nep-ets New Economics Papers
on Econometric Time Series
Issue of 2021‒05‒10
five papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Spectral decomposition of the information about latent variables in dynamic macroeconomic models By Nikolay Iskrev
  2. Estimation of High Dimensional Vector Autoregression via Sparse Precision Matrix By Template-Type: ReDIF-Paper 1.0; Benjamin Poignard; Manabu Asai
  3. Frequency causality measures and Vector AutoRegressive (VAR) models: An improved subset selection method suited to parsimonious systems By Christophe Chorro; Emmanuelle Jay; Philippe De Peretti; Thibault Soler
  4. An Eviews program to perform the fractional Dickey-Fuller test By Bensalma, Ahmed
  5. Economic predictions with big data: the illusion of sparsity By Giannone, Domenico; Lenza, Michele; Primiceri, Giorgio E.

  1. By: Nikolay Iskrev
    Abstract: In this paper, I show how to perform spectral decomposition of the information about latent variables in dynamic economic models. A model describes the joint probability distribution of a set of observed and latent variables. The amount of information transferred from the former to the latter is measured by the reduction of uncertainty in the posterior compared to the prior distribution of any given latent variable. Casting the analysis in the frequency domain allows decomposing the total amount of information in terms of frequency-specific contributions as well as in terms of information contributed by individual observed variables. I illustrate the usefulness of the proposed methodology with applications to two DSGE models taken from the literature.
    JEL: C32 C51 C52 E32
    Date: 2021
  2. By: Template-Type: ReDIF-Paper 1.0; Benjamin Poignard (Graduate School of Economics, Osaka University); Manabu Asai (Faculty of Economics, Soka University)
    Abstract: We consider the problem of estimating sparse structural vector autoregression (SVAR) processes via penalized precision matrix. Such matrix is the output of the underlying directed acyclic graph of the SVAR process, whose zero components correspond to zero SVAR coecients. The precision matrix estimators are deduced from the class of Bregman divergences and regularized by the SCAD, MCP and LASSO penalties. Under suitable regularity conditions, we derive error bounds for the regularized precision matrix for each Bregman divergence. Moreover, we establish the support recovery property, including the case when the penalty is non-convex. These theoretical results are supported by empirical studies.
    Keywords: sparse structural vector autoregression; statistical consistency; support recovery.
  3. By: Christophe Chorro (Centre d'Economie de la Sorbonne, Université Paris 1 Panthéon-Sorbonne); Emmanuelle Jay (Fidéas Capital Quanted & Europlace Institute of Finance); Philippe De Peretti (Centre d'Economie de la Sorbonne, Université Paris 1 Panthéon-Sorbonne); Thibault Soler (Fidéas Capital and Centre d'Economie de la Sorbonne)
    Abstract: Finding causal relationships in large dimensional systems is of key importance in a number of fields. Granger non-causality tests have become standard tools, but they only detect the direction of the causality, not its strength. To overcome this point, in the frequency domain, several measures have been introduced such as the Direct Transfer Function (DTF), the Partial Directed Coherence measure (PDC) or the Generalized Partial Directed Coherence measure (GPDC). Since these measures are based on a two-step estimation, consisting in i) estimating a Vector AutoRegressive (VAR) in the time domain and ii) using the VAR coefficients to compute measures in the frequency domain, they may suffer from cascading errors. Indeed, a flawed VAR estimation will translate into large biases in coherence measures. Our goal in this paper is twofold. First, using Monte Carlo simulations, we quantify these biases. We show that the two-step procedure results in highly inaccurate coherence measures, mostly due to the fact that non-significant coefficients are kept, especially in parsimonious systems. Based on this idea, we next propose a new methodology (mBTS-TD) based on VAR reduction procedures, combining the modified-Backward-in-Time selection method (mBTS) and the Top-Down strategy (TD). We show that our mBTS-TD method outperforms the classical two-step procedure. At last, we apply our new approach to recover the topology of a weighted financial network in order to identify through the local directed weighted clustering coefficient the most systemic assets and exclude them from the investment universe before allocating the portfolio to improve the return/risk ratio
    Keywords: VAR model; subset selection methods; frequency causality measures; weighted financial networks; portfolio allocation
    JEL: C5 G11
    Date: 2021–04
  4. By: Bensalma, Ahmed
    Abstract: This paper demonstrates how sequential fractional Dickey-Fuller (FDF in short) test can be implemented in EViews. We first briefly introduce how to use the fracdiff an EViews add-in to compute the fractional difference of the Nile data. Next, we give the program that executes the sequential FDF testing on the Nile data series.
    Keywords: ARFIMA; Dickey-Fuller test; Fractional Dickey-Fuller test; fractional integration parameter; type II fractional Brownian motion, Fracdiff, EViews add-in
    JEL: C1 C12 C18 C46
    Date: 2021–04–27
  5. By: Giannone, Domenico; Lenza, Michele; Primiceri, Giorgio E.
    Abstract: We compare sparse and dense representations of predictive models in macroeconomics, microeconomics and finance. To deal with a large number of possible predictors, we specify a prior that allows for both variable selection and shrinkage. The posterior distribution does not typically concentrate on a single sparse model, but on a wide set of models that often include many predictors. JEL Classification: C11, C52, C53, C55
    Keywords: curse of dimensionality, model uncertainty, shrinkage, variable selection
    Date: 2021–04

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