nep-ets New Economics Papers
on Econometric Time Series
Issue of 2016‒11‒13
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Use of unit root methods in early warning of financial crises By Virtanen, Timo; Tölö, Eero; Virén, Matti; Taipalus, Katja
  2. Forecasting stock market returns by summing the frequency-decomposed parts By Gonçalo Faria; Fabio Verona
  3. Factor augmented VAR revisited - A sparse dynamic factor model approach By Simon Beyeler; Sylvia Kaufmann
  4. A Simple Test for Causality in Volatility By Chia-Lin Chang; Michael McAleer
  5. Nonparametric Estimation of Dynamic Discrete Choice Models for Time Series Data By Byeong U. Park; Leopold Simar; Valentin Zelenyuk
  6. Adaptive state space models with applications to the business cycle and financial stress By Delle Monache, Davide; Petrella, Ivan; Venditti, Fabrizio

  1. By: Virtanen, Timo; Tölö, Eero; Virén, Matti; Taipalus, Katja
    Abstract: Unit root methods have long been used in detection of financial bubbles in asset prices. The basic idea is that fundamental changes in the autocorrelation structure of relevant time series imply the presence of a rational price bubble. We provide cross-country evidence for performance of unit-root-based early warning systems in ex-ante prediction of financial crises in 15 EU countries over the past three decades. We then combine the identified early warning signals from multiple time series into a composite indicator. We also show that a mix of data with different frequencies may be useful in providing timely warning signals. Our results suggest and an early warning tool based on unit root methods provides be a valuable accessory in financial stability supervision.
    Keywords: financial crises, unit root, combination of forecasts
    JEL: G01 G14 G21
    Date: 2016–11–03
  2. By: Gonçalo Faria (Católica Porto Business School and CEGE, Universidade Católica Portuguesa); Fabio Verona (Bank of Finland and CEF.UP)
    Abstract: We forecast stock market returns by applying, within a Ferreira and Santa-Clara (2011) sum-of-the-parts framework, a frequency decomposition of several predictors of stock returns. The method delivers statistically and economically significant improvements over historical mean forecasts, with monthly out- of-sample R2 of 3.27% and annual utility gains of 403 basis points. The strong performance of this method comes from its ability to isolate the frequencies of the predictors with the highest predictive power from the noisy parts, and from the fact that the frequency-decomposed predictors carry complementary information that captures both the long-term trend and the higher frequency movements of stock market returns.
    Keywords: predictability, stock returns, equity premium, asset allocation, frequency domain, wavelets
    JEL: G11 G12 G14 G17
    Date: 2016–10
  3. By: Simon Beyeler (Study Center Gerzensee and University of Bern); Sylvia Kaufmann (Study Center Gerzensee)
    Abstract: We combine the factor augmented VAR framework with recently developed estimation and identification procedures for sparse dynamic factor models. Working with a sparse hierarchical prior distribution allows us to discriminate between zero and non-zero factor loadings. The non-zero loadings identify the unobserved factors and provide a meaningful economic interpretation for them. Given that we work with a general covariance matrix of factor innovations, we can implement different strategies for structural shock identification. Applying our methodology to US macroeconomic data (FRED QD) reveals indeed a high degree of sparsity in the data. The proposed identification procedure yields seven unobserved factors that account for about 52 percent of the variation in the data. We simultaneously identify a monetary policy, a productivity and a news shock by recursive ordering and by applying the method of maximizing the forecast error variance share in a specific variable. Factors and specific variables show sensible responses to the identified shocks.
    Date: 2016–10
  4. By: Chia-Lin Chang (National Chung Hsing University, Taiwan); Michael McAleer (National Tsing Hua University, Taiwan; Erasmus School of Economics, Erasmus University Rotterdam, The Netherlands, Complutense University of Madrid, Spain, Yokohama National University, Japan)
    Abstract: An early development in testing for causality (technically, Granger non-causality) in the conditional variance (or volatility) associated with financial returns, was the portmanteau statistic for non-causality in variance of Cheng and Ng (1996). A subsequent development was the Lagrange Multiplier (LM) test of non-causality in the conditional variance by Hafner and Herwartz (2006), who provided simulations results to show that their LM test was more powerful than the portmanteau statistic. While the LM test for causality proposed by Hafner and Herwartz (2006) is an interesting and useful development, it is nonetheless arbitrary. In particular, the specification on which the LM test is based does not rely on an underlying stochastic process, so that the alternative hypothesis is also arbitrary, which can affect the power of the test. The purpose of the paper is to derive a simple test for causality in volatility that provides regularity conditions arising from the underlying stochastic process, namely a random coefficient autoregressive process, and for which the (quasi-) maximum likelihood estimates have valid asymptotic properties. The simple test is intuitively appealing as it is based on an underlying stochastic process, is sympathetic to Granger’s (1969, 1988) notion of time series predictability, is easy to implement, and has a regularity condition that is not available in the LM test.
    Keywords: Random coefficient stochastic process; Simple test; Granger non-causality; Regularity conditions; Asymptotic properties; Conditional volatility
    JEL: C22 C32 C52 C58
    Date: 2016–11–07
  5. By: Byeong U. Park (Department of Statistics, Seoul National University); Leopold Simar (Inst. de Statistique, Biostatistique et Sciences Actuarielles, Universite Catholique de Louvain); Valentin Zelenyuk (School of Economics, The University of Queensland)
    Abstract: The non-parametric quasi-likelihood method is generalized to the context of discrete choice models for time series data where dynamics is modelled via lags of the discrete dependent variable appearing among regressors. Consistency and asymptotic normality of the estimator for such models in the general case is derived under the assumption of stationarity with strong mixing condition. Monte Carlo examples are used to illustrate performance of the proposed estimator relative to the fully parametric approach. Possible applications for the proposed estimator may include modelling and forecasting of probabilities of whether a subject would get a positive response to a treatment, whether in the next period an economy would enter a recession, or whether a stock market will go down or up, etc.
    Keywords: Nonparametric, Dynamic Discrete Choice, Probit
    JEL: C14 C22 C25 C44
    Date: 2016–10
  6. By: Delle Monache, Davide; Petrella, Ivan; Venditti, Fabrizio
    Abstract: In this paper we develop a new theoretical framework for the analysis of state space models with time-varying parameters. We let the driver of the time variation be the score of the predictive likelihood and derive a new filter that allows us to estimate simultaneously the state vector and the time-varying parameters. In this setup the model remains Gaussian, the likelihood function can be evaluated using the Kalman filter and the model parameters can be estimated via maximum likelihood, without requiring the use of computationally intensive methods. Using a Monte Carlo exercise we show that the proposed method works well for a number of different data generating processes. We also present two empirical applications. In the former we improve the measurement of GDP growth by combining alternative noisy measures, in the latter we construct an index of financial stress and evaluate its usefulness in nowcasting GDP growth in real time. Given that a variety of time series models have a state space representation, the proposed methodology is of wide interest in econometrics and statistics.
    Keywords: Business cycle; financial stress.; score-driven models; State space models; time-varying parameters
    JEL: C22 C32 C51 C53 E31
    Date: 2016–11

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