, thereby supporting the forecasting accuracy of the VAR model. Visitor arrivals to Siem Reap and Phnom Penh airports are forecast to increase steadily in future periods, with varying fluctuations across months and origin countries of foreign tourists.

Improving runoff estimates from regional climate models: a performance analysis in Spain

An important step to assess water availability is to have monthly time series representative of the current situation. In this context, a simple methodology is presented for application in large-scale studies in regions where a properly calibrated hydrologic model is not available, using the output variables simulated by regional climate models (RCMs) of the European project PRUDENCE under current climate conditions (period 1961–1990). The methodology compares different interpolation methods and alternatives to generate annual times series that minimise the bias with respect to observed values. The objective is to identify the best alternative to obtain bias-corrected, monthly runoff time series from the output of RCM simulations. This study uses information from 338 basins in Spain that cover the entire mainland territory and whose observed values of natural runoff have been estimated by the distributed hydrological model SIMPA. Four interpolation methods for downscaling runoff to the basin scale from 10 RCMs are compared with emphasis on the ability of each method to reproduce the observed behaviour of this variable. The alternatives consider the use of the direct runoff of the RCMs and the mean annual runoff calculated using five functional forms of the aridity index, defined as the ratio between potential evapotranspiration and precipitation. In addition, the comparison with respect to the global runoff reference of the UNH/GRDC dataset is evaluated, as a contrast of the “best estimator” of current runoff on a large scale. Results show that the bias is minimised using the direct original interpolation method and the best alternative for bias correction of the monthly direct runoff time series of RCMs is the UNH/GRDC dataset, although the formula proposed by Schreiber (1904) also gives good results

Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting

In this work, we propose the Seasonal Dynamic Factor Analysis (SeaDFA), an extension of Nonstationary Dynamic Factor Analysis, through which one can deal with dimensionality reduction in vectors of time series in such a way that both common and specific components are extracted. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal ones, by means of the common factors following a multiplicative seasonal VARIMA(p, d, q) × (P, D, Q)s model. Additionally, a bootstrap procedure that does not need a backward representation of the model is proposed to be able to make inference for all the parameters in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing enhanced coverage of forecasting intervals. A challenging application is provided. The new proposed model and a bootstrap scheme are applied to an innovative subject in electricity markets: the computation of long-term point forecasts and prediction intervals of electricity prices. Several appendices with technical details, an illustrative example, and an additional table are available online as Supplementary Materials.

Forecasting Electricity Prices and their volatilities using Unobserved Components.

The liberalization of electricity markets more than ten years ago in the vast majority of developed countries has introduced the need of modelling and forecasting electricity prices and volatilities, both in the short and long term. Thus, there is a need of providing methodology that is able to deal with the most important features of electricity price series, which are well known for presenting not only structure in conditional mean but also time-varying conditional variances. In this work we propose a new model, which allows to extract conditionally heteroskedastic common factors from the vector of electricity prices. These common factors are jointly estimated as well as their relationship with the original vector of series, and the dynamics affecting both their conditional mean and variance. The estimation of the model is carried out under the state-space formulation. The new model proposed is applied to extract seasonal common dynamic factors as well as common volatility factors for electricity prices and the estimation results are used to forecast electricity prices and their volatilities in the Spanish zone of the Iberian Market. Several simplified/alternative models are also considered as benchmarks to illustrate that the proposed approach is superior to all of them in terms of explanatory and predictive power.

Comparison of methods for downscaling runoff from regional climate models in Spanish basins

At present there is much literature that refers to the advantages and disadvantages of different methods of statistical and dynamical downscaling of climate variables projected by climate models. Less attention has been paid to other indirect variables, like runoff, which play a significant role in evaluating the impact of climate change on hydrological systems. Runoff presents a much greater bias in climate models than other climate variables, like temperature or precipitation. It is very important to identify the methods that minimize bias while downscaling runoff from the gridded results of climate models to the basin scale