nep-ets New Economics Papers
on Econometric Time Series
Issue of 2016‒02‒04
twelve papers chosen by
Yong Yin
SUNY at Buffalo

  1. System Estimation of Panel Data Models under Long-Range Dependence By Yunus Emre Ergemen
  2. Data-Driven Inference on Sign Restrictions in Bayesian Structural Vector Autoregression By Markku Lanne; Jani Luoto
  3. Moment explosions, implied volatility and local volatility at extreme strikes By Sidi Mohamed Aly
  4. Chaos in Fractionally Integrated Generalized Autoregressive Conditional Heteroskedastic Processes By Adil Yilmaz; Gazanfer Unal
  5. How to improve accuracy for DFA technique By Alessandro Stringhi; Silvia Figini
  6. Multivariate moments expansion density: application of the dynamic equicorrelation model By Trino-Manuel Ñíguez; Javier Perote
  7. Applying Flexible Parameter Restrictions in Markov-Switching Vector Autoregression Models By Andrew Binning; Junior Maih
  8. Univariate Unit Root Tests Perform Poorly When Data Are Cointegrated By W. Robert Reed
  9. Monte Carlo evidence on the estimation of AR(1) panel data sample selection models By Sergi Jiménez-Martín; José María Labeaga
  10. Inference for nonparametric high-frequency estimators with an application to time variation in betas By KALNINA, Ilze
  11. The impact of the initial condition on covariate augmented unit root tests By Chrystalleni Aristidou; David Harvey; Stephen Leybourne
  12. Generalizing smooth transition autoregressions By Emilio Zanetti Chini

  1. By: Yunus Emre Ergemen (Aarhus University and CREATES)
    Abstract: A general dynamic panel data model is considered that incorporates individual and interactive fixed effects allowing for contemporaneous correlation in model innovations. The model accommodates general stationary or nonstationary long-range dependence through interactive fixed effects and innovations, removing the necessity to perform a priori unit-root or stationarity testing. Moreover, persistence in innovations and interactive fixed effects allows for cointegration; innovations can also have vector-autoregressive dynamics; deterministic trends can be featured. Estimations are performed using conditional-sum-of-squares criteria based on projected series by which latent characteristics are proxied. Resulting estimates are consistent and asymptotically normal at standard parametric rates. A simulation study provides reliability on the estimation method. The method is then applied to the long-run relationship between debt and GDP.
    Keywords: Long memory, factor models, panel data, endogeneity, fixed effects, debt and GDP
    JEL: C32 C33
    Date: 2016–01–13
  2. By: Markku Lanne (University of Helsinki and CREATES); Jani Luoto (University of Helsinki)
    Abstract: Sign-identified structural vector autoregressive (SVAR) models have recently become popular. However, the conventional approach to sign restrictions only yields set identification, and implicitly assumes an informative prior distribution of the impulse responses whose influence does not vanish asymptotically. In other words, within the set the impulse responses are driven by the implicit prior, and the likelihood has no significance. In this paper, we introduce a Bayesian SVAR model where unique identification is achieved by statistical properties of the data. Our setup facilitates assuming a genuinely noninformative prior and thus learning from the data about the impulse responses. While the shocks are statistically identified, they carry no economic meaning as such, and we propose a procedure for labeling them by their probabilities of satisfying each of the given sign restrictions. The impulse responses of the identified economic shocks can subsequently be computed in a straightforward manner. Our approach is quite flexible in that it facilitates labeling only a subset of the sign-restricted shocks, and also concluding that none of the sign restrictions is plausible. We illustrate the methods by two empirical applications to U.S. macroeconomic data.
    Keywords: Structural vector autoregression, independence, posterior model probability, monetary policy shock
    JEL: C32 C51 C52
    Date: 2016–01–25
  3. By: Sidi Mohamed Aly
    Abstract: We consider a stochastic volatility model where the moment generating function of the logarithmic price is finite only on part of the real line. Using a new Tauberian result obtained in [1] and [2], we show that the knowledge of the moment generating function near its critical moment gives a sharp asymptotic expansion (with an error of order o(1)) of the local volatility and implied volatility for small and large strikes. We apply our theoretical estimates to Gatheral's SVI parametrization of the implied volatility and Heston's model.
    Date: 2016–01
  4. By: Adil Yilmaz; Gazanfer Unal
    Abstract: Fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) arises in modeling of financial time series. FIGARCH is essentially governed by a system of nonlinear stochastic difference equations ${u_t}$ = ${z_t}$ $(1-\sum\limits_{j=1}^q \beta_j L^j)\sigma_{t}^2 = \omega+(1-\sum\limits_{j=1}^q \beta_j L^j - (\sum\limits_{k=1}^p \varphi_k L^k) (1-L)^d) u_t^2$, where $\omega\in$ R, and $\beta_j\in$ R are constant parameters, $\{u_t\}_{{t\in}^+}$ and $\{\sigma_t\}_{{t\in}^+}$ are the discrete time real valued stochastic processes which represent FIGARCH (p,d,q) and stochastic volatility, respectively. Moreover, L is the backward shift operator, i.e. $L^d u_t \equiv u_{t-d}$ (d is the fractional differencing parameter 0$
    Date: 2016–01
  5. By: Alessandro Stringhi; Silvia Figini
    Abstract: This paper extends the existing literature on empirical estimation of the confidence intervals associated to the Detrended Fluctuation Analysis (DFA). We used Montecarlo simulation to evaluate the confidence intervals. Varying the parameters in DFA technique, we point out the relationship between those and the standard deviation of H. The parameters considered are the finite time length L, the number of divisors d used and the values of those. We found that all these parameters play a crucial role, determining the accuracy of the estimation of H.
    Date: 2016–02
  6. By: Trino-Manuel Ñíguez (Banco de España); Javier Perote (Universidad de Salamanca)
    Abstract: In this study, we propose a new semi-nonparametric (SNP) density model for describing the density of portfolio returns. This distribution, which we refer to as the multivariate moments expansion (MME), admits any non-Gaussian (multivariate) distribution as its basis because it is specified directly in terms of the basis density s moments. To obtain the expansion of the Gaussian density, the MME is a reformulation of the multivariate Gram-Charlier (MGC), but the MME is much simpler and tractable than the MGC when positive transformations are used to produce well-defined densities. As an empirical application, we extend the dynamic conditional equicorrelation (DECO) model to an SNP framework using the MME. The resulting model is parameterized in a feasible manner to admit two-stage consistent estimation, and it represents the DECO as well as the salient non-Gaussian features of portfolio return distributions. The in- and out-of-sample performance of a MME-DECO model of a portfolio of 10 assets demonstrates that it can be a useful tool for risk management purposes.
    Keywords: density forecasting, dynamic equicorrelation, Gram-Charlier series, multivariate GARCH, semi-nonparametric method
    JEL: C16 G1
    Date: 2016–01
  7. By: Andrew Binning; Junior Maih
    Abstract: We present a new method for imposing parameter restrictions in Markov-Switching Vector Autoregression (MS-VAR) models. Our method is more flexible than competing methodologies and easily handles a range of parameter restrictions over different equations, regimes and parameter types. We also expand the range of priors used in the MS-VAR literature. We demonstrate the versatility of our approach using three appropriate examples.
    Keywords: Parameter Restrictions, MS-VAR estimation, Block Exogeneity, Zero Restrictions, Bayesian estimation
    Date: 2015–12
  8. By: W. Robert Reed (University of Canterbury)
    Abstract: This note demonstrates that unit root tests can suffer from inflated Type I error rates when data are cointegrated. Results from Monte Carlo simulations show that three commonly used unit root tests – the ADF, Phillips-Perron, and DF-GLS tests – frequently overreject the true null of a unit root for at least one of the cointegrated variables in reasonably sized samples. While the addition of lagged differenced (LD) terms can sometimes eliminate the size distortion, standard diagnostics such as (i) testing for serial correlation in the residuals and (ii) using information criteria to select lags are unable to identify the appropriate number of terms.
    Keywords: Unit root testing, cointegration, DF-GLS test, Augmented Dickey-Fuller test, Phillips-Perron test, Monte Carlo, simulation
    JEL: C32 C22 C18
    Date: 2016–01–22
  9. By: Sergi Jiménez-Martín; José María Labeaga
    Abstract: We present Generalized Method of Moments estimators for AR(1) dynamic panel data sample selection models. We perform a Monte Carlo study to evaluate the finite sample properties of the proposed estimators. Our results suggest that correcting for sample selection in many standard cases does not add much to the uncorrected estimates. In particular, the magnitude of the biases is similar and very small when estimating the model either correcting or not the equation of interest. This equivalence also holds in the dynamic model with exogenous regressors. These results are especially relevant for practitioners either when there is selection of unknown form or selection is difficult to model.
    Date: 2016–01
  10. By: KALNINA, Ilze
    Abstract: We consider the problem of conducting inference on nonparametric high-frequency estimators without knowing their asymptotic variances. We prove that a multivariate subsampling method achieves this goal under general conditions that were not previously available in the literature. We suggest a procedure for a data-driven choice of the bandwidth parameters. Our simulation study indicates that the subsampling method is much more robust than the plug-in method based on the asymptotic expression for the variance. Importantly, the subsampling method reliably estimates the variability of the Two Scale estimator even when its parameters are chosen to minimize the finite sample Mean Squared Error; in contrast, the plugin estimator substantially underestimates the sampling uncertainty. By construction, the subsampling method delivers estimates of the variance-covariance matrices that are always positive semi-definite. We use the subsampling method to study the dynamics of financial betas of six stocks on the NYSE. We document significant variation in betas within year 2006, and find that tick data captures more variation in betas than the data sampled at moderate frequencies such as every five or twenty minutes. To capture this variation we estimate a simple dynamic model for betas. The variance estimation is also important for the correction of the errors-in-variables bias in such models. We find that the bias corrections are substantial, and that betas are more persistent than the naive estimators would lead one to believe.
    JEL: F15 F34 F36 F41
    Date: 2015
  11. By: Chrystalleni Aristidou; David Harvey; Stephen Leybourne
    Abstract: We examine the behaviour of OLS-demeaned/detrended and GLS-demeaned/detrended unit root tests that employ stationary covariates, as proposed by Hansen (1995) and Elliott and Jansson (2003), respectively, in situations where the magnitude of the initial condition of the time series under consideration may be non-negligible. We show that the asymptotic power of such tests is very sensitive to the initial condition; OLS- and GLS-based tests achieve relatively high power for large and small magnitudes of the initial condition, respectively. Combining information from both types of test via a simple union of rejections strategy is shown to effectively capture the higher power available across all initial condition magnitudes.
    Keywords: Unit root tests; stationary covariates; initial condition uncertainty; asymptotic power.
  12. By: Emilio Zanetti Chini (Department of Economics and Management)
    Abstract: We introduce a new time series model capable to parametrize the joint asymmetry in duration and length of cycles - the dynamic asymmetry - by using a particular generalization of the logistic function. The modelling strategy is discussed in detail, with particular emphasis on two different tests for the null of symmetric adjustment and three diagnostic tests, whose power properties are explored via Monte Carlo experiments. Four case studies in classical economic and biological real datasets illustrate the versatility of the new model in different fields. In all the cases, the dynamic asymmetry in the cycle is efficiently detected and modelled. Finally, a rolling forecasting exercise is applied to the resulting estimates. Our model beats linear and conventional nonlinear competitors in point forecasting, while this superiority becomes less evident in density forecasting, specially when relying on robust measures.
    Keywords: Dynamic asymmetry, Nonlinear time series, Econometric Modelling, Point forecasts, Density forecasts, Evaluating forecasts, Combining forecasts, Error measures.
    JEL: C22 C51 C52
    Date: 2016–01

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