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on Econometric Time Series |
| By: | Niko Hauzenberger; Florian Huber; Karin Klieber; Massimiliano Marcellino |
| Abstract: | We develop Bayesian neural networks (BNNs) that permit to model generic nonlinearities and time variation for (possibly large sets of) macroeconomic and financial variables. From a methodological point of view, we allow for a general specification of networks that can be applied to either dense or sparse datasets, and combines various activation functions, a possibly very large number of neurons, and stochastic volatility (SV) for the error term. From a computational point of view, we develop fast and efficient estimation algorithms for the general BNNs we introduce. From an empirical point of view, we show both with simulated data and with a set of common macro and financial applications that our BNNs can be of practical use, particularly so for observations in the tails of the cross-sectional or time series distributions of the target variables. |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2211.04752&r=ets |
| By: | Petre, Konstantin; Varoutas, Dimitris |
| Abstract: | Over the past few decades, a large number of research papers has published focused on forecasting ICT products using various diffusion models like logistic, Gompertz, Bass, etc. Much less research work has been done towards the application of time series forecasting in ICT such as ARIMA model which seems to be an attractive alternative. More recently with the advancement in computational power, machine learning and artificial intelligence have become popular due to superior performance than classical models in many areas of concern. In this paper, broadband penetration is analysed separately for all OECD countries, trying to figure out which model is superior in most cases and phases in time. Although diffusion models are dedicated for this purpose, the ARIMA model has nevertheless shown an enormous influence as a good alternative in many previous works. In this study, a new approach using LSTM networks stands out to be a promising method for projecting high technology innovations diffusion. |
| Keywords: | Diffusion models,ARIMA,LSTM,broadband penetration forecasting |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:itse22:265665&r=ets |
| By: | Claudio Morana (Center for European Studies, University of Milano-Bicocca, Italy; Rimini Centre for Economic Analysis; CeRP, Collegio Carlo Alberto, Italy; CES, Harvard, USA) |
| Abstract: | This paper introduces a new decomposition of euro area headline inflation into core, cyclical and residual components. Our new core inflation measure, the structural core inflation rate, bears the interpretation of expected headline inflation, conditional to medium to long-term demand and supply-side developments. It shows smoothness and trending properties, economic content, and forecasting ability for headline inflation and other available core inflation measures routinely used at the ECB for internal or external communication. Hence, it carries additional helpful information for policy-making decisions. Concerning recent developments, all the inflation components contributed to its post-pandemic upsurge. Since mid-2021, core inflation has been on a downward trend, landing at about 3% in 2022. Cyclical and residual inflation -associated with idiosyncratic supply chains, energy markets, and geopolitical tensions- are currently the major threats to price stability. While some cyclical stabilization is ongoing, a stagflation scenario cum weakening overall financial conditions might be lurking ahead. A pressing issue for ECB monetary policy will be to face -mostly supply-side- inflationary pressure without triggering a financial crisis. |
| Keywords: | headline inflation, core inflation, Russia's war in Ukraine, COVID-19 pandemic, sovereign debt crisis, subprime financial crisis, dot-com bubble, euro area, ECB monetary policy, trend-cycle decomposition |
| JEL: | C22 C38 E32 F44 G01 |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:22-14&r=ets |