# Author: Eric

## The Quantile Autoregressive-Distributed Lag Parameter Estimation and Interpretation in GAUSS

The QARDL model has grown increasingly popular in time series analysis. It is a convenient model for addressing autocorrelation, disentangling long-term and short-term relationships, and addressing asymmetric relationships. In today’s blog, we look at the basics of the QARDL model including:
1. The intuition behind the QARDL model.
2. How to estimate the QARDL model in GAUSS.
3. How to interpret the QARDL results.

## 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.
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## 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.
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## 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.

## 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?
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## 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:
1. What is Granger-causality?
2. When to use Granger causality?
3. How to use Granger causality?

## 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:
2. Viewing and displaying dates in easy-to-read formats.
3. Easily changing the date format.
4. Using familiar date formats for filtering data.

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