We develop a non-linear forecast combination rule based on copulas that incorporate the dynamic interaction between individual predictors. This approach is optimal in the sense that the resulting combined forecast produces the highest discriminatory power as measured by the receiver operating...

Ranking methods are fundamental tools in many areas. Popular methods aggregate the statements of 'experts' in different ways. As such, there are various reasonable ranking methods, each one of them more or less adapted to the environment under consideration. This paper introduces a new method,...

We analyze the effects of better algorithmic demand forecasting on collusive profits. We show that the comparative statics crucially depend on the whether actions are observable. Thus, the optimal antitrust policy needs to take into account the institutional settings of the industry in question....

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...

This paper investigates persistence in financial time series at three different frequencies (daily, weekly and monthly). The analysis is carried out for various financial markets (stock markets, FOREX, commodity markets) over the period from 2000 to 2016 using two different long memory...

Sophisticated collusive compensation schemes such as assigning future market shares or direct transfers are frequently observed in detected cartels. We show formally why these schemes are useful for dampening deviation incentives when colluding firms are temporary asymmetric. The relative...