Reevaluating the prudence of economic forecasts in the EU: The role of instrument persistence

Christodoulakis and Mamatzakis (2009, Journal of Applied Econometrics 24, pp. 583–606) estimate the EU Commission loss preferences for selected economic forecasts of 12 EU Member States. They employ the generalized method of moments (GMM) estimation procedure proposed by Elliott et al. (2005, Review of Economic Studies 72, pp. 1107–1125) and find the forecasts to be somewhat optimistic on average. However, this note shows the GMM estimator to possess nonstandard limiting distributions when some of the instruments are highly persistent, which is the case with one of the instruments employed by Christodoulakis and Mamatzakis. Standard distributions are recovered in some interesting particular cases which are relevant in practice. A reexamination of the EU Commission loss preferences using methods robust to persistence and a dataset extended to 2017 reveals that, while the conclusions of the original study are, by and large, still justified, the EU Commission loss preferences have become more symmetric over the whole studied period.     

Incorporating Asymmetric Preferences into Fan Charts and Path Forecasts*

Ordinary fan charts consist of symmetric marginal forecast intervals, and do not take into consideration the concrete loss function of the user of the forecast. The note shows how to build fan charts that have exact joint coverage even under asymmetric loss, and maintain at the same time the intuition conveyed by ordinary fan charts. The proposed method is computationally simple, and easily implemented with any loss function. The differences between the information conveyed by fan charts with or without asymmetries, and with or without exact joint coverage, are illustrated with a Bayesian forecast exercise of US GDP growth rates.     

Combining Significance of Correlated Statistics with Application to Panel Data*

The inverse normal method, which is used to combine P‐values from a series of statistical tests, requires independence of single test statistics in order to obtain asymptotic normality of the joint test statistic. The paper discusses the modification by Hartung (1999, Biometrical Journal, Vol. 41, pp. 849–855) , which is designed to allow for a certain correlation matrix of the transformed P‐values. First, the modified inverse normal method is shown here to be valid with more general correlation matrices. Secondly, a necessary and sufficient condition for (asymptotic) normality is provided, using the copula approach. Thirdly, applications to panels of cross‐correlated time series, stationary as well as integrated, are considered. The behaviour of the modified inverse normal method is quantified by means of Monte Carlo experiments.     

Exploring the role of FDI in enhancing the entrepreneurial activity in Europe: a panel data analysis

This paper explores the impact of foreign direct investment (FDI) on the entrepreneurial activity in 16 European countries. By using Global Entrepreneurship Monitor (GEM) data, which enables the distinction necessity-driven vs. opportunity-driven entrepreneurs, we assess the influence of both inward and outward FDI on the entrepreneurial activity during the time span 2005–2012. We resort to a static, as well as to a dynamic panel data analysis. Our findings highlight the fact that the FDI has no clear influence on the total entrepreneurial activity, or on the established business ownership rate. Nevertheless, our results clearly state that both inward and outward FDI positively influences the necessity-driven entrepreneurs, while having a negative impact on the opportunity-driven entrepreneurs. The results prove to be robust regarding the use of a fixed and random effects panel model, two stages least square (2SLS) model, as well as the use of a system-Generalized Method of Moments (system-GMM) approach.     

Pitfalls of post-model-selection testing: experimental quantification

Traditional specification testing does not always improve subsequent inference. We demonstrate by means of computer experiments under which circumstances, and how severely, data-driven model selection can destroy the size properties of subsequent parameter tests, if they are used without adjusting for the model-selection step. The investigated models are representative of macroeconometric and microeconometric workhorses. The model selection procedures include information criteria as well as sequences of significance tests (“general-to-specific”). We find that size distortions can be particularly large when competing models are close, with closeness being defined relatively to the sample size.     

Enhancing human–computer interaction design education: teaching affordance design for emerging mobile devices

The evolution of human–computer interaction design (HCID) over the last 20 years suggests that there is a growing need for educational scholars to consider new and more applicable theoretical models of interactive product design. The authors suggest that such paradigms would call for an approach that would equip HCID students with a better understanding of the social context of technology design and development. An intrinsic part of the proposed pedagogical model is the concept of affordance or that which implicitly suggests to the user a particular kind of functionality of the product. According to cognitive theory, people approach multi-functional mobile devices by building mental models of their functions, starting with physical appearance. A case study of an HCID teaching strategy, based upon the primacy of affordance, highlights how students can be taught a range of knowledge domains for product design to support creative problem-solving and critical thinking skills.     

Testing for mutually exciting jumps and financial flights in high frequency data

We propose a new nonparametric test to identify mutually exciting jumps in high frequency data. We derive the asymptotic properties of the test statistics and show that the tests have good size and reasonable power in finite sample cases. Using our mutual excitation tests, we empirically characterize the dynamics of financial flights in forms of flight-to-safety and flight-to-quality. The results indicate that mutually exciting jumps and risk-off trades mostly occur in periods of high market stress. Flight-to-safety episodes (from stocks to gold) arrive more frequently than do flight-to-quality spells (from stocks to bonds). We further find evidence that reverse cross-excitations or seeking-return-strategies exhibit significant asymmetry over the business cycle, reflecting the fact that investors appear to be selling gold – rather than bonds – to invest in stocks during good market conditions.     

Enhancing the local power of IVX-based tests in predictive regressions

IVX estimation is used increasingly often in predictive regressions with regressors of unknown persistence. While not exhibiting the second-order bias the OLS estimator has in this setup, IVX estimators have reduced rates of convergence when the regressors are highly persistent. The reduced convergence rates may sometimes lead to power losses in finite samples when testing for no predictability, for instance. The note discusses a simple way of improving the local power of IVX-based tests, consisting of augmenting the predictive regression with the lagged dependent variable. This implies a feed-back loop which strengthens the signal of the IVX instrument without changing its dynamic properties. The proposed augmentation works best when the power loss of IVX would have been maximal compared to the infeasible OLS-based test.     

Multiple Testing for No Cointegration under Nonstationary Volatility

With cointegration tests often being oversized under time‐varying error variance, it is possible, if not likely, to confuse error variance non‐stationarity with cointegration. This paper takes an instrumental variable (IV) approach to establish individual‐unit test statistics for no cointegration that are robust to variance non‐stationarity. The sign of a fitted departure from long‐run equilibrium is used as an instrument when estimating an error‐correction model. The resulting IV‐based test is shown to follow a chi‐square limiting null distribution irrespective of the variance pattern of the data‐generating process. In spite of this, the test proposed here has, unlike previous work relying on instrumental variables, competitive local power against sequences of local alternatives in 1/T‐neighbourhoods of the null. The standard limiting null distribution motivates, using the single‐unit tests in a multiple testing approach for cointegration in multi‐country data sets by combining P‐values from individual units. Simulations suggest good performance of the single‐unit and multiple testing procedures under various plausible designs of cross‐sectional correlation and cross‐unit cointegration in the data. An application to the equilibrium relationship between short‐ and long‐term interest rates illustrates the dramatic differences between results of robust and non‐robust tests.