Combining forecasts: some results on exchange and interest rates

The aim of this work is to investigate whether the combination of forecasts plays an important role in the improvement of forecast accuracy Particular attention is paid to: (a) the methods of forecasting (the methods compared are neural networks, fuzzy logic, GARCH models, switching regime and chaotic dynamics); (b) combining the forecasts provided by the different methods. This work has also the aim of revising a short-term econometric forecast using a longer-term forecast. The revision process usually runs the opposite way (revision is made on a longer-term forecast using a short-term one to reflect the current available information, but it is not excluded that it is possible to proceed as described above. Daily data from the financial market is used. Some empirical applications on exchange and interest rates are given.     

Intertemporal consumption choices,transaction costs and limited participation in financial markets: reconciling data and theory

This paper builds a unifying framework based on the theory of intertemporal consumption choices that brings together the limited participation‐based explanation of the Consumption Capital Asset Pricing Model's poor empirical performance and the transaction costs‐based explanation of incomplete portfolios. Using the implications of the consumption model and observed household consumption and portfolio choices, we identify the preference parameters of interest and a lower bound for the costs rationalizing non‐participation in financial markets. Using the US Consumer Expenditure Survey and assuming isoelastic preferences, we estimate the coefficient of relative risk aversion at 1.7 and a cost bound of 0.4% of non‐durable consumption. Our estimate of the preference parameter is theoretically plausible and the bound sufficiently small to be likely to be exceeded by the actual total (observable and unobservable) costs of participating in financial markets. Copyright © 2010 John Wiley & Sons, Ltd.     

Heterogeneity in Financial Market Participation: Appraising its Implications for the C-CAPM

This paper presents empirical evidence that accounting for heterogeneity in financial market participation is important for evaluating the empirical performance of the Consumption-based Capital Asset Pricing Model (C-CAPM). Using the US Consumer Expenditure Survey as a common testing ground, I re-assess three well-known characterizations of the equity premium puzzle (i) the inconsistency of the representative agent's IMRS with Hansen and Jagannathan bounds; (ii) Mehra and Prescott's calibration of a large representative agent's risk aversion; (iii) Hansen and the Singleton's large structural estimates of the preference parameters based on aggregate data. In all three cases, the estimates of risk aversion conditional upon financial market participation are not as far from reasonable values as the corresponding unconditional ones. The differences suggest that part of the equity premium puzzle can be accounted for by the use of a representative agent assumption rather than a more appropriate "representative stockholding agent assumption.     

Combination schemes for turning point predictions

We propose new forecast combination schemes for predicting turning points of business cycles. The proposed combination schemes are based on the forecasting performances of a given set of models with the aim to provide better turning point predictions. In particular, we consider predictions generated by autoregressive (AR) and Markov-switching AR models, which are commonly used for business cycle analysis. In order to account for parameter uncertainty we consider a Bayesian approach for both estimation and prediction and compare, in terms of statistical accuracy, the individual models and the combined turning point predictions for the United States and the Euro area business cycles.     

Marshallian labour market pooling: Evidence from Italy

This paper employs a unique Italian data source to take a comprehensive approach to labour market pooling. It jointly considers many different aspects of the agglomeration — labour market relationship, including turnover, learning, matching, and hold up. It also considers labour market pooling from the perspective of both workers and firms and across a range of industries. Overall, the paper finds some support for theories of labour market pooling, but the support is weak. Specifically, there is a general positive relationship of turnover to local population density, which is consistent with theories of agglomeration and uncertainty. There is also evidence of on-the-job learning that is consistent with theories of labour pooling, labour poaching, and hold up. In addition, the paper provides evidence consistent with agglomeration improving job matches. However, the labour market pooling gains that we measure are small in magnitude and seem unlikely to account for a substantial share of the agglomeration benefits accruing to Italian workers and firms.     

An Empirical Study of the Impact of CEO Characteristics on New Firms' Time to IPO*

An initial public offering (IPO) is one of the most critical events in the life of a firm. As the IPO market continues to attract attention from both entrepreneurs and investors, research examining the relationship between the firm's characteristics and its IPO performance is growing. In this paper, we use the upper echelon perspective to empirically examine the relationship between the firm's chief executive officer (CEO) and the firm's time to IPO, a relationship that has so far received little attention. Using data obtained from 237 IPOs in the U.S. software industry, we found that the CEO's prior executive experience, network, and age are significantly related to the new firm's time to IPO. This study extends the understanding of the important role of the CEO in the IPO and provides investors greater insight into those variables that influence the speed with which firms go public.