nep-ets New Economics Papers
on Econometric Time Series
Issue of 2017‒12‒18
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. An endogenous regime-switching model of ordered choice with an application to federal funds rate target. By Andrei A. Sirchenko
  2. Forecasting realized volatility: a review By Bucci, Andrea
  3. Mixed Models as an Alternative to Farima By Jos\'e Igor Morlanes
  4. On the consistency of the two-step estimates of the MS-DFM: a Monte Carlo study By Catherine Doz; Anna Petronevich

  1. By: Andrei A. Sirchenko
    Abstract: This paper introduces a class of ordered probit models with endogenous switching among N latent regimes and possibly endogenous explanatory variables. The paper contributes to and bridges two strands of microeconometric literature. First, it extends endogenous switching regressions to models of ordered choice with N unknown regimes. Second, it generalizes the existing zero-inflated ordered probit models to make them suitable for ordinal data that take on negative, zero and positive values and characterized by abundant and heterogeneous zero observations. From a macroeconomic perspective, it is the first attempt to implement regime switching and accommodate endogenous regressors in discrete-choice monetary policy rules. Recurring oscillating regime switches in the three regimes evolving endogenously in response to the state of economy are detected during a relatively stable policy period such as the Greenspan era. The Monte Carlo experiments and an application to the federal funds rate target demonstrate that ignoring endogeneity and regime-switching environment can lead to seriously distorted statistical inference. In the simulations, the new models perform well in small samples. In the application, they not only have better in-sample fit for the Greenspan era than the existing models but also forecast better out of sample for the entire Bernanke era, correctly predicting 91 percent of policy decisions.
    JEL: C34 C35 C36 E52
    Date: 2017–11–19
  2. By: Bucci, Andrea
    Abstract: Modeling financial volatility is an important part of empirical finance. This paper provides a literature review of the most relevant volatility models, with a particular focus on forecasting models. We firstly discuss the empirical foundations of different kinds of volatility. The paper, then, analyses the non-parametric measure of volatility, named realized variance, and its empirical applications. A wide range of realized volatility models, both univariate and multivariate, is presented, such as time series models, MIDAS and GARCH-MIDAS models, Realized GARCH, and HEAVY models. We further discuss forecasting evaluation methods specifically suited for volatility models.
    Keywords: Realized Volatility; Stochastic Volatility; Volatility Models
    JEL: C22 C53 G10
    Date: 2017–12
  3. By: Jos\'e Igor Morlanes
    Abstract: We construct a new process using a fractional Brownian motion and a fractional Ornstein-Uhlenbeck process of the Second Kind as building blocks. We consider the increments of the new process in discrete time and, as a result, we obtain a more parsimonious process with similar autocovariance structure to that of a FARIMA. In practice, variance of the new increment process is a closed-form expression easier to compute than that of FARIMA.
    Date: 2017–12
  4. By: Catherine Doz (PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Panthéon-Sorbonne - ENS Paris - École normale supérieure - Paris - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics); Anna Petronevich (PSE - Paris School of Economics, CREST - Centre de Recherche en Economie et Statistique [Bruz] - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz])
    Abstract: The Markov-Switching Dynamic Factor Model (MS-DFM) has been used in different applications, notably in the business cycle analysis. When the cross-sectional dimension of data is high, the Maximum Likelihood estimation becomes unfeasible due to the excessive number of parameters. In this case, the MS-DFM can be estimated in two steps, which means that in the first step the common factor is extracted from a database of indicators, and in the second step the Markov-Switching autoregressive model is fit to this extracted factor. The validity of the two-step method is conventionally accepted, although the asymptotic properties of the two-step estimates have not been studied yet. In this paper we examine their consistency as well as the small-sample behavior with the help of Monte Carlo simulations. Our results indicate that the two-step estimates are consistent when the number of cross-section series and time observations is large, however, as expected, the estimates and their standard errors tend to be biased in small samples.
    Keywords: Markov-switching, Dynamic Factor models, two-step estimation,small-sample performance, consistency, Monte Carlo simulations
    Date: 2017–09

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