Showing posts with label Econometrics. Show all posts
Showing posts with label Econometrics. Show all posts

R: A Simple Replication of Cointegration Test Results

This post is a straightforward replication of the Johansen cointegration test results from Johansen and Juselius (1990) using R urca package.

Equivalence of VAR models between original variables and their linear transformations

This post demonstrates the VAR forecasting equivalence between original variables and their linear transformations by examining a simple example.

R: The Hodrick-Prescott filter or HP filter

This post demonstrates how to apply the Hodrick-Prescott (HP) filter in R by using the hpfilter R library.

R: Beveridge–Nelson Trend and Cycle Decomposition

This post shows how to extract trend and cyclical components from a univariate time series based on the Beveridge–Nelson (BN) decomposition using tsm R package.

R : Diebold-Mariano Test

This post shows how to use Diebold-Mariano test by using multDM R package.

R : Nyholm (2018) Rotated Nelson-Siegel Model

This post implements R code to estimate the Rotated Nelson-Siegel yield curve model of Nyholm (2018)

R : Nelson-Siegel-Svensson (NSS) model with fixed or estimated constant decay parameters

This post implements the period-by-period estimation of the Nelson-Siegel-Svensson yield curve model with fixed or estimated constant decay parameters using R code.

R : VAR Impulse Response Function

This post draws the orthogonal impulse response functions from an estimated VAR model with 1 standard deviation or 1 unit shock. vars R package does not provide 1 unit shock so I implemented it using the Cholesky decomposition.

Python: The Hodrick-Prescott filter or HP filter

This post shows how to extract trend and cyclical components from a univariate time series using the Hodrick-Prescott (HP) filter. A more realistic version is the one-sided HP filter since it uses only the information available at time t, not the full sample.

R code: Understanding Dynamic Conditional Correlation (DCC) model

This post explains the structure of DCC (dynamic conditional correlation) model of Engle (2002) and then, uses rmgarch R package for estimating DCC model. This R package also provides various extended versions of DCC model.