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on Econometric Time Series |
| By: | Tingguo Zheng; Han Xiao; Rong Chen |
| Abstract: | One of the important and widely used classes of models for non-Gaussian time series is the generalized autoregressive model average models (GARMA), which specifies an ARMA structure for the conditional mean process of the underlying time series. However, in many applications one often encounters conditional heteroskedasticity. In this paper we propose a new class of models, referred to as GARMA-GARCH models, that jointly specify both the conditional mean and conditional variance processes of a general non-Gaussian time series. Under the general modeling framework, we propose three specific models, as examples, for proportional time series, nonnegative time series, and skewed and heavy-tailed financial time series. Maximum likelihood estimator (MLE) and quasi Gaussian MLE (GMLE) are used to estimate the parameters. Simulation studies and three applications are used to demonstrate the properties of the models and the estimation procedures. |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2105.05532&r= |
| By: | Silva Lopes, Artur C. |
| Abstract: | Motivated by the purpose to assess the income convergence hypothesis, a simple new Fourier-type unit root test of the Dickey-Fuller family is introduced and analysed. In spite of a few shortcomings that it shares with rival tests, the proposed test generally improves upon them in terms of power performance in small samples. The empirical results that it produces for a recent and updated sample of data for 25 countries clearly contrast with previous evidence produced by the Fourier approach and, more generally, they also contradict a recent wave of optimism concerning income convergence, as they are mostly unfavourable to it. |
| Keywords: | income convergence; unit root tests; structural breaks |
| JEL: | C22 F43 O47 |
| Date: | 2021–03–19 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:107676&r= |
| By: | Hauber, Philipp; Schumacher, Christian |
| Abstract: | We propose a new approach to sample unobserved states conditional on available data in (conditionally) linear unobserved component models when some of the observations are missing. The approach is based on the precision matrix of the states and model variables, which is sparse and banded in many economic applications and allows for efficient sampling. The existing literature on precision-based sampling is focused on complete-data applications, whereas the proposed samplers in this paper provide draws for states and missing observations by using permutations of the precision matrix. The approaches can be easily integrated into Bayesian estimation procedures like the Gibbs sampler. By allowing for incomplete data sets, the proposed sampler expands the range of potential applications for precision-based samplers in practice. We derive the sampler for a factor model, although it can be applied to a wider range of empirical macroeconomic models. In an empirical application, we estimate international factors in GDP growth in a large unbalanced data set of about 180 countries. |
| Keywords: | Precision-based sampling,Bayesian estimation,state-space models,missing observations,factor models,banded matrices |
| JEL: | C32 C38 C63 C55 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdps:112021&r= |
| By: | Piergiorgio Alessandri (Bank of Italy); Andrea Gazzani (Bank of Italy); Alejandro Vicondoa (Universidad Católica de Chile) |
| Abstract: | Isolating financial uncertainty shocks is difficult because financial markets rapidly price changes in several economic fundamentals. To bypass this difficulty, we identify uncertainty shocks using daily data and use their monthly averages as an instrument in a VAR. We show that this novel approach is theoretically appealing and has dramatic implications for leading empirical studies on financial uncertainty. Daily interactions between equity returns, bond spreads and expected volatility cause previous identification schemes to fail at the monthly frequency. Once these interactions are explicitly modeled, the impact of uncertainty shocks on output and inflation is significant and similar across specifications. |
| Keywords: | uncertainty shocks financial shocks structural vector autoregression high-frequency identification external instruments |
| JEL: | C32 C36 E32 |
| Date: | 2021–04 |
| URL: | http://d.repec.org/n?u=RePEc:aoz:wpaper:61&r= |
| By: | Geoffrey Ducournau |
| Abstract: | One of the standardized features of financial data is that log-returns are uncorrelated, but absolute log-returns or their squares namely the fluctuating volatility are correlated and is characterized by heavy tailed in the sense that some moment of the absolute log-returns is infinite and typically non-Gaussian [20]. And this last characteristic change accordantly to different timescales. We propose to model this long-memory phenomenon by superstatistical dynamics and provide a Bayesian Inference methodology drawing on Metropolis-Hasting random walk sampling to determine which superstatistics among inverse-Gamma and log-Normal describe the best log-returns complexity on different timescales, from high to low frequency. We show that on smaller timescales (minutes) even though the Inverse-Gamma superstatistics works the best, the log-Normal model remains very reliable and suitable to fit the absolute log-returns probability density distribution with strong capacity of describing heavy tails and power law decays. On larger timescales (daily), we show in terms of Bayes factor that the inverse-Gamma superstatistics is preferred to the log-Normal model. We also show evidence of a transition of statistics from power law decay on small timescales to exponential decay on large scale with less heavy tails meaning that on larger time scales the fluctuating volatility tend to be memoryless, consequently superstatistics becomes less relevant. |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2105.04171&r= |
| By: | Bańbura, Marta; Brenna, Federica; Paredes, Joan; Ravazzolo, Francesco |
| Abstract: | This paper studies how to combine real-time forecasts from a broad range of Bayesian vector autoregression (BVAR) specifications and survey forecasts by optimally exploiting their properties. To do that, it compares the forecasting performance of optimal pooling and tilting techniques, including survey forecasts for predicting euro area inflation and GDP growth at medium-term forecast horizons using both univariate and multivariate forecasting metrics. Results show that the Survey of Professional Forecasters (SPF) provides good point forecast performance, but also that SPF forecasts perform poorly in terms of densities for all variables and horizons. Accordingly, when the model combination or the individual models are tilted to SPF's first moments, point accuracy and calibration improve, whereas they worsen when SPF's second moments are included. We conclude that judgement incorporated in survey forecasts can considerably increase model forecasts accuracy, however, the way and the extent to which it is incorporated matters. JEL Classification: C11, C32, C53, E27, E37 |
| Keywords: | Entropic tilting, Judgement, Optimal Pooling, Real Time, Survey of Professional Forecasters |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20212543&r= |