|
on Econometric Time Series |
| By: | Khraief, Naceur (University of Sousse); Shahbaz, Muhammad (COMSATS Institute of Information Technology); Heshmati, Almas (Jönköping University, Sogang University); Azam, Muhammad (Universiti Utara Malaysia) |
| Abstract: | This paper revisits the dynamics of unemployment rate for 29 OECD countries over the period of 1980-2013. Numerous empirical studies of the dynamics of unemployment rate are carried out within a linear framework. However, unemployment rate can show nonlinear behaviour as a result of business cycles or some idiosyncratic factors specific to labour market (Cancelo, 2007). Thus, as a testing strategy we first perform Harvey et al. (2008) linearity unit root test and then apply the newly ESTAR nonlinear unit root test suggested by Kruse (2011). This test has higher power than conventional unit root tests when time series exhibits nonlinear behaviour. Our empirical findings provide significant evidence in favour of unemployment rate stationarity for 25 countries. For robustness purpose, we have also used panel unit root tests without and with structural breaks. The results show that unemployment hysteresis hypothesis is strongly rejected when taking into account the cross-sectional and structural break assumptions. Thus, unemployment rates are expected to return back to their natural levels without executing any costly macroeconomic labour market policies by the OECD's governments. |
| Keywords: | unemployment, unit root, labour market policy, OECD |
| JEL: | C23 E24 J48 J64 N30 |
| Date: | 2015–12 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp9571&r=ets |
| By: | Monokroussos, George |
| Abstract: | This paper proposes a Bayesian nowcasting approach that utilizes information coming both from large real-time data sets and from priors constructed using internet search popularity measures. Exploiting rich information sets has been shown to deliver significant gains in nowcasting contexts, whereas popularity priors can lead to better nowcasts in the face of model and data uncertainty in real time, challenges which can be particularly relevant during turning points. It is shown, for a period centered on the latest recession in the United States, that this approach has the potential to deliver particularly good real-time nowcasts of GDP growth. |
| Keywords: | Nowcasting, Gibbs Sampling, Factor Models, Kalman Filter, Real-Time Data, Google Trends, Monetary Policy, Great Recession. |
| JEL: | C11 C22 C53 E37 E52 |
| Date: | 2015–11–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:68594&r=ets |
| By: | Drew Creal (The University of Chicago Booth School of Business, United States); Siem Jan Koopman (VU University Amsterdam, the Netherlands); André Lucas (VU University Amsterdam, the Netherlands); Marcin Zamojski (VU University Amsterdam, the Netherlands) |
| Abstract: | We introduce a new estimation framework which extends the Generalized Method of Moments (GMM) to settings where a subset of the parameters vary over time with unknown dynamics. To filter out the dynamic path of the time-varying parameter, we approximate the dynamics by an autoregressive process driven by the score of the local GMM criterion function. Our approach is completely observation driven, rendering estimation and inference straightforward. It provides a unified framework for modeling parameter instability in a context where the model and its parameters are only specified through (conditional) moment conditions, thus generalizing approaches built on fully specified parametric models. We provide examples of increasing complexity to highlight the advantages of our method. |
| Keywords: | dynamic models; time-varying parameters; generalized method of moments; non-linearity |
| JEL: | C10 C22 C32 C51 |
| Date: | 2015–12–24 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20150138&r=ets |