Linear and Threshold Forecasts of Output and Inflation with Stock and Housing Prices

The authors examine whether simple measures of Canadian equity and housing price misalignments contain leading information about output growth and inflation. Previous authors have found that the information content of asset prices in general, and equity and housing prices in particular, are unreliable in that they do not systematically predict future economic activity or inflation. However, earlier studies relied on simple linear relationships that would fail to pick up the potential non-linear effects of asset-price misalignments. The authors' results suggest that housing prices are useful for predicting GDP growth, even within a linear context. Moreover, both stock and housing prices can improve inflation forecasts, especially when using a threshold specification. These improvements in forecast performance are relative to the information contained in Phillips-curve type indicators for inflation and IS-curve type indicators for GDP growth.Inflation and prices; Business fluctuations and cycles

A Consistent Test for Multivariate Conditional Distributions

We propose a new test for a multivariate parametric conditional distribution of a vector of variables yt given a conditional vector xt. The proposed test is shown to have an asymptotic normal distribution under the null hypothesis, while being consistent for all fixed alternatives, and having non-trivial power against a sequence of local alternatives. Monte Carlo simulations show that our test has reasonable size and good power for both univariate and multivariate models, even for highly persistent dependent data with sample sizes often encountered in empirical finance.Econometric and statistical methods

A Note on Monitoring Daily Economic Activity Via Electronic Transaction Data

Economists have traditionally relied on monthly or quarterly data supplied by central statistical agencies for macroeconomic monitoring. 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, and using two potentially important economic events as examples. In particular we are able to analyze expenditure patterns around the September 11 terrorist attacks and the August 2003 electrical blackout, and are able to note qualitative differences in the effects of these events which could not be observed through aggregate measures. Les économistes se sont traditionnellement appuyés sur les données mensuelles ou trimestrielles publiées par les agences centrales de statistiques pour suivre la situation macroéconomique. Cependant, les avancées technologiques qui ont été réalisées au cours des dernières années ont entraîné de nouvelles sources de données à haute fréquence, et ces dernières pourraient potentiellement donner lieu à une information plus exacte et plus opportune sur l’état actuel de l’activité économique. Dans le document actuel, nous explorons l’utilité des transactions électroniques comme indicateurs en temps réel de l’activité économique. Pour ce faire, nous recourons aux données canadiennes sur les cartes de débit et utilisons, à titre d’exemples, deux événements économiques susceptibles d’être importants. Plus particulièrement, nous sommes en mesure d’analyser la structure des dépenses lors des attaques terroristes du 11 septembre et de la panne d’électricité d’août 2003 et de noter des différences qualitatives dans les répercussions de ces événements, lesquelles ne pourraient être observées en recourant aux mesures globales.debit cards, electronic transactions, monitoring , cartes de débit, transactions électroniques, suivi

Credit, Asset Prices, and Financial Stress in Canada

Historical narratives typically associate financial crises with credit expansions and asset price misalignments. The question is whether some combination of measures of credit and asset prices can be used to predict these events. Borio and Lowe (2002) answer this question in the affirmative for a sample of 34 countries, but the question is surprisingly difficult to answer for individual developed countries that have faced very few, if any, financial crises in the past. To circumvent this problem, we focus on financial stress and ask whether credit and asset price movements can help predict it. To measure financial stress, we use the Financial Stress Index (FSI) developed by Illing and Liu (2006). Other innovations include the estimation and forecasting using both linear and endogenous threshold models, and a wide range of asset prices (stock and housing prices, for example). The exercise is performed for Canada, but the methodology is suitable for any country that fits the above description.Credit and credit aggregates; Financial stability

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

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 WE FORECAST? FORECAST CONTENT HORIZONS FOR SOME IMPORTANT MACROECONOMIC TIME SERIES

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. In this paper we estimate such content horizons for a variety of economic variables, and compare these with the maximum horizons which we observe reported in a large sample of empirical economic forecasting studies. We find that there are many instances of published studies which provide forecasts exceeding, often by substantial margins, our estimates of the content horizon for the particular variable and frequency. We suggest some simple reporting practices for forecasts that could potentially bring greater transparency to the process of making the interpreting economic forecasts.

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

Analyzing Economic Effects of Extreme Events using Debit and Payments System Data

This paper uses payments system data to study the impact on personal consumption expenditure, and therefore on economic activity, of occasional extreme events. The usual quarterly data supplied by central statistical agencies are of little use to policy makers for monitoring effects of transitory events, as the impacts of events lasting a few days or weeks may be obscured in time-aggregated data. 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 economic activity. Here we use daily Canadian debit transaction volume data, and business-day (five times per week) debit and check transaction volume and value data, to investigate the impact on consumer expenditure of several extreme events: the September 11 2001 terrorist attacks, the SARS epidemic in the spring of 2003, and the August 2003 electrical blackout. Contrary to initial perceptions of these events, we find only small and transitory effects.debit card transactions, macroeconomic monitoring, real-time data,

Quantity, Quality, and Relevance: Central Bank Research, 1990-2003

The authors document the research output of 34 central banks from 1990 to 2003, and use proxies of research inputs to measure the research productivity of central banks over this period. Results are obtained with and without controlling for quality and for policy relevance. The authors find that, overall, central banks have been hiring more researchers and publishing more research since 1990, with the United States accounting for more than half of all published central bank research output, although the European Central Bank is rapidly establishing itself as an important research centre. When controlling for research quality and relevance, the authors generally find that there is no clear relationship between the size of an institution and its productivity. They also find preliminary evidence of positive correlations between the policy relevance and the scientific quality of central bank research. There is only very weak evidence of a positive correlation between the quantity of external partnerships and the productivity of researchers in central banks.Central bank research