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on Econometric Time Series |
| By: | NAKAJIMA, Jouchi |
| Abstract: | This study discusses a general approach to dynamic modeling using the local projection (LP) method. Previous studies have proposed time-varying (TV) parameters in LPs; however, they did not address possible variations in error variances. Overlooking this could introduce significant bias in the estimate of the TV parameter, and consequently, the estimated impulse response. We develop an estimation strategy for LPs with stochastic volatility (SV) and illustrate the importance of SV inclusion using simulated data. Application to a topical macroeconomic time-series analysis illustrates the benefits of the proposed approach in terms of improved predictions. |
| Keywords: | Local projections, Time-varying parameters, Stochastic volatility |
| JEL: | C15 C22 C53 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:hit:hituec:761 |
| By: | Yufei Li (King's Business School, King's College London,); Liudas Giraitis (School of Economics and Finance, Queen Mary University of London); Genaro Sucarrat (BI Norwegian Business School) |
| Abstract: | The presence of autocorrelated nancial returns has major implications for investment decisions.Unsurprisingly, therefore, numerous studies have sought to shed light on whether returns areautocorrelated or not, to what extent, and when. Standard tests for autocorrelation rely onthe assumption of strict stationarity of returns, possibly after a suitable transformation. Recentstudies, however, reveal that intraday nancial returns are often characterised by a subtle formof non-stationarity that cannot be transformed away, namely non-stationary periodicity in thezero-process. Here, we propose tests for autocorrelation that are valid under this (and otherforms) of non-stationarity. The tests are simple to implement, and well-sized and powerful asdocumented in our Monte Carlo simulations. Next, in a study of the intraday returns of stocksand exchange rates, our robust tests document that returns are rarely autocorrelated. This is insharp contrast to the standard benchmark test, which spuriously detects a substantial numberof autocorrelations. Moreover, stability analyses with our robust tests suggest the signi canceof the autocorrelations is short-lived and very erratic. So it is unclear whether the short-livedautocorrelations can be used to inform decision-making. |
| Keywords: | robust correlation testing, zero-process, non-stationary periodicity |
| JEL: | C01 C12 C22 |
| Date: | 2024–02–26 |
| URL: | https://d.repec.org/n?u=RePEc:qmw:qmwecw:987 |
| By: | Temurbek Boymirzaev (Central Bank of Uzbekistan) |
| Abstract: | This study investigates the application of Factor-Augmented Vector Autoregression (FAVAR) and Bayesian Vector Autoregression (BVAR) models for inflation forecasting. FAVAR models deal with high-dimensional data by extracting latent factors from extensive macroeconomic indicators, while BVAR models incorporate prior distributions to enhance forecast stability and precision in data-limited environments. Employing a comprehensive dataset of Uzbekistan-specific inflation determinants, we conduct an empirical assessment of both models, examining their predictive accuracy. Findings from this research aim to optimize inflation forecasting methodologies, providing the Central Bank of Uzbekistan with robust, data-driven insights for improved policy formulation. |
| Keywords: | FAVAR; BVAR; inflation forecast; forecast combination |
| JEL: | E30 E31 E37 |
| Date: | 2025–02–27 |
| URL: | https://d.repec.org/n?u=RePEc:gii:giihei:heidwp06-2025 |