nep-ets New Economics Papers
on Econometric Time Series
Issue of 2017‒05‒14
ten papers chosen by
Yong Yin
SUNY at Buffalo

  1. GDP Trend-cycle Decompositions Using State-level Data By Manuel Gonzalez-Astudillo
  2. Autoregressive Wild Bootstrap Inference for Nonparametric Trends By Friedrich, Marina; Smeekes, Stephan; Urbain, Jean-Pierre
  3. A note on the Nelson Cao inequality constraints in the GJR-GARCH model: Is there a leverage effect? By Stavros Stavroyiannis
  4. Stochastic modelling of non-stationary financial assets By Joana Estevens; Paulo Rocha; Joao Boto; Pedro Lind
  5. Uncertainty and Forecasts of U.S. Recessions By Christian Pierdzioch; Rangan Gupta
  6. Likelihood inference and the role of initial conditions for the dynamic panel data model By Jose Diogo Barbosa; Marcelo Moreira
  7. A coupled component GARCH model for intraday and overnight volatility By Oliver Linton; Jianbin Wu
  8. Nonlinear panel data methods for dynamic heterogeneous agent models By Manuel Arellano; Stéphane Bonhomme
  9. How to predict financial stress? An assessment of Markov switching models By Duprey, Thibaut; Klaus, Benjamin
  10. A semi-parametric point process model of the interactions between equity markets By Clements, A.E.; Hurn, A.S.; Lindsay, K.A.; Volkov, V.V

  1. By: Manuel Gonzalez-Astudillo
    Abstract: This paper develops a method for decomposing GDP into trend and cycle exploiting the cross-sectional variation of state-level real GDP and unemployment rate data. The model assumes that there are common output and unemployment rate trend and cycle components, and that each state’s output and unemployment rate are subject to idiosyncratic trend and cycle perturbations. The model is estimated with Bayesian methods using quarterly data from 2005:Q1 to 2016:Q1 for the 50 states and the District of Columbia. Results show that the U.S. output gap reached about -8% during the Great Recession and is about 0.6% in 2016:Q1.
    Keywords: Unobserved components model ; State-level GDP data ; Trend-cycle decomposition
    JEL: C13 C32 C52
    Date: 2017–05
  2. By: Friedrich, Marina (QE / Econometrics); Smeekes, Stephan (QE / Econometrics); Urbain, Jean-Pierre
    Abstract: In this paper a modified wild bootstrap method is presented to construct pointwise confidence intervals around a nonparametric deterministic trend model. We derive the asymptotic distribution of a nonparametric kernel estimator of the trend function under general conditions, which allow for serial correlation and heteroskedasticity. Asymptotic validity of the bootstrap method is established and it is shown to work well in finite samples in an extensive simulation study. The bootstrap method has the potential of providing simultaneous confidence bands for the same models along the lines of Bühlmann (1998) and can be applied without further adjustments to missing data. We illustrate this by applying the proposed method to a time series of atmospheric ethane which can be used as an indicator of atmospheric pollution and transport.
    Keywords: autoregressive wild bootstrap, nonparametric estimation, time series, simultaneous confidence bands, trend estimation
    JEL: C14 C22
    Date: 2017–05–01
  3. By: Stavros Stavroyiannis
    Abstract: The majority of stylized facts of financial time series and several Value-at-Risk measures are modeled via univariate or multivariate GARCH processes. It is not rare that advanced GARCH models fail to converge for computational reasons, and a usual parsimonious approach is the GJR-GARCH model. There is a disagreement in the literature and the specialized econometric software, on which constraints should be used for the parameters, introducing indirectly the distinction between asymmetry and leverage. We show that the approach used by various software packages is not consistent with the Nelson-Cao inequality constraints. Implementing Monte Carlo simulations, despite of the results being empirically correct, the estimated parameters are not theoretically coherent with the Nelson-Cao constraints for ensuring positivity of conditional variances. On the other hand ruling out the leverage hypothesis, the asymmetry term in the GJR model can take negative values when typical constraints like the condition for the existence of the second and fourth moments, are imposed.
    Date: 2017–05
  4. By: Joana Estevens; Paulo Rocha; Joao Boto; Pedro Lind
    Abstract: We model non-stationary volume-price distributions with a log-normal distribution and collect the time series of its two parameters. The time series of the two parameters are shown to be stationary and Markov-like and consequently can be modelled with Langevin equations, which are derived directly from their series of values. Having the evolution equations of the log-normal parameters, we reconstruct the statistics of the first moments of volume-price distributions which fit well the empirical data. Finally, the proposed framework is general enough to study other non-stationary stochastic variables in other research fields, namely biology, medicine and geology.
    Date: 2017–04
  5. By: Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Hamburg, Germany); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa)
    Abstract: We use a Boosted Regression Trees (BRT) approach to study the potentially nonlinear link between various standard predictors (stock-market returns, term spread, a short-term interest rate, among others), components of a news-based uncertainty index, and U.S recessions. The BRT approach shows that, according to a relative-importance measure, war-related uncertainty is among the top five predictors of recessions at three different forecast horizons. In the second half of the 20th century, uncertainty regarding the state of securities markets has gained in relative importance. Partial-dependence curves show that the probability of a recession is nonlinearly linked to war-related and securities-markets uncertainty. An analysis based on receiver-operating-characteristic (ROC) curves shows that including war-related uncertainty in the list of predictors improves out-of-sample forecasting performance at a longer-term forecasting horizon, where the predictive value of this component relative to other components of uncertainty has fallen in the second half of the 20th century. Estimation results for a dynamic version of the BRT approach recover the relative importance of various lags of government-related uncertainty for recession forecasting at a longer forecast horizon.
    Keywords: Recessions, Uncertainty, Forecasting, Boosted regression trees, ROC curves
    JEL: C53 E32 E37
    Date: 2017–05
  6. By: Jose Diogo Barbosa (Institute for Fiscal Studies); Marcelo Moreira (Institute for Fiscal Studies and Fundação Getúlio Vargas)
    Abstract: Lancaster (2002) proposes an estimator for the dynamic panel data model with homoskedastic errors and zero initial conditions. In this paper, we show this estimator is invariant to orthogonal transformations, but is inefficient because it ignores additional information available in the data. The zero initial condition is trivially satis fied by subtracting initial observations from the data. We show that di fferencing out the data further erodes efficiency compared to drawing inference conditional on the rst observations. Finally, we compare the conditional method with standard random eff ects approaches for unobserved data. Standard approaches implicitly rely on normal approximations, which may not be reliable when unobserved data is very skewed with some mass at zero values. For example, panel data on fi rms naturally depend on the fi rst period in which the fi rm enters on a new state. It seems unreasonable then to assume that the process determining unobserved data is known or stationary. We can instead make inference on structural parameters by conditioning on the initial observations.
    Keywords: Autoregressive, Panel Data, Invariance, Eciency.
    JEL: C12 C30
    Date: 2017–01–20
  7. By: Oliver Linton (Institute for Fiscal Studies and University of Cambridge); Jianbin Wu (Institute for Fiscal Studies)
    Abstract: We propose a semi-parametric coupled component GARCH model for intraday and overnight volatility that allows the two periods to have di fferent properties. To capture the very heavy tails of overnight returns, we adopt a dynamic conditional score model with t innovations. We propose a several step estimation procedure that captures the nonparametric slowly moving components by kernel estimation and the dynamic parameters by t maximum likelihood. We establish the consistency and asymptotic normality of our estimation procedures. We extend the modelling to the multivariate case. We apply our model to the study of the Dow Jones industrial average component stocks over the period 1991-2016 and the CRSP cap based portfolios over the period of 1992-2015. We show that actually the ratio of overnight to intraday volatility has increased in importance for big stocks in the last 20 years. In addition, our model provides better intraday volatility forecast since it takes account of the full dynamic consequences of the overnight shock and previous ones.
    Date: 2017–01–26
  8. By: Manuel Arellano (Institute for Fiscal Studies and CEMFI); Stéphane Bonhomme (Institute for Fiscal Studies and University of Chicago)
    Abstract: Recent developments in nonlinear panel data analysis allow identifying and estimating general dynamic systems. In this review we describe some results and techniques for nonparametric identifi cation and flexible estimation in the presence of time-invariant and time-varying latent variables. This opens the possibility to estimate nonlinear reduced forms in a large class of structural dynamic models with heterogeneous agents. We show how such reduced forms may be used to document policy-relevant derivative e ffects, and to improve the understanding and facilitate the implementation of structural models.
    Keywords: dynamic models, structural economic models, panel data, unobserved heterogeneity.
    JEL: C23
    Date: 2016–11–01
  9. By: Duprey, Thibaut; Klaus, Benjamin
    Abstract: This paper predicts phases of the financial cycle by combining a continuous financial stress measure in a Markov switching framework. The debt service ratio and property market variables signal a transition to a high financial stress regime, while economic sentiment indicators provide signals for a transition to a tranquil state. Whereas the in-sample analysis suggests that these indicators can provide an early warning signal up to several quarters prior to the respective regime change, the out-of-sample findings indicate that most of this performance is due to the data gathered during the global financial crisis. Comparing the prediction performance with a standard binary early warning model reveals that the MS model is outperforming in the vast majority of model specifications for a horizon up to three quarters prior to the onset of financial stress. JEL Classification: C54, G01, G15
    Keywords: continuous coincident financial stress measure, early warning model, time-varying transition probability Markov switching model
    Date: 2017–05
  10. By: Clements, A.E. (Queensland University of Technology); Hurn, A.S. (Queensland University of Technology); Lindsay, K.A. (Queensland University of Technology); Volkov, V.V (Tasmanian School of Business & Economics, University of Tasmania)
    Abstract: A novel point process framework to examine the links between transaction data across equity markets is proposed. Moving beyond a simple exponential kernel specification, it is shown that the kernel matrix can be estimated by solving a system of integral equations which is uniquely characterised by second order cumulants. The cumulant based estimator is shown to be asymptotically normally distributed and consistent and is shown to perform well in a small simulation study. Applying this method to data from U.S and U.K. equity markets when both are open, reveals that two-way interaction between trades is significant. Moreover, this interaction is characterised by both complex short term dynamics and long memory, which cannot be captured by conventioanl exponential kernels.
    Keywords: point processes, high-frequency data, conditional intensity
    JEL: C32 C51 C52 G10
    Date: 2017

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