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on Econometric Time Series |
| By: | Alain Hecq; Ivan Ricardo; Ines Wilms |
| Abstract: | This paper proposes a Matrix Error Correction Model to identify cointegration relations in matrix-valued time series. We hereby allow separate cointegrating relations along the rows and columns of the matrix-valued time series and use information criteria to select the cointegration ranks. Through Monte Carlo simulations and a macroeconomic application, we demonstrate that our approach provides a reliable estimation of the number of cointegrating relationships. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.05601 |
| By: | Aknouche, Abdelhakim; Dimitrakopoulos, Stefanos |
| Abstract: | We consider two popular classes of volatility models, the generalized autoregressive conditional heteroscedastic (GARCH) model and the stochastic volatility (SV) model. We compare these two models with two classes of intensity models, the integer-valued GARCH (INGARCH) model and the integer-valued stochastic volatility/intensity (INSV) model, which are corresponding integer-valued counterparts of the former. We reveal the analogy and differences of the models within the same class of volatility/intensity models, as well as between the two different classes of models. |
| Keywords: | GARCH, integer-valued GARCH, integer-valued stochastic intensity, observation-driven models, parameter-driven models, stochastic volatility. |
| JEL: | C25 C51 C58 |
| Date: | 2024–10–28 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122528 |
| By: | Massimiliano Marcellino; Andrea Renzetti; Tommaso Tornese |
| Abstract: | We propose a functional MIDAS model to leverage high-frequency information for forecasting and nowcasting distributions observed at a lower frequency. We approximate the low-frequency distribution using Functional Principal Component Analysis and consider a group lasso spike-and-slab prior to identify the relevant predictors in the finite-dimensional SUR-MIDAS approximation of the functional MIDAS model. In our application, we use the model to nowcast the U.S. households' income distribution. Our findings indicate that the model enhances forecast accuracy for the entire target distribution and for key features of the distribution that signal changes in inequality. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.05629 |
| By: | Jamie L. Cross; Aubrey Poon; Wenying Yao; Dan Zhu |
| Abstract: | The Dynamic Nelson-Siegel (DNS) model implies that the instantaneous bond yield is a linear combination of yield curve’s level and slope factors. However, this constraint is not used in practice because it induces a singularity in the state covariance matrix. We show that this problem can be resolved using Bayesian methods. The key idea is to view the state equation as a prior distribution over missing data to obtain a hyperplane truncated multivariate normal conditional posterior distribution for the latent factors. This distribution can then be reparameterized as a conditional multivariate normal distribution given the constraint. Samples from this distribution can be obtained in a direct and computationally efficient manner, thus bypassing the Kalman filter recursions. The empirical significance of the resulting Yield-Macro Constrained DNS (YM-CDNS) model is demonstrated through both a reduced form analysis of the US Treasury yield curve, and a structural analysis of functional conventional and unconventional monetary policy shocks on the yield curve and the broader macroeconomy. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:bny:wpaper:0133 |
| By: | Clark, Todd E.; Ganics, Gergely; Mertens, Elmar |
| Abstract: | We develop models that take point forecasts from the Survey of Professional Forecasters (SPF) as inputs and produce estimates of survey-consistent term structures of expectations and uncertainty at arbitrary forecast horizons. Our models combine fixed-horizon and fixed-event forecasts, accommodating time-varying horizons and availability of survey data, as well as potential inefficiencies in survey forecasts. The estimated term structures of SPF-consistent expectations are comparable in quality to the published, widely used short-horizon forecasts. Our estimates of time-varying forecast uncertainty reflect historical variations in realized errors of SPF point forecasts, and generate fan charts with reliable coverage rates. |
| Keywords: | Term structure of expectations, uncertainty, survey forecasts, fan charts |
| JEL: | E37 C53 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bubdps:305275 |
| By: | Iva Glišic (National Bank of Serbia) |
| Abstract: | The paper elaborates on machine and deep learning methods, as well as mixed data sampling regression models, used for GDP nowcasting. The aim is to select an adequate model that shows better performance on the data used. The paper provides an answer to the question of whether the use of deep learning methods can improve GDP nowcasting compared to traditional econometric methods, as well as whether the use of specific high-frequency indicators improves the quality of the models used. The paper examines the selection of adequate indicators – both official and those from alternative sources, presents the framework of mixed data sampling regression models and deep learning models used for nowcasting, and gives an assessment of two such models on the example of Serbian GDP. Serbia’s GDP was modelled for the period Q1 2016 – Q2 2023 and the end of the observed period (six quarters) was used for the forecast. Finally, two assessed models were compared – the mixed data sampling regression model and the LSTM neural network. A special focus is placed on ways to improve both models. The LSTM recurrent neural network model had a smaller forecast error, with the use of a combination of official and alternative (high-frequency) indicators, but the mixed data sampling regression model also proved to be a good tool for decision-makers, since its structure allows insight into the ongoing movements impacting GDP dynamics. The use of alternative indicators in nowcasting improved the projections through both presented models. |
| Keywords: | GDP, nowcasting, MIDAS, neural networks, high-frequency indicators |
| JEL: | C32 C45 C53 |
| Date: | 2024–03 |
| URL: | https://d.repec.org/n?u=RePEc:nsb:bilten:22 |