L1 Trend Filtering

Author and guest post by Eren Ocakverdi.

Extracting the trend of a time series is an important analytical task as it simply depicts the underlying movement of the variable of interest. Had this so-called long term component known in advance, we would have been able to foresee its future course. In practice, however, there are several other factors (e.g. cycle, noise) in play that have influence on the dynamics of a time dependent variable.

Time path of a variable can either be deterministic (assuming the change in trend is constant) or stochastic (assuming the change in trend varies randomly around a constant). Estimation of a deterministic trend is straightforward, yet it often oversimplifies the data generating process. The assumption of stochastic trend seems to be a better fit to observed behavior of various time series as they tend to evolve with abrupt changes. Nevertheless, its estimation is difficult and can have serious implications due to accumulation of past errors.

Impulse Responses by Local Projections

Author and guest post by Eren Ocakverdi.


Vector Autoregression (VAR) is a standard tool for analyzing interactions among variables and making inferences about the historical evolution of a system (e.g., an economy). When doing so, however, interpreting the estimated coefficients of the model is generally neither an easy or useful task due to complicated dynamics of VARs. As Stock and Watson (2001) aptly puts it, impulse responses are reported as a more informative statistic instead.

The Impulse Response Function (IRF) measures the reaction of the system to a shock of interest. Unfortunately, when the underlying data generating process (DGP) cannot be well approximated by a VAR(p) process, IRFs derived from the model will be biased and misleading. Jordà (2005) introduced an alternative method for computing IRFs based on local projections that do not require specification and estimation of the unknown true multivariate dynamic system itself¹.

The usual presentation of IRFs is through visualizing the dynamic propagation mechanism accompanied by error bands. In addition to marginal error bands, Jordà (2009) introduced two new sets of bands to represent uncertainty about the shape of the impulse response and to examine the individual significance of coefficients in a given trajectory. In this framework, it becomes straightforward to impose restrictions on impulse response trajectories and formally test their significance.

Add-in Round Up for 2016 Q1

In this section of the blog, we provide a summary of the Add-ins that have been released or updated within the previous few months, and we announce the winner of our “Add-in of the Quarter” prize!

As a reminder, EViews Add-ins are additions to the EViews interface or command language written by our users or the EViews Development Team and released to the public. You can install Add-ins to your EViews by using the Add-ins menu from within EViews, or by visiting our Add-ins webpage.

We have five new Add-ins within the last few months:

1. BFAVAR
2. SRVAR
3. TVSVAR
4. FORCOMB
5. TSCVAL

BFAVAR

The BFAVAR Add-in, written by Davaajargal Luvsannyam, estimates Factor Augmented Vector Auto Regression (FAVAR) models using the one-step Bayesian likelihood approach.

Add-in Round Up

In this section of the blog we provide a summary of the Add-ins that have been released or updated in the previous few months, and we announce the winner of our “Add-in of the Quarter” prize!

As a reminder, EViews Add-ins are additions to the EViews interface or command language written by our users or the EViews development team and released to the public. You can install Add-ins to your EViews by using the Add-ins menu from within EViews, or by visiting the EViews website.

The past few months have seen the release of three new Add-ins: HEGY, Backtest and FAVAR.