Electronic Transactions as High-Frequency Indicators of Economic Activity

Since the advent of standard national accounts data over 60 years ago, economists have traditionally relied on monthly or quarterly data supplied by central statistical agencies for macroeconomic modelling and forecasting. However, technological advances of the past several years have resulted in new high-frequency data sources that could potentially provide more accurate and timely information on the current level of economic activity. In this paper we explore the usefulness of electronic transactions as real-time indicators of economic activity, using Canadian debit card data as an example. These data have the advantages of daily availability and the high market penetration of debit cards. We find that (i) household transactions vary greatly according to the day of the week, peaking every Friday and falling every Sunday; (ii) debit card data can help lower consensus forecast errors for GDP and consumption (especially non-durable) growth; (iii) debit card transactions are correlated with Statistics Canada’s revisions to GDP; (iv) high-frequency analyses of transactions around extreme events are possible, and in particular we are able to analyze expenditure patterns around the September 11 terrorist attacks and the August 2003 electrical blackout.Business fluctuations and cycles

FORECAST CONTENT AND CONTENT HORIZONS FOR SOME IMPORTANT MACROECONOMIC TIME SERIES

For quantities that are approximately stationary, the information content of statistical forecasts tends to decline as the forecast horizon increases, and there exists a maximum horizon beyond which forecasts cannot provide discernibly more information about the variable than is present in the unconditional mean (the content horizon). The pattern of decay of forecast content (or skill) with increasing horizon is well known for many types of meteorological forecasts; by contrast, little generally-accepted information about these patterns or content horizons is available for economic variables. In this paper we attempt to develop more information of this type by estimating content horizons for variety of macroeconomic quantities; more generally, we characterize the pattern of decay of forecast content as we project farther into the future. We find wide variety of results for the different macroeconomic quantities, with models for some quantities providing useful content several years into the future, for other quantities providing negligible content beyond one or two months or quarters.

How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables

For stationary transformations of variables, there exists a maximum horizon beyond which forecasts can provide no more information about the variable than is present in the unconditional mean. Meteorological forecasts, typically excepting only experimental or exploratory situations, are not reported beyond this horizon; by contrast, little generally accepted information about such maximum horizons is available for economic variables. The authors estimate such content horizons for a variety of economic variables, and compare these with the maximum horizons that they observe reported in a large sample of empirical economic forecasting studies. The authors find that many published studies provide forecasts exceeding, often by substantial margins, their estimates of the content horizon for the particular variable and frequency. The authors suggest some simple reporting practices for forecasts that could potentially bring greater transparency to the process of making and interpreting economic forecasts.Econometric and statistical methods, Business fluctuations and cycles

Human Papillomavirus Awareness, Vaccine Status, and Risk Factors in Female Emergency Patients

Introduction: A vaccine targeting high-risk human papillomavirus (HPV) strains can effectively prevent HPV-associated cervical cancer risk. However, many girls and women do not receive the vaccine, more often those impacted by health disparities associated with race and/or socioeconomic status. This same disparate population has also been shown to be at higher risk for cervical cancer. Many of these women also rely on the emergency department (ED) as a safety net for their healthcare. This study sought to gather information pertaining to HPV and cervical cancer risk factors, awareness of HPV and the vaccine, as well as HPV vaccine uptake in female patients presenting to an ED.Methods: We obtained 81 surveys completed by female ED patients. Demographics included age, race, income, insurance status, primary care provider status, and known cervical-cancer risk factors. Subsequent survey questions explored respondents’ knowledge, familiarity, and attitudes regarding HPV, cervical cancer, and the HPV vaccine, including vaccination uptake rates. We analyzed data using descriptive statistics and Fisher’s exact test.Results: Approximately one in seven respondents (14.8%) had never previously heard of HPV and 32.1% were unaware of the existence of a HPV vaccine. Minority patients, including those who were Black and Hispanic patients, low income patients, and uninsured and publicly insured patients were less likely to be aware of HPV and the vaccine and likewise were less likely to be offered and receive the vaccine. More than 60% of all respondents (61.3%) had never previously been offered the vaccine, and only 24.7% of all respondents had completed the vaccine series.Conclusion: Female ED patients may represent an at-risk cohort with relatively low HPV awareness and low HPV vaccine uptake. The ED could represent a novel opportunity to access and engage high-risk HPV populations

Les progrès dans les prévisions : météorologie et économique

La modélisation et la prévision en météorologie et en économique présentent un certain nombre de caractéristiques communes qui laissent à penser qu’il pourrait être intéressant de comparer leurs récents progrès en matière de prévision. Nous portons notre attention sur deux aspects de la prévision. Premièrement, nous étudions les mesures de la valeur ajoutée des prévisions et l’évolution de ces mêmes mesures au cours des 20 à 30 dernières années ; deuxièmement, l’estimation et la représentation de l’incertitude de ces prévisions sont examinées. Nous considérons certaines variables quantitatives particulières comme étant représentatives de différents types de prévisions : température, croissance du PIB et volatilité des marchés financiers (variables continues) ; probabilité de précipitations et probabilité de récession (prévisions de probabilité) ; prévision de type 0/1 de précipitations ou de récession (prévisions binaires) ; tornades et krachs boursiers (événements rares). Nous effectuons un survol de l’information disponible à ce jour sur l’évolution de la qualité des prévisions et décrivons les méthodes en développement dans le but de comprendre et de représenter l’incertitude dans ces prévisions.Meteorological and economic modelling and forecasting have a number of common features, which suggest that it may be interesting to compare their recent progress in forecasting. We concentrate on two aspects of forecasting: first, measures of the value added of forecasts, and the evolution of these measures over approximately the last twenty to thirty years; second, the estimation and representation of uncertainty of forecasts. We follow several particular quantities as representative of different types of forecasts: temperature, GDP growth and financial market volatility (continuous variables); probability of precipitation and probability of recession; 0/1 predictions of precipitation or recession (binary forecasts); tornadoes and market crashes (rare events). We describe the available information on the evolution of forecast quality, and describe also the evolving methods for understanding and representing uncertainty in the forecasts

Les progrès dans les prévisions : météorologie et économique*

Meteorological and economic modelling and forecasting have a number of common features, which suggest that it may be interesting to compare their recent progress in forecasting. We concentrate on two aspects of forecasting: first, measures of the value added of forecasts, and the evolution of these measures over approximately the last twenty to thirty years; second, the estimation and representation of uncertainty of forecasts. We follow several particular quantities as representative of different types of forecasts: temperature, GDP growth and financial market volatility (continuous variables); probability of precipitation and probability of recession; 0/1 predictions of precipitation or recession (binary forecasts); tornadoes and market crashes (rare events). We describe the available information on the evolution of forecast quality, and describe also the evolving methods for understanding and representing uncertainty in the forecasts. La modélisation et la prévision en météorologie et en économique présentent un certain nombre de caractéristiques communes qui laissent à penser qu’il pourrait être intéressant de comparer leurs récents progrès en matière de prévision. Nous portons notre attention sur deux aspects de la prévision. Premièrement, nous étudions les mesures de la valeur ajoutée des prévisions et l’évolution de ces mêmes mesures au cours des 20 à 30 dernières années ; deuxièmement, l’estimation et la représentation de l’incertitude de ces prévisions sont examinées. Nous considérons certaines variables quantitatives particulières comme étant représentatives de différents types de prévisions : température, croissance du PIB et volatilité des marchés financiers (variables continues) ; probabilité de précipitations et probabilité de récession (prévisions de probabilité) ; prévision de type 0/1 de précipitations ou de récession (prévisions binaires) ; tornades et krachs boursiers (événements rares). Nous effectuons un survol de l’information disponible à ce jour sur l’évolution de la qualité des prévisions et décrivons les méthodes en développement dans le but de comprendre et de représenter l’incertitude dans ces prévisions.

Nowcasting GDP with electronic payments data

We assess the usefulness of a large set of electronic payments data comprising debit and credit card transactions, as well as cheques that clear through the banking system, as potential indicators of current GDP growth. These variables capture a broad range of spending activity and are available on a very timely basis, making them suitable current indicators. While every transaction made with these payment mechanisms is in principle observable, the data are aggregated for macroeconomic forecasting. Controlling for the release dates of each of a set of indicators, we generated nowcasts of GDP growth for a given quarter over a span of five months, which is the period over which interest in nowcasts would exist. We find that nowcast errors fall by about 65 per cent between the first and final nowcast. Evidence on the value of the additional payments variables suggests that there may be modest reductions in forecast loss, tending to appear in nowcasts produced at the beginning of a quarter. Among the payments variables considered, debit card transactions appear to produce the greatest improvements in forecast accuracy