Nowcasting Unemployment

Peter Ellis takes us through an attempt to perform near-term projection of Australian unemployment rates based on macroeconomic indicators:

“Leading” in this case will have to mean pretty fast, because the official unemployment stats in Australia come out from the Australian Bureau of Statistics (ABS) with admirable promptitude given the complexity of managing the Labour Force Survey. ABS Series 6202.0 – the monthly summary from the Labour Force Survey – comes out around two weeks after the reference month. Only a few economic variables of interest are available faster than that. In this blog post I look at two candidates for leading information that are readily available in more or less real time – interest rates and stock exchange prices.

One big change in the past decade in this sort of short-term forecasting of unemployment has been to model the transitions between participation, employed and unemployed people, rather than direct modelling of the resulting proportions. This innovation comes from an interesting 2012 paper by Barnichon and Nekarda. I’ve only skimmed this paper, but I’d like to look into how much of the gains they report comes from the focus on workforce transitions, and how much from their inclusion of new information in the form of vacancy postings and claims for unemployment insurance. My suspicion is that these latter two series have powerful new information. I will certainly be returning to vacancy information and job adverts at a later time – these are items which feature prominently for me in my day job at Nous Group in analysing the labour market.

This gets a little deep but it’s well worth the read. H/T R-bloggers

An Intro to k-Means Clustering

Holger von Jouanne-Diedrich takes us through an example of how k-means clustering works:

The guiding principles are:

– The distance between data points within clusters should be as small as possible.
– The distance of the centroids (= centres of the clusters) should be as big as possible.

Because there are too many possible combinations of all possible clusters comprising all possible data points k-means follows an iterative approach

Click through for a demonstration. I appreciate adding visualizations for intermediate steps in there as well because it gives you an intuitive understanding for what the one-liner function is really doing.

Easy Navigation with Power BI

Marc Lelijveld has started a series on storytelling with Power BI. Part one is all about navigation:

Providing an easy navigation is important for the usability of your report. In order to make it as intuitive as possible, you should think as an end-user. By opening a report, where do you expect the navigation to be?

If you open the first webpage you can think of, most likely you will find the navigation on top or on the left side. Which is totally reasonable (at least according to my opinion) since we read from left to right and from top to bottom.

By default in Power BI you will have the navigation on the bottom where you can switch between your report pages. But we just concluded that it is more intuitive to put your navigation on top or on the left side. So why not do the same with your Power BI reports? We can do this by creating our own navigation and bookmarks for that!

Read the whole thing.

SQL Assessment API

Ebru Ersan announces a public preview of the SQL Assessment API:

SQL Assessment API is a new mechanism to evaluate configuration of your SQL Server for best practices. The API methods are used by means of a SQL Server Management Object (SMO) extension and new cmdlets in SqlServer PowerShell module. API is delivered with a ruleset that is highly customizable and extensible. It can be used to assess SQL Server versions 2012 and higher, both on Windows and Linux.

Looks like you can customize rules as well. I wonder if it will work better (or have more support) than Policy-Based Management. I’m also clocking how many minutes before dbatools supports this…

Procedure Parameters: Optional and Required

Kenneth Fisher takes us through procedure parameters:

If you are executing a stored procedure with a bunch of parameters it can be a bit of a pain if you have to pass a value in for each of them. Fortunately, it’s pretty easy to make some parameters required and others optional. You simply give them a default value.

Kenneth also points out that functions don’t behave this way, and shows how to handle parameters where you don’t want to accept NULL under any circumstances. This is useful when NULL is just a placeholder for “I don’t really want to use this parameter” but the application doesn’t know how to avoid sending the parameter in the first place.

LIKE Pattern Matching with Brackets

Kevin Feasel

2019-07-30

T-SQL

Madhivanan shows how you can find special characters like left brackets when performing pattern matching with LIKE:

In SQL Server you can use LIKE operator to search for particular string in a column value. However it is important to note that when you search for a string which is within square braces, you need to escape the opening square brace.

Click through for an example.

Building an Azure DevOps Pipeline

Kevin Feasel

2019-07-30

DevOps

Grant Fritchey shows off how to build an Azure DevOps pipeline:

I don’t mean for this to be a complete tutorial on setting up Azure DevOps (see the bottom of this post for my all-day, in-person, teaching sessions, where I will). I just want to discuss a set of pipelines I’ve built and why I built them that way as a method for illustrating how you can begin automating your processes in support of a DevOps implementation.

The most important concepts when we get to building and deploying within Azure DevOps are Builds and Releases. Yes, we can get into all the fun of talking about Deployment Groups (sets of servers for simultaneous deployment) and others, however, we have to first get a successful and then define how that build will get deployed/released; Builds and Releases.

Read on for the demonstration.

Gaps in Identity Columns

Josh Simar doesn’t like gaps in identity columns brought about by rollbacks:

At the end of this you can see that those records were in the table before the rollback as evidenced by the now 2000 records in the table. However we ran our identity checker after the rollback and we’ll see some interesting results because of that.

Checking identity information: current identity value '2000', current column value '1000'. DBCC execution completed. If DBCC printed error messages, contact your system administrator.

The rollback did absolutely nothing for our identity and left it as is.

The short answer is that this is expected and reasonable behavior. Don’t expect identity integers to be sequential; the best you’ll get (assuming no resets or overflows) is a guarantee that they’ll be monotonically increasing. I left the long answer as a comment, currently awaiting moderation.

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