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on Econometric Time Series |
| By: | Karavias, Yiannis; Tzavalis, Elias |
| Abstract: | In this paper we suggest panel data unit root tests which allow for a structural breaks in the individual effects or linear trends of panel data models. This is done under the assumption that the disturbance terms of the panel are heterogeneous and serially correlated. The limiting distributions of the suggested test statistics are derived under the assumption that the time-dimension of the panel (T) is fixed, while the cross-section (N) grows large. Thus, they are appropriate for short panels, where T is small. The tests consider the cases of a known and unknown date break. For the latter case, the paper gives the analytic form of the distribution of the test statistics. Monte Carlo evidence suggest that our tests have size which is very close to its nominal level and satisfactory power in small-T panels. This is true even for cases where the degree of serial correlation is large and negative, where single time series unit root tests are found to be critically oversized. |
| Keywords: | Panel data models; unit roots; structural breaks; |
| JEL: | C23 C22 |
| Date: | 2012–07 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:43128&r=ets |
| By: | Helmut Lütkepohl |
| Abstract: | Identification of shocks of interest is a central problem in structural vector autoregressive (SVAR) modelling. Identification is often achieved by imposing restrictions on the impact or long-run effects of shocks or by considering sign restrictions for the impulse responses. In a number of articles changes in the volatility of the shocks have also been used for identification. The present study focusses on the latter device. Some possible setups for identification via heteroskedasticity are reviewed and their potential and limitations are discussed. Two detailed examples are considered to illustrate the approach. |
| Keywords: | Markov switching model, vector autoregression, heteroskedasticity, vector GARCH, conditional heteroskedasticity |
| JEL: | C32 |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:diw:diwwpp:dp1259&r=ets |
| By: | Karavias, Yiannis; Tzavalis, Elias |
| Abstract: | Analytical asymptotic local power functions are employed to study the effects of general form short term serial correlation on fixed-T panel data unit root tests. Two models are considered, one that has only individual intercepts and one that has both individual intercepts and individual trends. It is shown that tests based on IV estimators are more powerful in all cases examined. Even more, for the model with individual trends an IV based test is shown to have non-trivial local power at the natural root-N rate. |
| Keywords: | Panel data models; unit roots; local power functions; serial correlation; incidental trends |
| JEL: | C23 |
| Date: | 2012–12 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:43131&r=ets |
| By: | Andrea Carriero; Todd E. Clark; Massimiliano Marcellino |
| Abstract: | This paper develops a method for producing current-quarter forecasts of GDP growth with a (possibly large) range of available within-the-quarter monthly observations of economic indicators, such as employment and industrial production, and financial indicators, such as stock prices and interest rates. In light of existing evidence of time variation in the variances of shocks to GDP, we consider versions of the model with both constant variances and stochastic volatility. We also evaluate models with either constant or time-varying regression coefficients. We use Bayesian methods to estimate the model, in order to facilitate providing shrinkage on the (possibly large) set of model parameters and conveniently generate predictive densities. We provide results on the accuracy of nowcasts of real-time GDP growth in the U.S. from 1985 through 2011. In terms of point forecasts, our proposal is comparable to alternative econometric methods and survey forecasts. In addition, it provides reliable density forecasts, for which the stochastic volatility specification is quite useful, while parameter time-variation does not seem to matter. |
| Keywords: | Bayesian statistical decision theory |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedcwp:1227&r=ets |