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Time Series Analysis Part 1: Regression with a Twist

Time Series Analysis Part 1: Regression with a Twist

We’re surrounded by time series. It’s one of the more common plots we see in day-to-day life. Finance and economics are full of them – stock prices, GDP over time, and 401K value over time to name a few. The plot looks deceptively simple; just a nice univariate squiggle. No crazy vectors, no surfaces, just one predictor – time. It turns out time is a tricky and fickle explanatory variable, which makes analysis of time series a bit more nuanced than first glance. This nuance is obscured by the ease of automatic implementation of time series modeling in languages like R1 As nice as this is for practitioners, the mathematics behind this analysis is lost. Ignoring the mathematics can lead to improper use of these tools. This series will examine some of the mathematics behind stationarity and what is known as ARIMA (Auto-Regressive Integrated Moving Average) modeling. Part 1 will examine the very basics, showing that time series modeling is really just regression with a twist.

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The Central Limit Theorem isn’t a Statistical Silver Bullet

The Central Limit Theorem isn’t a Statistical Silver Bullet

Chances are, if you took anything away from that high school or college statistics class you were dragged into, you remember some vague notion about the Central Limit Theorem. It’s likely the most famous theorem in statistics, and the most widely used. Most introductory statistics textbooks state the theorem in broad terms, that as the sample size increases, the sample distribution of the sum of the sample elements will be approximately normally distributed, regardless of the underlying distribution. Many things used in statistical inference as justification in a broad variety of fields, such as the classical z-test, rely on this theorem. Many conclusions in science, economics, public policy, and social studies have been drawn with tests that rely on the Central Limit Theorem as justification. We’re going to dive into this theorem a bit more formally, and discuss some counterexamples to this theorem. Not every sequence of random variables will obey the conditions of theorem, and the assumptions are a bit more strict than are used in practice.

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