nep-ets New Economics Papers
on Econometric Time Series
Issue of 2017‒04‒09
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Intuitive and Reliable Estimates of the Output Gap from a Beveridge-Nelson Filter By Güneş Kamber; James Morley; Benjamin Wong
  2. Jump-Preserving Varying-Coefficient Models for Nonlinear Time Series By Cizek, Pavel; Koo, Chao
  3. Balanced bootstrap joint confidence bands for structural impulse response functions By Stefan Bruder; Michael Wolf
  5. Shock Restricted Structural Vector-Autoregressions By Sydney C. Ludvigson; Sai Ma; Serena Ng
  6. Testing for multiple level shifts in I(0) and I(1) stochastic processes By Josep Lluís Carrion-i-Silvestre; Maria Dolores Gadea
  7. Joint tests of contagion with applications to financial crises By Renee Fry-McKibbin; Cody Yu-Ling Hsiao; Vance L. Martin

  1. By: Güneş Kamber; James Morley; Benjamin Wong (Reserve Bank of New Zealand)
    Abstract: The Beveridge-Nelson (BN) trend-cycle decomposition based on autoregressive forecasting models of U.S. quarterly real GDP growth produces estimates of the output gap that are strongly at odds with widely-held beliefs about the amplitude, persistence, and even sign of transitory movements in economic activity. These antithetical attributes are related to the autoregressive coefficient estimates implying a very high signal-to-noise ratio in terms of the variance of trend shocks as a fraction of the overall quarterly forecast error variance. When we impose a lower signal-to-noise ratio, the resulting BN decomposition, which we label the "BN filter", produces a more intuitive estimate of the output gap that is large in amplitude, highly persistent, and typically increases in expansions and decreases in recessions. Real-time estimates from the BN filter are also reliable in the sense that they are subject to smaller revisions and predict future output growth and inflation better than estimates from other methods of trend-cycle decomposition that also impose a low signal-to-noise ratio, including deterministic detrending, the Hodrick-Prescott filter, and the bandpass filter.
    Date: 2017–01
  2. By: Cizek, Pavel (Tilburg University, Center For Economic Research); Koo, Chao (Tilburg University, Center For Economic Research)
    Abstract: An important and widely used class of semiparametric models is formed by the varyingcoefficient models. Although the varying coefficients are traditionally assumed to be smooth functions, the varying-coefficient model is considered here with the coefficient functions containing a finite set of discontinuities. Contrary to the existing nonparametric and varying-coefficient estimation of piecewise smooth functions, the varying-coefficient models are considered here under dependence and are applicable in time series with heteroscedastic and serially correlated errors. Additionally, the conditional error variance is allowed to exhibit discontinuities at a finite set of points too. The (uniform) consistency and asymptotic normality of the proposed estimators are established and the finite-sample performance is tested via a simulation study.
    Keywords: change point; Heteroscedasticity; local linear fitting; nonlinear time series; varying-coefficient models
    JEL: C13 C14 C22
    Date: 2017
  3. By: Stefan Bruder; Michael Wolf
    Abstract: Constructing joint confidence bands for structural impulse response functions based on a VAR model is a difficult task because of the non-linear nature of such functions. We propose new joint confidence bands that cover the entire true structural impulse response function up to a chosen maximum horizon with a prespecified probability (1 - α), at least asymptotically. Such bands are based on a certain bootstrap procedure from the multiple-testing literature. We compare the finite-sample properties of our method with those of existing methods via extensive Monte Carlo simulations. We also investigate the effect of endogenizing the lag order in our bootstrap procedure on the finite-sample properties. Furthermore, an empirical application to a real data set is provided.
    Keywords: Bootstrap, impulse response functions, joint confidence bands, vector autoregressive process
    JEL: C12 C32
    Date: 2017–03
    Abstract: This paper proposes spatial autoregressive conditional heteroscedasticity (S-ARCH) models to estimate spatial volatility in spatial data. S-ARCH model is a spatial extension of time series ARCH model. S-ARCH models specify conditional variances as the variances given the values of surrounding observations in spatial data, which is regarded as a spatial extension of time series ARCH models that specify conditional variances as the variances given the values of past observations. We consider parameter estimation for S-ARCH models by maximum likelihood method and propose test statistics for ARCH effects in spatial data. We demonstrate the empirical properties by simulation studies and real data analysis of land price data in Tokyo.
    Date: 2016–04–26
  5. By: Sydney C. Ludvigson; Sai Ma; Serena Ng
    Abstract: Identifying assumptions need to be imposed on autoregressive models before they can be used to analyze the dynamic effects of economically interesting shocks. Often, the assumptions are only rich enough to identify a set of solutions. This paper considers two types of restrictions on the structural shocks that can help reduce the number of plausible solutions. The first is imposed on the sign and magnitude of the shocks during unusual episodes in history. The second restricts the correlation between the shocks and components of variables external to the autoregressive model. These non-linear inequality constraints can be used in conjunction with zero and sign restrictions that are already widely used in the literature. The effectiveness of our constraints are illustrated using two applications of the oil market and Monte Carlo experiments calibrated to study the role of uncertainty shocks in economic fluctuations.
    JEL: C01 C5 C51 E17
    Date: 2017–03
  6. By: Josep Lluís Carrion-i-Silvestre; Maria Dolores Gadea
    Abstract: The paper analyzes the detection and estimation of multiple level shifts regardless of the order of integration of the time series. We show that it is possible to extend the sequential testing procedure of Bai and Perron (1998) to the I(1) non-stationary case so that a unified framework based on this approach can be applied. The performance of the test statistic is carried out, establishing a comparison with other existing proposals in the literature.Developing of a sup test statistic for detecting multiple leve shifts for I(1) processes.Simulations are reported on the finite sample performance of the statistic. Further details in the uploaded paper.
    Keywords: No empirical application in the paper., Macroeconometric modeling, Modeling: new developments
    Date: 2015–07–01
  7. By: Renee Fry-McKibbin; Cody Yu-Ling Hsiao; Vance L. Martin
    Abstract: Joint tests of contagion are derived which are designed to have power where contagion operates simultaneously through coskewness, cokurtosis and covolatility. Finite sample properties of the new tests are evaluated and compared with existing tests of contagion that focus on a single channel. Applying the tests to daily Eurozone equity returns from 2005 to 2014 shows that contagion operates through higher order moment channels during the GFC and the European debt crisis, which are not necessarily detected by traditional tests based on correlations.
    Keywords: Coskewness, Cokurtosis, Covolatility, Lagrange multiplier tests, European debt crisis, equity markets
    JEL: C1 F3
    Date: 2017–03

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