Sincere congratulations to Mark O’Sullivan for winning this year’s grand prize with an exceptional innovation and equally superb presentation. Also, sincere congratulations to prize winners Aleksander Jaksic, Nick Cotter Callan Maguire and to all respective finalists for doing this competition so proud.

Thank you to Professor John O’Halloran for a hugely positive address and informed perspective on modern innovation. Thank you also to Professor Joe Bogue for a profound contribution to the entrepreneurial insights of their candidates throughout their respective journeys.

I would like to give a significant mention to Peter Finnegan of Blackstone Launchpad who is a huge driving force of this competition on an annual basis. On a similar note, thank you to Eamon Curtin for his ongoing endeavours to drive these entrepreneurs forward.

Finally, I would like to thank my superb fellow panel of judges Alan Carroll of Bank of Ireland, Pat Lynch of EviView Ltd. and Paul McGuirk of LEO Cork City. It was a pleasure working with you.

Thank you all for a superb occasion yet again and it was a pleasure meeting all of you.

Gary Wade

#ucc #entrepreneurial #innovation

The post UCC Entrepreneur of the Year Awards 2020 first appeared on GSE.

If you are interested in watching a recent address I gave at the European Youth Parliament, you can view it here.

In times like these, when our society faces unprecedented uncertainty, it is almost always our nurses and the police force that we turn to. Yet, we never pay them what they deserve. Even though they are quite literally the fabric of our society.

Maybe when the present COVID-19 pandemic has passed, we will resurrect stronger values. Maybe we will pay nurses and the police force what they are worth. Maybe these young, future leaders of Europe will one day bring such values to society.

Maybe they will promote empathy and positivity. Maybe for example they will ban tabloid invasion and we will all have a better sense of community across the globe rather than one of division. I certainly hope so.

This talk is based upon one thing. Empathy. What we all need to realise more than ever is that empathy is what can save the world. Never has it been more evident than right now.

Thank you to European Youth Parliament Ireland for the invitation to speak at this event. It was a pleasure to be involved.

Take care.

Gary Wade

The post Gary Wade keynote address at European Youth Parliament, 2020 first appeared on GSE.

So what is interval data?

When used in econometrics, interval data is that which is a fixed distance apart on a given scale. For example, the measurement of temperature on a given scale would be an example of this. This is often seen in economics and econometrics when we are comparing different time frames.

For example, all years are measured in the same interval and as a result data can be placed consistently in a fixed time period. For example, the Gross Domestic Product of the Netherlands over the period 2015 to 2020 is a five year period or interval across which we can make an econometric assessment.

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The key in econometrics to what a particular concept means is often found in the title. In this case, for example  ordinal data is data which is in ‘order’. This data would include grades received on a college exam, the varying income brackets that people are in or their respective rank in the military for example.

This type of data is also very useful in quantifying opinion. For example, if you were to ask a set of tourists their opinion regarding a given holiday, you could measure their opinion on a Likert Scale such as:

1. Extremely enjoyable 2. Moderately enjoyable 3. Adequate 4. Unenjoyable 5. Extremely unenjoyable.

Such a Likert Scale as this one will always produce ordinal or ‘ordered’ data. However, the difference between each category in this case cannot be quantified.

That is ordinal data!

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A useful way of remembering the meaning of a nominal scale variable is that a category is ‘nominated’ in each scenario hence nominal. In fact, the word nominal derives from the late word for ‘name’ which is nomin.

Nominal scale variables occur where data is put into a certain set of categories. Further examples would include categorizing the type of internet provider you use from a selection or the type of insurance you possess. In econometrics, these nominal scale variables are known as dummy variables.

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The post Econometrics: What is nominal data? first appeared on GSE.

So what is cross-sectional data?

Cross-sectional data is effectively a snap shot of a given population. For example if you wanted to achieve a measure of income levels in a country at a given point in time, you make decide to take a random sample of 10,000 people and survey these participants. Such a sampling process will give you a specific point in time where you can describe the income levels of the population as a whole based on your snap shot of the population in question.

Simple!

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An example of time series data used in econometrics would be that of share price movements. These data points increase, decrease or remain the same and can be plotted accordingly. These respective data points are often plotted in a line graph to give a visual representation of the performance of the stock in question. Other examples of data that occurs in a time series is weather reports or  population censuses.

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Take an example where we have 200 observations (N) and four variables with one constant. In the case of this model itself, the actual model has 4 degrees of freedom because the model has 4 independent variables within which the data is ‘free’ to vary. 1 degree of freedom out of the 200 observations is lost due to the present of the constant or intercept. Obviously, because the constant is fixed, it cannot vary. The remaining degrees of freedom equal to 195 are associated with the remainder which is the residual. Therefore, we have 200 observations with 199 degrees of freedom after you take 1 away for the presence of the constant. Hence, why we often observe the degrees of freedom presented as N-1.

As a general rule, the lower the degrees of freedom, the less reliable the estimates will be and vice versa.

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Read our post on Classical Assumption 3 here.

Broad assumptions regarding an independent variable can often result in endogeneity in a linear regression model. For example, let’s say that we are running a regression to assess the relationship between age and earnings. Now we would assume that as someone ages that they begin to earn more money. However, if we assume that age is the only determinant of earnings then this variable is going to be correlated to the error term.

Why?

Because there are other regressors know as instrument variables (IV) such as experience and education for example which will play a key role in determining the level of income that an individual will attain. Therefore our explanatory variable of age will be correlated to variables accounted for within the error term.

As discussed in a previous post on the error term, the error term is the difference between what your model estimates as Y and the true population value of Y. Now if you have not accounted for the inclusion of certain variables in your models, then these will be correlated with your regressor and will be accounted for within the error term.

If endogeneity is present in your regression, this model’s estimates cannot be relied upon.

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The post Econometrics: What is Endogeneity? first appeared on GSE.

The logic is to start at a core set of explanatory variables and only add to this selection if a substantial amount of variation has not been explained by this collection of regressors.

Of course, if there is a variable which theoretically seems likely to exert a significant impact on the independent variable, Y, then of course this should be included.

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The post Econometrics: What is parsimony? first appeared on GSE.