Few weeks back I simulated a model and made the point that in practice, the difference between Bayesian and Frequentist is not large. Here I apply the code to some real data; a model for Industrial Production (IP).

# Category: Statistics and Econometrics

## Bayesian vs. Frequentist in Practice

Rivers of ink have been spilled over the ‘Bayesian vs. Frequentist’ dispute. Most of us were trained as Frequentists. Probably because the computational power needed for Bayesian analysis was not around when the syllabus of your statistical/econometric courses was formed. In this age of tablets and fast internet connection, your training does not matter much, you can easily transform between the two approaches, engaging the right webpages/communities. I will not talk about the ideological differences between the two, or which approach is more appealing and why. Larry Wasserman already gave an excellent review.

## Understanding Multicollinearity

Roughly speaking, Multicollinearity occurs when two or more regressors are highly correlated. As with heteroskedasticity, students often know what does it mean, how to detect it and are taught how to cope with it, but not *why* is it so. From Wikipedia: “In this situation (Multicollinearity) the coefficient estimates may change erratically in response to small changes in the model or the data.” The Wikipedia entry continues to discuss detection, implications and remedies. Here I try to provide the intuition.

## How Important is Variable Selection?

Very.

If you have 10 possible independent regressors, and *none of which matter*, you have a good chance to find at least one is important.

## Moving Average Representation of VAR

A vector autoregression (VAR) process can be represented in a couple of ways. The usual form is as follows:

## Quantile Autoregression in R

In the past, I wrote about robust regression. This is an important tool which handles outliers in the data. Roger Koenker is a substantial contributor in this area. His website is full of useful information and code so visit when you have time for it. The paper which drew my attention is “Quantile Autoregression” found under his research tab, it is a significant extension to the time series domain. Here you will find short demonstration for stuff you can do with quantile autoregression in R.

## On p-value

Albert Schweitzer said: “Example is not the main thing in influencing others. It is the only thing.”, so I start with it.

## Heteroskedasticity tests

Assume you have a variable *y*, which has an expectation and a variance. The expectation is often modeled using linear regression so that E(y) equals, on average, $\beta_0 +\beta_1x$. The origin of the variability in *y* is the residual. Now, standard econometric courses start with the simple notion of “constant variance”, which means that the variance of the disturbances is steady and is not related to any of the explanatory variables that were chosen to model the expectation, this is called homoskedasticity assumption. In fact, in real life it is rarely the case. Courses should start with the heteroskedasticity assumption as this is the prevalent state of the world. In almost any situation you will encounter, the variance of the dependent variable is not constant, it matters what is the *x* for which we want to determine the variance of *y*.

## A Simple Model for Realized Volatility

The post has two goals:

**(1)** Explain how to forecast volatility using a simple Heterogeneous Auto-Regressive (HAR) model. (Corsi, 2002)

**(2)** Check if higher moments like Skewness and Kurtosis add forecast value to this model.

## Intraday volatility measures

In the last few decades there has been tremendous progress in the realm of volatility estimation. A major step is the additional use of intraday price path. It has been shown that estimates which consider intraday information are more accurate. Which is to say they converge faster to the real unobserved value of the true volatility.

## Information Criteria for Autoregression

Some knowledge about the bootstrapping procedure is assumed.

In time series analysis, Information Criteria can be found under every green tree. These are function to help you determine when to stop adding explanatory variables to your model.

## Bootstrapping time series – R code

Bootstrapping in its general form (“ordinary” bootstrap) relies on IID observations which staples the theory backing it. However, time series are a different animal and bootstrapping time series requires somewhat different procedure to preserve dependency structure.

## Forecasting the Eurozone Misery index

Is Miss Stagflation coming to visit?

The Misery index is the sum of inflation and unemployment rate. We would like them both to stay naturally low, and we are miserable when they are not. The index is currently floating in it’s record scratching levels. In this post I demonstrate the use of the nice *FitAR* package in R to fit an AR model and see what we can expect accordingly. Inflation and unemployment numbers concerning the Eurozone (17 countries) can be found here.

Have a look at the index over time:

## Stock market Kurtosis over time

In the last decade we have observed an increase in computational power, information availability, speed of execution and stock market competition in general. One might think that, as a result, we are prone to larger shocks that occur faster than what was common in the past. I crunched some numbers and was surprised to see that this is not the case.

## Kurtosis Interpretation

When you google “Kurtosis”, you encounter many formulas to help you calculate it, talk about how this measure is used to evaluate the “peakedness” of your data, maybe some other measures to help you do so, maybe all of a sudden a side step towards Skewness, and how both Skewness and Kurtosis are *higher moments* of the distribution. This is all very true, but maybe you just want to understand what does Kurtosis mean and how to interpret this measure. Similarly to the way you interpret standard deviation (the average distance from the average). Here I take a shot at giving a more intuitive interpretation.