An empirical note about estimation and forecasting Latin American Forex returns volatility: the role of long memory and random level shifts components

Portuguese Economic Journal - A set of RLS-type models with ARMA and ARFIMA dynamics is estimated and compared in a forecasting exercise with ARFIMA, GARCH and FIGARCH models. It is an extension of...     

Hayek on the essential dispersion of market knowledge

The Review of Austrian Economics - A key insight in Hayek’s thought is the importance of so-called “local knowledge” in economic activity. In The Fatal Conceit, he states that...     

Data-based mechanistic modelling and forecasting globally averaged surface temperature

The main objective of this paper it to model the dynamic relationship between global averaged measures of Total Radiative Forcing (RTF) and surface temperature, measured by the Global Temperature Anomaly (GTA), and then use this model to forecast the GTA. The analysis utilizes the Data-Based Mechanistic (DBM) approach to the modelling and forecasting where, in this application, the unobserved component model includes a novel hybrid Box-Jenkins stochastic model in which the relationship between RTF and GTA is based on a continuous time transfer function (differential equation) model. This model then provides the basis for short term, inter-annual to decadal, forecasting of the GTA, using a transfer function form of the Kalman Filter, which produces a good prediction of the ‘pause’ or ‘levelling’ in the temperature rise over the period 2000 to 2011. This derives in part from the effects of a quasi-periodic component that is modelled and forecast by a Dynamic Harmonic Regression (DHR) relationship and is shown to be correlated with the Atlantic Multidecadal Oscillation (AMO) index.     

The power of positivity? The influence of positive psychological capital language on crowdfunding performance

We extend the entrepreneurship literature to include positive psychological capital — an individual or organization's level of psychological resources consisting of hope, optimism, resilience, and confidence — as a salient signal in crowdfunding. We draw from the costless signaling literature to argue that positive psychological capital language usage enhances crowdfunding performance. We examine 1726 crowdfunding campaigns from Kickstarter, finding that entrepreneurs conveying positive psychological capital experience superior fundraising performance. Human capital moderates this relationship while social capital does not, suggesting that costly signals may, at times, enhance the influence of costless signals. Post hoc analyses suggest findings generalize across crowdfunding types, but not to IPOs.     

Price dispersion and optimal price categories with limited memory consumers

In this paper, we study consumers with limited memory and examine the effects of their price categorization on the pricing strategies of competing firms. The valuations of consumers are assumed to be heterogeneous. We find that it is possible to observe price dispersion even when each firm charges a single price if the consumers categorize prices non‐optimally. Moreover, we demonstrate that the likelihood of a price dispersion outcome is reduced when consumers with limited memory set up the price categories optimally. These findings suggest that the consumers' limited memory and their sub‐optimal behavior, that is, their inability to choose price categories optimally can be a reason for observed price dispersion.     

The US financial crisis and corporate dividend reactions: for better or for worse?

Review of Quantitative Finance and Accounting - We examine how changes in dividend policy in 2008 as the financial crisis was unfolding influenced firm risk-adjusted returns in the following years....     

Information flows and crashes in dynamic social networks

We develop a dynamic model of information transmission and aggregation in social networks in which continued membership in the network is contingent on the accuracy of opinions. Agents have opinions about a state of the world and form links to others in a directed fashion probabilistically. Agents update their opinions by averaging those of their connections, weighted by how long their connections have been in the system. Agents survive or die based on how far their opinions are from the true state. In contrast to the results in the extant literature on DeGroot learning, we show through simulations that for some parameterizations the model cycles stochastically between periods of high connectivity, in which agents arrive at a consensus opinion close to the state, and periods of low connectivity, in which agents’ opinions are widely dispersed.

     

Missing the Popular Vote: Pitfalls in US Democracy and Reform Proposals

Intereconomics - Only a few years ago, it was a widespread belief that globalisation would trigger processes of democratisation worldwide. However, even old and established democracies such as the...     

The Cost of War: A Comment and a Reply

Intereconomics - In the July/August issue of Intereconomics, Ilona Sologoub argued that increasing the cost of war would limit the ability of an authoritarian state to wage a war. Here, Charles D....