Forecasting aggregates using panels of nonlinear time series

Macroeconomic time series such as total unemployment or total industrial production concern data which are aggregated across regions, sectors, or age categories. In this paper we examine whether forecasts for these aggregates can be improved by considering panel models for the disaggregate series. As many macroeconomic variables have nonlinear properties, we specifically focus on panels of nonlinear time series. We discuss the representation of such models, parameter estimation and a method for generating forecasts. We illustrate the usefulness of our approach for simulated data and for the US coincident index, making use of state-specific component series.     

The Effects of Following Islam in Decisions about Taboo Products

This study tests the role of consumers’ religious motivation (intrinsic and extrinsic) in their decision making regarding the consumption of a prohibited brand or product category because of a religious declaration called Fatwa. Despite numerous studies on the effects of religion in consumers’ marketplace behaviors, little is known of about consumers’ decision making under a religious ruling like Fatwa. A Fatwa is a decree issued by religious scholars for Muslim communities. A survey based on the Theory of Planned Behavior asked young adult Muslims about their responses to a brand, and two product categories that were subject to Fatwa. The data concerning the respondents’ decision making were analyzed using structural equation modeling to test hypotheses based on the available literature. The analyses found that the respondents’ motivation in following Islamic teachings had the greatest effects in their deciding to smoke, listen to contentious popular music, but was not relevant for buying the Coca Cola brand. The results are discussed in terms of the study's theoretical contributions, managerial implications, and future research.     

Recognizing the benefits of diversity: When and how does diversity increase group performance?

Diversity does not only bring positive consequences. It has often been recognized that heterogeneity in teams can reduce intra-group cohesiveness, and that it can lead to conflicts and misunderstandings which, in turn, can lower employee satisfaction, citizenship behaviors and increase turnover. On the other hand, there is also evidence for performance-increasing effects of diversity because it can improve creativity and innovation through the team members' greater variety of perspectives. Little is known, however, about the conditions and the psychological mechanisms required for increasing group performance under diverse settings. Answers to research questions such as how and when diversity influences performance at work are still limited. The purpose of the paper is to provide theoretical answers to these questions by proposing a model of managing diversity which draws on social psychology theories. The model brings a new perspective by identifying the process of learning from one another's identity within a group. This process underlies two different levels of mechanisms (individual and group level). The model proposes that when these social psychological mechanisms are activated, diversity will lead to an increase in group performance. The model also suggests that collective identity is salient and when psychological safety climate are the psychological conditions that activate these mechanisms.     

Asymmetric effects of federal funds target rate changes on S&P100 stock returns,volatilities and correlations

We study the effects of FOMC announcements of federal funds target rate decisions on individual stock returns, volatilities and correlations at the intraday level. For all three characteristics we find that the stock market responds differently to positive and negative target rate surprises. First, the average response to positive surprises (that is, bad news for stocks) is larger. Second, in case of bad news the mere occurrence of a surprise matters most, whereas for good news its magnitude is more important. These new insights are possible due to the use of high-frequency intraday data.     

Assessing the impact of NERICA on income and poverty in central and western Uganda

Persistent food shortage and poverty in sub-Saharan Africa (SSA) are major development issues. New Rice for Africa (NERICA) was developed to boost crop yield and income of rural households in SSA. Although its high-yielding traits have become fairly well known, there is no empirical analysis of its impact on income and poverty. By taking the case of Uganda where a NERICA promoting program was initiated as one of the major poverty eradication measures, this study attempts to compare actual income with the hypothetical income without NERICA. We found that introduction of NERICA decreases poverty to a significant extent without deteriorating income distribution.     

Globalisation and Policy Effects in Africa

Globalisation or market integration in Sub‐Saharan Africa is closely linked to the structural adjustment programmes. In this paper we focus on their dependence on the politics and institutional characteristics of the countries concerned. In particular, we argue that one important explanation for the dismal performance of many African countries, in spite of all the measures taken towards market liberalisation, is the combination of, first, a magnification of the effects of policy and, second, a lack of willingness or ability on the part of politicians to respect the restrictions imposed on their behaviour and policy choices by the liberalised markets. We look at how the increased exposure to international prices and returns on assets make the economic equilibrium relations – the law of one price and uncovered interest parity – relevant guidelines for economic policy. The argument is illustrated by the case of Zimbabwe, where lack of respect for the restrictions imposed by international markets has led to an economic crisis with negative growth rates and a departure from globalisation.     

Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination

In this paper, we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR), and neural network (NN) time series models for 47 monthly macroeconomic variables of the G7 economies. Unlike previous studies that typically consider multiple but fixed model specifications, we use a single but dynamic specification for each model class. The point forecast results indicate that the STAR model generally outperforms linear autoregressive models. It also improves upon several fixed STAR models, demonstrating that careful specification of nonlinear time series models is of crucial importance. The results for neural network models are mixed in the sense that at long forecast horizons, an NN model obtained using Bayesian regularization produces more accurate forecasts than a corresponding model specified using the specific-to-general approach. Reasons for this outcome are discussed.     

Competitive reaction- and feedback effects based on VARX models of pooled store data

We apply a model that accommodates dynamic phenomena in demand- and reaction functions. The latter functions capture reactions to actions as well as to consequences of actions. We estimate a fixed effects VARX model with dynamic and interactive effects for multiple brands based on pooled time series and cross-sectional data for two product categories. The Impulse Response Analysis (IRA) results for one category (tuna) under different scenarios show that the inclusion/exclusion of competitive reaction- and feedback effects matters a lot, consistent with a high degree of competitive interaction in this market. We find that the role of cross-brand feedback effects is greater than the role of traditional competitive reaction effects. Intrafirm effects (internal decisions) also play an important role. In a decomposition study we show that the exclusion of these effects may either increase (up to 12%) or decrease (by as much as 50%) the net unit sales effect of a 20% price reduction. By contrast, in the second category (shampoo), where brands have distinct positions, the exclusion of these effects matters very little.