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on Econometric Time Series |
| By: | Florian Eckert (KOF Swiss Economic Institute, ETH Zurich, Switzerland); Samad Sarferaz (KOF Swiss Economic Institute, ETH Zurich, Switzerland) |
| Abstract: | This paper uses a Bayesian non-stationary dynamic factor model to extract common trends and cycles from large datasets. An important but neglected feature of Bayesian statistics allows to treat stationary and non-stationary time series equally in terms of parameter estimation. Based on this feature we show how to extract common trends and cycles from the data by ex-post processing the posterior output and describe how to derive an agnostic output gap measure. We apply the procedure to a large panel of quarterly time series that covers 158 macroeconomic and financial series for the United States. We find that our derived output gap measure tracks the U.S. business cycle well, exhibiting a high correlation with alternative estimates of the output gap. Since the factors are extracted from a comprehensive dataset, the resulting output gap estimates are stable at the current edge and can be decomposed in a new and meaningful way. |
| Keywords: | Non-Stationary Dynamic Factor Model, Potential Output Estimation, Output Gap Decomposition |
| JEL: | C11 C32 E32 |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:kof:wpskof:19-467&r=all |
| By: | Canepa, Alessandra (University of Turin) |
| Abstract: | Johansen?s (2000) Bartlett correction factor for the LR test of linear restrictions on cointegrated vectors is derived under the i.i.d. Gaussian assumption for the innovation terms. However, the distribution of most data relating to ?nancial variables are fat-tailed and often skewed, there is therefore a need to examine small sample inference procedures that require weaker assumptions for the innovation term. This paper suggests that using a non-parametric bootstrap to approximate a Bartlett-type correction provides a statistic that does not require speci?cation of the innovation distribution and can be used by applied econometricians to perform a small sample inference procedure that is less computationally demanding than estimating the p-value of the observed statistic. |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:uto:dipeco:202006&r=all |
| By: | Cem Cakmaklı (Koc University, Turkey; Rimini Centre for Economic Analysis); Yasin Simsek (Koc University, Turkey) |
| Abstract: | This paper extends the canonical model of epidemiology, SIRD model, to allow for time varying parameters for real-time measurement of the stance of the COVID-19 pandemic. Time variation in model parameters is captured using the generalized autoregressive score modelling structure designed for the typically daily count data related to pandemic. The resulting specification permits a flexible yet parsimonious model structure with a very low computational cost. This is especially crucial at the onset of the pandemic when the data is scarce and the uncertainty is abundant. Full sample results show that countries including US, Brazil and Russia are still not able to contain the pandemic with the US having the worst performance. Furthermore, Iran and South Korea are likely to experience the second wave of the pandemic. A real-time exercise show that the proposed structure delivers timely and precise information on the current stance of the pandemic ahead of the competitors that use rolling window. This, in turn, transforms into accurate short-term predictions of the active cases. We further modify the model to allow for unreported cases. Results suggest that the effects of the presence of these cases on the estimation results diminish towards the end of sample with the increasing number of testing. |
| Keywords: | COVID-19, SIRD, Observation driven models, Score models, Count data, time varying parameters |
| JEL: | C13 C32 C51 I19 |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:20-23&r=all |
| By: | Galvao, Ana Beatriz (University of Warwick); Mitchell, James (University of Warwick) |
| Abstract: | GDP is measured with error. But data uncertainty is rarely communicated quantitatively in real-time. An exception are the fan charts for historical GDP growth published by the Bank of England. To assess how well understood data uncertainty is, we first evaluate the accuracy of the historical fan charts and compare them with models of past revisions data. Secondly, to gauge perceptions of GDP data uncertainty across a wider set of experts, we conduct a new online survey. Our results call for greater communication of data uncertainties, to anchor dispersed expectations of data uncertainty. But they suggest that transitory data uncertainties can be adequately quantified, even without judgement, using past revisions data. |
| Keywords: | data revisions ; fan charts ; expectations survey ; backcasts ; density forecast calibration ; |
| JEL: | C53 E32 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:wrk:wrkemf:35&r=all |
| By: | Michal Franta; Ivan Sutoris |
| Abstract: | We decompose the Czech inflation time series into the trend and short-lived deviations from the trend by means of an unobserved component stochastic volatility model. We then carry out a regression analysis to interpret the two inflation components. The results indicate a fall in the inflation trend since the start of the sample (1998) which coincides with the introduction of the inflation targeting regime and with subsequent changes to the inflation target pursued by the Czech National Bank. Moreover, the regression analysis suggests that inflation expectations play a dominant role in the evolution of the trend. The behavior of the deviations from the trend exhibits features of an open-economy Phillips curve. |
| Keywords: | Czech inflation, inflation trend, Phillips curve, UCSV |
| JEL: | E5 E31 |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:cnb:wpaper:2020/1&r=all |