In this paper, we use high-frequency transaction data to develop a weekly tracker for private consumption expenditures. Furthermore, we apply the transaction data in a nowcasting experiment and compare their performance with other, readily available indicators that are regularly linked to...

The focus of this paper is on nowcasting and forecasting quarterly private consumption. The selection of real-time, monthly indicators focuses on standard (“hard”/“soft” indicators) and less-standard variables. Among the latter group we analyze: i) proxy indicators of economic and policy...

This article is the first formal investigation of consumer attitudes that compares the forecasting power of the University of Michigan's Index of Consumer Sentiment and the Conference Board's Consumer Confidence Index. The authors find that measures available from the Conference Board have both...

We propose a regression-based algorithm that allows to construct arbitrarily many comparable, multi-annual, consistent time series on monthly, weekly, daily, hourly and minute-by-minute search volume indices based on the scattered data obtained from Google Trends. The accuracy of the algorithm...

We assess the ability of the Reuters/Michigan Surveys of Consumers to predict future changes in consumer expenditures. The information in the Surveys is summarized by means of principal components of consumer attitudes with respect to income and wealth, interest rates, and prices. These summary...

We use a novel data set covering all domestic debit card transactions in physical terminals by Norwegian households, to nowcast quarterly Norwegian household consumption. These card payments data are free of sampling errors and are available weekly without delays, providing a valuable early...

We propose the use of Google online search data for nowcasting and forecasting the number of food stamps recipients. We perform a large out-of-sample forecasting exercise with almost 3000 competing models with forecast horizons up to 2 years ahead, and we show that models including Google search...

We study the role of consumer confidence in forecasting real personal consumption expenditure, and contribute to the extant literature in three substantive ways: First, we reexamine existing empirical models of consumption and consumer confidence not only at the quarterly frequency, but using...

We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model provides favorable credit risk assessment to young borrowers relative to standard credit scoring...

We examine the information content of a newly created news sentiment index from over 300,000 articles from some of the most widely read newspapers in the US to explain changes in the University of Michigan Index of Consumer Sentiment from 1995 to 2009. Using ARMA-models, we show that consumer...