|
on Econometric Time Series |
| By: | Ladislav Kristoufek |
| Abstract: | In this short report, we investigate the ability of the DCCA coefficient to measure correlation level between non-stationary series. Based on a wide Monte Carlo simulation study, we show that the DCCA coefficient can estimate the correlation coefficient accurately regardless the strength of non-stationarity (measured by the fractional differencing parameter $d$). For a comparison, we also report the results for the standard Pearson's correlation coefficient. The DCCA coefficient dominates the Pearson's coefficient for non-stationary series. |
| Date: | 2013–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1310.3984&r=ets |
| By: | Solberger M.; Zhou X. (GSBE) |
| Abstract: | We consider an exact factor model and derive a Lagrange multiplier-type test for unit roots in the idiosyncratic components. The asymptotic distribution of the statistic is derived under the misspecification that the differenced factors are white noise. We prove that the asymptotic distribution is independent of the distribution of the factors, and that the factors are allowed to be integrated, cointegrate, or be stationary. In a simulation study, size and power is compared with some popular second generation panel unit root tests. The simulations suggest that our statistic is well-behaved in terms of size and that it is powerful and robust in comparison with existing tests. |
| Keywords: | Hypothesis Testing: General; Single Equation Models; Single Variables: Models with Panel Data; Longitudinal Data; Spatial Time Series; |
| JEL: | C12 C23 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:umagsb:2013058&r=ets |
| By: | Zhou X.; Solberger M. (GSBE) |
| Abstract: | Recent developments within the panel unit-root literature have illustrated how the exact factor model serves as a parsimonious framework and allows for consistent maximum likelihood inference even when it is misspecified contra the more general approximate factor model. In this paper we consider an exact factor model with AR1 dynamics and propose LM-type tests for idiosyncratic and common unit roots. We derive the asymptotic distributions and carry out simulations to investigate size and power of the tests in finite samples, as well as compare the performance with some existing tests. |
| Keywords: | Hypothesis Testing: General; Single Equation Models; Single Variables: Models with Panel Data; Longitudinal Data; Spatial Time Series; |
| JEL: | C12 C23 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:umagsb:2013059&r=ets |
| By: | Westerlund J.; Smeekes S. (GSBE) |
| Abstract: | Most panel data studies of the predictability of returns presume that the cross-sectional units are independent, an assumption that is not realistic. As a response to this, the current paper develops block bootstrap-based panel predictability tests that are valid under very general conditions. Some of the allowable features include heterogeneous predictive slopes, persistent predictors, and complex error dynamics, including cross-unit endogeneity. |
| Keywords: | Statistical Simulation Methods: General; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Single Equation Models; Single Variables: Models with Panel Data; Longitudinal Data; Spatial Time Series; Financial Crises; Asset Pricing; Trading volume; Bond Interest Rates; |
| JEL: | C15 C22 C23 G01 G12 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:umagsb:2013060&r=ets |
| By: | Hadri, Kaddour; Kurozumi, Eiji; Rao, Yao |
| Abstract: | In this paper, we propose new cointegration tests for single equations and panels. In both cases, the asymptotic distributions of the tests, which are derived with N fixed and T going to infinity, are shown to be standard normals. The effects of serial correlation and cross-sectional dependence are mopped out via long-run variances. An effective bias correction is derived which is shown to work well in finite samples; particularly when N is smaller than T. Our panel tests are robust to possible cointegration across units. |
| Keywords: | cointegration, panel cointegration, cross-section dependence, bias correction, DOLS, FCLT |
| JEL: | C12 C15 C22 C23 |
| Date: | 2013–09 |
| URL: | http://d.repec.org/n?u=RePEc:hit:econdp:2013-12&r=ets |
| By: | Antonia Arsova (Leuphana University Lueneburg, Germany); Deniz Dilan Karaman Oersal (Leuphana University Lueneburg, Germany) |
| Abstract: | This paper proposes a new likelihood-based panel cointegration rank test which extends the test of Oersal and Droge (2012) (henceforth Panel SL test) to allow for crosssectional dependence. The dependence is modelled by unobserved common factors which affect the variables in each cross-section through heterogeneous loadings. The common components are estimated following the panel analysis of nonstationarity in idiosyncratic and common components (PANIC) approach of Bai and Ng (2004) and the estimates are subtracted from the observations. The cointegrating rank of the defactored data is then tested by the Panel SL test. A Monte Carlo study demonstrates that the proposed testing procedure has reasonable size and power properties in finite samples. |
| Keywords: | panel cointegration rank test, cross-sectional dependence, common factors, likelihoodratio, time trend |
| JEL: | C12 C15 C33 |
| Date: | 2013–08 |
| URL: | http://d.repec.org/n?u=RePEc:lue:wpaper:280&r=ets |
| By: | Sucarrat, Genaro; Escribano, Alvaro |
| Abstract: | A critique that has been directed towards the log-GARCH model is that its log-volatility specification does not exist in the presence of zero returns. A common ``remedy" is to replace the zeros with a small (in the absolute sense) non-zero value. However, this renders Quasi Maximum Likelihood (QML) estimation asymptotically biased. Here, we propose a solution to the case where actual returns are equal to zero with probability zero, but zeros nevertheless are observed because of measurement error (due to missing values, discreteness approximisation error, etc.). The solution treats zeros as missing values and handles these by combining QML estimation via the ARMA representation with the Expectation-maximisation (EM) algorithm. Monte Carlo simulations confirm that the solution corrects the bias, and several empirical applications illustrate that the bias-correcting estimator can make a substantial difference. |
| Keywords: | ARCH, exponential GARCH, log-GARCH, ARMA, Expectation-Maximisation (EM) |
| JEL: | C22 C58 |
| Date: | 2013–09–09 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:50699&r=ets |
| By: | Emilio Zanetti Chini (University of Rome "Tor Vergata") |
| Abstract: | This paper introduces a variant of the smooth transition autoregression (STAR). The proposed model is able to parametrize the asymmetry in the tails of the transition equation by using a particular generalization of the logistic function. The null hypothesis of symmetric adjustment toward a new regime is tested by building two different LM-type tests. The first one maintains the original parametrization, while the second one is based on a third-order expanded auxiliary regression. Three diagnostic tests for no error autocorrelation, no additive asymmetry and parameter constancy are also discussed. The empirical size and power of the new symmetry as well as diagnostic tests are investigated by an extensive Monte Carlo experiment. An empirical application of the so generalized STAR (GSTAR) model to four economic time series reveals that the asymmetry in the transition between two regimes is a feature to be considered for economic analysis. |
| Date: | 2013–10–15 |
| URL: | http://d.repec.org/n?u=RePEc:rtv:ceisrp:294&r=ets |
| By: | Pauwels, Laurent; Vasnev, Andrey |
| Abstract: | The problem of finding appropriate weights to combine several density forecasts is an important issue currently debated in the forecast combination literature. Recently, a paper by Hall and Mitchell (IJF, 2007) proposes to combine density forecasts with optimal weights obtained from solving an optimization problem. This paper studies the properties of this optimization problem when the number of forecasting periods is relatively small and finds that it often produces corner solutions by allocating all the weight to one density forecast only. This paper's practical recommendation is to have an additional training sample period for the optimal weights. While reserving a portion of the data for parameter estimation and making pseudo-out-of-sample forecasts are common practices in the empirical literature, employing a separate training sample for the optimal weights is novel, and it is suggested because it decreases the chances of corner solutions. Alternative log-score or quadratic-score weighting schemes do not have this training sample requirement. January |
| Date: | 2013–01 |
| URL: | http://d.repec.org/n?u=RePEc:syb:wpbsba:2123/8932&r=ets |
| By: | Audrino, Francesco; Camponovo, Lorenzo |
| Abstract: | We derive new theoretical results on the properties of the adaptive least absolute shrinkage and selection operator (adaptive lasso) for time series regression models. In particular we investigate the question of how to conduct finite sample inference on the parameters given an adaptive lasso model for some fixed value of the shrinkage parameter. Central in this study is the test of the hypothesis that a given adaptive lasso parameter equals zero, which therefore tests for a false positive. To this end we construct a simple (conservative) testing procedure and show, theoretically and empirically through extensive Monte Carlo simulations, that the adaptive lasso combines efficient parameter estimation, variable selection, and valid finite sample inference in one step. Moreover, we analytically derive a bias correction factor that is able to significantly improve the empirical coverage of the test on the active variables. Finally, we apply the introduced testing procedure to investigate the relation between the short rate dynamics and the economy, thereby providing a statistical foundation (from a model choice perspective) to the classic Taylor rule monetary policy model. |
| Keywords: | Adaptive lasso; Time series; Oracle properties; Finite sample inference; Taylor rule monetary policy model. |
| JEL: | C12 C22 E43 |
| Date: | 2013–10 |
| URL: | http://d.repec.org/n?u=RePEc:usg:econwp:2013:27&r=ets |