Eric | Aptech | Page 4

Sep 29 by Eric Posted in Econometrics, Time Series

The Structural VAR Model at Work: Analyzing Monetary Policy

In today’s blog, we put the building blocks of the structural vector autoregressive (SVAR) model to work in a practical application. We’ll use one of the most common applications of SVAR models, monetary policy analysis, to see the SVAR in action. After this blog, you should have a stronger understanding of:

How to use Granger causality testing to inform model selection.
How to implement short-run identification restrictions.
How to conduct and interpret structural VAR analysis.

Tagged in SVAR

Sep 24 by Eric Posted in Econometrics, Time Series, Video

How to Run the Fourier LM Test (Video)

Learn everything you need to know to run the Fourier LM unit root test with your data and interpret the results.

Tagged in structural breaks, unit root

Sep 03 by Eric Posted in Time Series

Introduction to Markov-Switching Models

Markov-switching models offer a powerful tool for capturing the real-world behavior of time series data. Today’s blog provides an introduction to Markov-switching models including:

What a regime switching model is and how it differs from a structural break model.
When we should use the regime switching model.
What a Markov-switching model is.
What tools we use to estimate Markov-switching models.

Tagged in regime-switching, structural breaks

Aug 20 by Eric Posted in Econometrics, Programming

Introduction to Handling Missing Values

Handling missing values is an important step in data cleaning that can impact model validity and reliability. Despite this, it can be difficult to find examples and resources about how to deal with missing values. This blog helps to fill that void and covers:

Types of missing values.
Dealing with missing values.
Missing values in practice.

Aug 02 by Eric Posted in Econometrics, Time Series

Understanding and Solving the Structural Vector Autoregressive Identification Problem

The structural vector autoregressive model is a crucial time series model used to understand and predict economic impacts and outcomes. In this blog, we look closely at the identification problem posed by structural vector autoregressive models and its solution. In particular, we cover:

What is the structural VAR model and what is the reduced form VAR?
What is the relationship between structural VAR and reduced form VAR models?
What is the structural VAR identification problem?
What are common solutions to the structural VAR identification problem?

Tagged in vector autoregression

Jun 29 by Eric Posted in Econometrics, Time Series

Introduction to Granger Causality

Multivariate time series analysis turns to VAR models not only for understanding the relationships between variables but also for forecasting. In today’s blog, we look at how to improve VAR model selection and achieve better forecasts using Granger-causality. We explore the questions:

What is Granger-causality?
When to use Granger causality?
How to use Granger causality?

Jun 18 by Eric Posted in Econometrics, Programming, Time Series

Dates and Times Made Easy

Working with dates in data analysis software can be tedious and error-prone. The new GAUSS date type, introduced in GAUSS 21, can save you time and prevent frustration and errors. The date data type is part of the GAUSS dataframe alongside the category, string, and numeric type. In this blog, we will explore the advantages the date type has to offer, including:

Loading and viewing dates side-by-side with other data types.
Viewing and displaying dates in easy-to-read formats.
Easily changing the date format.
Using familiar date formats for filtering data.

Jun 14 by Eric Posted in Econometrics, Time Series, Video

How to Run the Maki Cointegration Test (Video)

Learn everything you need to know to run the Maki cointegration test with your data and interpret the results in this new GAUSS video.

May 21 by Eric Posted in Programming

What is a GAUSS Dataframe and Why Should You Care?

The GAUSS dataframe, introduced in GAUSS 21, is a powerful tool for storing data. In today’s blog, we explain what a GAUSS dataframe is and discuss the advantages of making it a part of your everyday GAUSS use.

May 06 by Eric Posted in Econometrics, Time Series

The Intuition Behind Impulse Response Functions and Forecast Error Variance Decomposition

This blog provides a non-technical look at impulse response functions and forecast error variance decomposition, both integral parts of vector autoregressive models. If you’re looking to gain a better understanding of these important multivariate time series techniques you’re in the right place. We cover the basics, including:

What is structural analysis?
What are impulse response functions?
How do we interpret impulse response functions?
What is forecast error variance decomposition?
How do we interpret forecast error variance decomposition?

Author: Eric