Excel: Economic Analysis and Data Analytics
Big data is transforming the world of business. Yet many people don't understand what big data and
business intelligence are, or how to apply the techniques to their day-to-day jobs. This course
addresses that knowledge gap by showing how to use large volumes of economic data to gain key
business insights and analyze market conditions.
Professor Michael McDonald demonstrates how to harness the wealth of information available on the
Internet to forecast statistics such as industry growth, GDP, and unemployment rates, as well as
factors that directly affect your business, like property prices and future interest rate hikes.
All you need is Microsoft Excel. Michael uses the built-in formulas, functions, and calculations to
perform regression analysis, calculate confidence intervals, and stress test your results. He also
covers time series exponential smoothing, fixed effects regression, and difference estimators. You'll
walk away from the course able to immediately begin creating forecasts for your own business needs.
Introduction
- Hello, I'm Dr. Michael McDonald. I'm a professor of finance and consultant to industry. I've been
teaching forecasting, using data, for more than a decade, to financial firms, Fortune 500 companies
and government agencies.
In this course, I'm going to show you how to effectively use economic data in practical business
forecasting settings.
Data analytics is one of the hottest growth areas in business and it is a key opportunity for those
looking to develop their career.
In this course I will demonstrate how to gather data from government sources, like the Federal
Reserve, and use that data to forecast critical variables, like property prices and interest rate hikes.
We'll explore a variety of different types of forecasts, from time series exponential smoothing, to fixed
effects regressions, to difference in difference estimators.
Please join me know, and let's get started.
What you should know
- Before getting started with this course, there's a few things you should know.
First of all, we'll be using Excel extensively throughout the course as a tool for making forecasts in a
variety of settings. You should be familiar with Excel. Not only should you know how to move around
Excel and navigate the tabs effectively, but it'll also be helpful if you understand how to change
decimal places in Excel, how to convert numbers to currency, how to convert numbers to
percentages, et cetera.
It'll also be helpful if you have some experience with the analysis tool pack in Excel, as we'll relying
heavily on that.
In additional to a knowledge of Excel, you'll want to have some basic knowledge of statistics. In
particular, a minimal knowledge of the script of statistics like means and medians is helpful.
It'll also be great if you have some background in regression analysis or more advance statistics as
well.
1
, In addition to statistics, you'll need some familiarity with basic accounting and finance. You don't have
to be a professional accountant, mind you. You don't have to have worked at a big bank, but if you
have an understand of what revenue is, interest rates are, what net profit means, it'll make a lot of
what I'm talking about in this course make much more sense.
Finally, and most importantly, you'll need to be hungry for this course. You'll need a hunger for
knowledge.
1. The Basics
1.1 Basics of economic analysis
Economic forecasting is complex. Most economic forecasts are built on three key parts, data,
econometrics, and judgment.
These three aspects of any forecast form the stool on which our results are built. Take away any of
the three legs and the stool falls.
Our focus is going to be on understanding how to use that data and combine it with data analysis
through econometrics and make judgements based on the results that are going to form the outcomes
for our forecasts.
In particular, we're going to be looking at two types of data, microeconomic data and macroeconomic
data. Microeconomic data is firm-level data. It's focused on data about individual
consumers, individual companies, et cetera. Microeconomic data is great for making powerful
analysis, but it's harder to gather. There are simply fewer sources of microeconomic data out
there. It's harder to gather as a result. Macroeconomic data, on the other hand, is national-level data.
Think about things like gross domestic product, unemployment rates, interest rates. These are factors
that affect everyone across the country. This kind of data is easier to gather and find, but it's harder to
analyze. In particular, if we're trying to make forecasts about GDP or unemployment or interest
rates, we're often going to face an insidious problem what we call omitted variables.
Omitted variables simply means that there's other factors that we can't take into account when we're
trying to forecast based on macroeconomic data.
Our focus today is going to be on making forecasts. In particular, we've got someone joining us. His
name is Ed. Ed is a business economist at an online rental property company. Ed is trying to forecast
housing demand and interest rates to help with his firm's expansion plans.
1.2 Sources of economic data
One of the first steps that you're going to take when starting any kind of business intelligence or data
analytics project is to gather data. All businesses out there have access to lots of data.
There's proprietary data, often customer based from a particular firm, and there's publicly available
data, such as that from the U.S. Census Bureau or the Federal Reserve. The data that you need is
going to depend on the question that you're asking and typically you're going to need to use a mix of
public and private data. So where can you get data like this?
Well there's three options. First you can buy it. For a lot of financial data out there or very customized
and specific data. Buying it is your only choice. Second, you can build your own data set. Again, if you
have access to company data that's collected on say customers, or based on specific data points
about the market you can often build a very powerful data set. And third, and perhaps most under
appreciated by much of the market today, is that you can gather data, for free.
In fact the Federal Reserve has reams of free data that's available to anyone who chooses to access
it. We're going to talk about how to gather that data in just a moment. Before we get to that you might
2
Big data is transforming the world of business. Yet many people don't understand what big data and
business intelligence are, or how to apply the techniques to their day-to-day jobs. This course
addresses that knowledge gap by showing how to use large volumes of economic data to gain key
business insights and analyze market conditions.
Professor Michael McDonald demonstrates how to harness the wealth of information available on the
Internet to forecast statistics such as industry growth, GDP, and unemployment rates, as well as
factors that directly affect your business, like property prices and future interest rate hikes.
All you need is Microsoft Excel. Michael uses the built-in formulas, functions, and calculations to
perform regression analysis, calculate confidence intervals, and stress test your results. He also
covers time series exponential smoothing, fixed effects regression, and difference estimators. You'll
walk away from the course able to immediately begin creating forecasts for your own business needs.
Introduction
- Hello, I'm Dr. Michael McDonald. I'm a professor of finance and consultant to industry. I've been
teaching forecasting, using data, for more than a decade, to financial firms, Fortune 500 companies
and government agencies.
In this course, I'm going to show you how to effectively use economic data in practical business
forecasting settings.
Data analytics is one of the hottest growth areas in business and it is a key opportunity for those
looking to develop their career.
In this course I will demonstrate how to gather data from government sources, like the Federal
Reserve, and use that data to forecast critical variables, like property prices and interest rate hikes.
We'll explore a variety of different types of forecasts, from time series exponential smoothing, to fixed
effects regressions, to difference in difference estimators.
Please join me know, and let's get started.
What you should know
- Before getting started with this course, there's a few things you should know.
First of all, we'll be using Excel extensively throughout the course as a tool for making forecasts in a
variety of settings. You should be familiar with Excel. Not only should you know how to move around
Excel and navigate the tabs effectively, but it'll also be helpful if you understand how to change
decimal places in Excel, how to convert numbers to currency, how to convert numbers to
percentages, et cetera.
It'll also be helpful if you have some experience with the analysis tool pack in Excel, as we'll relying
heavily on that.
In additional to a knowledge of Excel, you'll want to have some basic knowledge of statistics. In
particular, a minimal knowledge of the script of statistics like means and medians is helpful.
It'll also be great if you have some background in regression analysis or more advance statistics as
well.
1
, In addition to statistics, you'll need some familiarity with basic accounting and finance. You don't have
to be a professional accountant, mind you. You don't have to have worked at a big bank, but if you
have an understand of what revenue is, interest rates are, what net profit means, it'll make a lot of
what I'm talking about in this course make much more sense.
Finally, and most importantly, you'll need to be hungry for this course. You'll need a hunger for
knowledge.
1. The Basics
1.1 Basics of economic analysis
Economic forecasting is complex. Most economic forecasts are built on three key parts, data,
econometrics, and judgment.
These three aspects of any forecast form the stool on which our results are built. Take away any of
the three legs and the stool falls.
Our focus is going to be on understanding how to use that data and combine it with data analysis
through econometrics and make judgements based on the results that are going to form the outcomes
for our forecasts.
In particular, we're going to be looking at two types of data, microeconomic data and macroeconomic
data. Microeconomic data is firm-level data. It's focused on data about individual
consumers, individual companies, et cetera. Microeconomic data is great for making powerful
analysis, but it's harder to gather. There are simply fewer sources of microeconomic data out
there. It's harder to gather as a result. Macroeconomic data, on the other hand, is national-level data.
Think about things like gross domestic product, unemployment rates, interest rates. These are factors
that affect everyone across the country. This kind of data is easier to gather and find, but it's harder to
analyze. In particular, if we're trying to make forecasts about GDP or unemployment or interest
rates, we're often going to face an insidious problem what we call omitted variables.
Omitted variables simply means that there's other factors that we can't take into account when we're
trying to forecast based on macroeconomic data.
Our focus today is going to be on making forecasts. In particular, we've got someone joining us. His
name is Ed. Ed is a business economist at an online rental property company. Ed is trying to forecast
housing demand and interest rates to help with his firm's expansion plans.
1.2 Sources of economic data
One of the first steps that you're going to take when starting any kind of business intelligence or data
analytics project is to gather data. All businesses out there have access to lots of data.
There's proprietary data, often customer based from a particular firm, and there's publicly available
data, such as that from the U.S. Census Bureau or the Federal Reserve. The data that you need is
going to depend on the question that you're asking and typically you're going to need to use a mix of
public and private data. So where can you get data like this?
Well there's three options. First you can buy it. For a lot of financial data out there or very customized
and specific data. Buying it is your only choice. Second, you can build your own data set. Again, if you
have access to company data that's collected on say customers, or based on specific data points
about the market you can often build a very powerful data set. And third, and perhaps most under
appreciated by much of the market today, is that you can gather data, for free.
In fact the Federal Reserve has reams of free data that's available to anyone who chooses to access
it. We're going to talk about how to gather that data in just a moment. Before we get to that you might
2
