Emerging tracking data allow precise predictions of individuals' reservation values. However, firms are reluctant to conspicuously implement personalized pricing because of concerns about consumer and regulatory reprisals. This paper proposes and applies a method which disguises personalized...

This paper conducts a broad-based comparison of iterated and direct multi-step forecasting approaches applied to both univariate and multivariate models. Theoretical results and Monte Carlo simulations suggest that iterated forecasts dominate direct forecasts when estimation error is a...

For forecasting and economic analysis many variables are used in logarithms (logs). In time series analysis this transformation is often considered to stabilize the variance of a series. We investigate under which conditions taking logs is beneficial for forecasting. Forecasts based on the...

In this paper we specify a multi-factor long-memory process that enables us to estimate the fractional differencing parameters at each frequency separately, and adopt this framework to model quarterly prices in three European countries (France, Italy and the UK). The empirical results suggest...

This paper introduces the concepts of time-specific weak and strong cross section dependence. A double-indexed process is said to be cross sectionally weakly dependent at a given point in time, t, if its weighted average along the cross section dimension (N) converges to its expectation in...

We investigate the dynamics of prices, information and expectations in a competitive, noisy, dynamic asset pricing equilibrium model. We show that prices are farther away from (closer to) fundamentals compared with average expectations if and only if traders over- (under-) rely on public...

This paper provides evidence that informed traders dominate the response of limit-order submissions to shocks in a pure limit-order market. In the market we study, informed traders are highly sensitive to spreads, volatility, momentum and depth. By contrast, uninformed traders are relatively...