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In a brilliant career spanning almost five decades, Sir Clive Granger has made numerous contributions to time series econometrics. This paper reappraises his very first paper, published in 1957 on sunspot numbers.

Historically, the study of the world’s economy was classified into Micro-economics and Macro-economics. Perhaps, contemporary economists should learn from the universe which we are part of. Let us name this as “Uni-economics.” Many scientists have found that sunspots affect human behavior. Some research findings even relate the 11-year periodic cycle to war and peace of mankind. It is also widely known in the medical profession that sunspot radiation actually affects our human body. With all these evidence in mind, the purpose of this paper is to investigate how sunspot activities can affect business growth since 1960s when the global economy was building up fast since the Second World War.

The econometrics methodology deployed is in three steps. First, time series techniques were deployed to track down the changes of Sunspot Counts over the last 48 years on the world’s four main financial indices, i.e., S & P, FTSE, Nikkei and HSI. Second, the long run function of a particular stock price index could be specified as a natural logarithm transformation function. Finally, Granger’s co-integration methodology is deployed to test the equilibrium relationships.

This paper has harnessed a set of system and process that can ensure the long-term productivity and business growth of firms in this contemporary business world. It is predicted with 80 percent confidence that the next property depression in some affluent cities will happen in 2014/2015, with the global financial tsunami coming in 2019.

More important is how organizations can make use of the trade-wind and avoid the counter-wind from the Uni-economics phenomena for their quality, productivity and business growth, in accordance to the Deming Cycle. As recalled from the previous two oil crisis (around 1975, 1986) and the two financial tsunami (1997 and 2008), 40-year-old organizations that can still survive today must have done something good, despite all these turmoil confronting them.

There is a type of art that is known as “naive,” which can be very simplistic and have a certain amount of charm. Reading about my own work on sunspots published in 1957 also brings to mind the description “naive.” I was certainly naive in thinking that I could undertake a piece of simple numerical analysis and then send it off to a major journal. The world of empirical research was then very simplistic as we had no computer at the University of Nottingham where I was employed, and all calculations had to be done on an electronic calculator and all plots by hand. It was clear that if monthly data for sunspots had been available, I would have been overwhelmed by the quantity of data! Trying to plot by hand nearly 200 years of monthly data is a lengthy task! The computing restrictions also limited the types of model that could be considered.

The purpose of this study is to evaluate a practical laboratory task where final year undergraduate students design, implement and validate an inferred security wireless sensor access system.

The quality of the learning and technical environment was evaluated from a number of perspectives using a mixed methods approach where both quantitative and qualitative data was collected and analysed. Two‐tailed paired t‐tests were used to analyse data on student performance, while a targeted survey was used to assess the achievement of the learning outcomes. The students’ experience of working with the Sun Microsystems SunSPOTs was evaluated and the evolution of their perception of both ubiquity and security from inception to completion of the activity was explored.

The results of this study indicate that the students’ level of engagement with the assigned laboratory task was higher than with the two other forms of continuous assessment used on the module. Quantitative feedback gathered indicated that the learning outcomes were achieved, while the qualitative data indicated a high level of student satisfaction with the laboratory assignment.

A novel wireless sensor network laboratory that encourages students to engage with the concepts of ubiquity and security is presented and evaluated.

This paper surveys the treatment of expectations in estimated Dynamic Stochastic General Equilibrium (DSGE) macroeconomic models.

A recent notable development in the empirical macroeconomics literature has been the rapid growth of papers that build structural models, which include a number of frictions and shocks, and which are confronted with the data using sophisticated full-information econometric approaches, often using Bayesian methods.

A widespread assumption in these estimated models, as in most of the macroeconomic literature in general, is that economic agents' expectations are formed according to the Rational Expectations Hypothesis (REH). Various alternative ways to model the formation of expectations have, however, emerged: some are simple refinements that maintain the REH, but change the information structure along different dimensions, while others imply more significant departures from rational expectations.

I review here the modeling of the expectation formation process and discuss related econometric issues in current structural macroeconomic models. The discussion includes benchmark models assuming rational expectations, extensions based on allowing for sunspots, news, sticky information, as well as models that abandon the REH to use learning, heuristics, or subjective expectations.

In this article, we survey experiments that are directly related to monetary policy and central banking. We argue that experiments can also be used as a tool for central bankers for bench testing policy measures or rules. We distinguish experiments that analyze the reasons for non-neutrality of monetary policy, experiments in which subjects play the role of central bankers, experiments that analyze the role of central bank communication and its implications, experiments on the optimal implementation of monetary policy, and experiments relevant for monetary policy responses to financial crises. Finally, we mention open issues and raise new avenues for future research.