nep-ets New Economics Papers
on Econometric Time Series
Issue of 2022‒10‒31
five papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. The Unit-effect Normalisation in Set-identified Structural Vector Autoregressions By Matthew Read
  2. Multiscale Comparison of Nonparametric Trend Curves By Marina Khismatullina; Michael Vogt
  3. Bayesian Modeling of Time-varying Parameters Using Regression Trees By Niko Hauzenberger; Florian Huber; Gary Koop; James Mitchell
  4. Statistical inference for rough volatility: Central limit theorems By Carsten Chong; Marc Hoffmann; Yanghui Liu; Mathieu Rosenbaum; Gr\'egoire Szymanski
  5. Detecting asset price bubbles using deep learning By Francesca Biagini; Lukas Gonon; Andrea Mazzon; Thilo Meyer-Brandis

  1. By: Matthew Read (Reserve Bank of Australia)
    Abstract: Structural vector autoregressions that are set identified (e.g. with sign restrictions) are typically used to analyse the effects of standard deviation shocks. However, answering questions of economic interest often requires knowing the effects of a 'unit' shock. For example, central bankers want to answer questions like 'what are the effects of a 100 basis point increase in the policy rate?' The problem is that set-identifying restrictions do not always rule out the possibility that a variable does not react contemporaneously to its own shock. As a consequence, identified sets for the impulse responses to unit shocks may be unbounded, which implies that set-identifying restrictions may be extremely uninformative. Simply assuming that responses are non-zero turns out to be an arbitrary and unsatisfactory solution. I argue that it is therefore important to communicate about the extent to which the identified set may be unbounded, since this tells us about the informativeness of the identifying restrictions, and I develop tools to facilitate this. I explain how to draw useful posterior inferences about impulse responses when identified sets are unbounded with positive probability. I illustrate the empirical relevance of these issues by estimating the response of US output to a 100 basis point federal funds rate shock under different sets of identifying restrictions. Some restrictions are very uninformative about the effects of a 100 basis point shock. The output responses I obtain under a rich set of identifying restrictions lie towards the smaller end of the range of existing estimates.
    Keywords: Bayesian inference; impulse responses; monetary policy; set-identified models; sign restrictions; zero restrictions
    JEL: C32 E52
    Date: 2022–10
  2. By: Marina Khismatullina; Michael Vogt
    Abstract: We develop new econometric methods for the comparison of nonparametric time trends. In many applications, practitioners are interested in whether the observed time series all have the same time trend. Moreover, they would often like to know which trends are different and in which time intervals they differ. We design a multiscale test to formally approach these questions. Specifically, we develop a test which allows to make rigorous confidence statements about which time trends are different and where (that is, in which time intervals) they differ. Based on our multiscale test, we further develop a clustering algorithm which allows to cluster the observed time series into groups with the same trend. We derive asymptotic theory for our test and clustering methods. The theory is complemented by a simulation study and two applications to GDP growth data and house pricing data.
    Date: 2022–09
  3. By: Niko Hauzenberger; Florian Huber; Gary Koop; James Mitchell
    Abstract: In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian Additive Regression Trees (BART). The novelty of this model arises from the law of motion driving the parameters being treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. In contrast to other nonparametric and machine learning methods that are black box, inference using our model is straightforward because, in treating the parameters rather than the variables nonparametrically, the model remains conditionally linear in the mean. Parsimony is achieved through adopting nonparametric factor structures and use of shrinkage priors. In an application to US macroeconomic data, we illustrate the use of our model in tracking both the evolving nature of the Phillips curve and how the effects of business cycle shocks on inflationary measures vary nonlinearly with movements in uncertainty.
    Date: 2022–09
  4. By: Carsten Chong; Marc Hoffmann; Yanghui Liu; Mathieu Rosenbaum; Gr\'egoire Szymanski
    Abstract: In recent years, there has been substantive empirical evidence that stochastic volatility is rough. In other words, the local behavior of stochastic volatility is much more irregular than semimartingales and resembles that of a fractional Brownian motion with Hurst parameter $H
    Date: 2022–10
  5. By: Francesca Biagini; Lukas Gonon; Andrea Mazzon; Thilo Meyer-Brandis
    Abstract: In this paper we employ deep learning techniques to detect financial asset bubbles by using observed call option prices. The proposed algorithm is widely applicable and model-independent. We test the accuracy of our methodology in numerical experiments within a wide range of models and apply it to market data of tech stocks in order to assess if asset price bubbles are present. In addition, we provide a theoretical foundation of our approach in the framework of local volatility models. To this purpose, we give a new necessary and sufficient condition for a process with time-dependent local volatility function to be a strict local martingale.
    Date: 2022–10

This nep-ets issue is ©2022 by Jaqueson K. Galimberti. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.