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on Econometric Time Series |
| By: | Degui Li (Faculty of Business Administration, University of Macau); Bin Peng (Department of Econometrics and Business Statistics, Monash University in Australia); Songqiao Tang (School of Mathematical Sciences, Zhejiang University); Weibiao Wu (Department of Statistics, University of Chicago) |
| Abstract: | This paper introduces a flexible time-varying network vector autoregressive model framework for large-scale time series. A latent group structure is imposed on the heterogeneous and node-specific time-varying momentum and network spillover effects so that the number of unknown time-varying coefficients to be estimated can be reduced considerably. A classic agglomerative clustering algorithm with nonparametrically estimated distance matrix is combined with a ratio criterion to consistently estimate the latent group number and membership. A post-grouping local linear smoothing method is proposed to estimate the group-specific time-varying momentum and network effects, substantially improving the convergence rates of the preliminary estimates which ignore the latent structure. We further modify the methodology and theory to allow for structural breaks in either the group membership, group number or group-specific coefficient functions. Numerical studies including Monte-Carlo simulation and an empirical application are presented to examine the finite-sample performance of the developed model and methodology. |
| Keywords: | cluster analysis, network VAR, latent groups, local linear estimator, time-varying coefficients |
| JEL: | C14 C32 C55 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:boa:wpaper:202526 |
| By: | Xiaohu Wang (School of Economics, Fudan University, Shanghai, China); Weilin Xiao (School of Management, Zhejiang University, Hangzhou, 310058, China); Jun Yu (Faculty of Business Administration, University of Macau, Macao, China); Chen Zhang (Faculty of Business Administration, University of Macau, Macao, China) |
| Abstract: | This paper first derives two analytic formulae for the autocovariance of the discretely sampled fractional Ornstein-Uhlenbeck (fOU) process. Utilizing the analytic formulae, two main applications are demonstrated: (i) investigation of the accuracy of the likelihood approximation by the Whittle method; (ii) the optimal forecasts with fOU based on discretely sampled data. The finite sample performance of the Whittle method and the derived analytic formula motivate us to introduce a feasible exact maximum likelihood (ML) method to estimate the fOU process. The long-span asymptotic theory of the ML estimator is established, where the convergence rate is a smooth function of the Hurst parameter (i.e., H) and the limiting distribution is always Gaussian, facilitating statistical inference. The asymptotic theory is different from that of some existing estimators studied in the literature, which are discontinuous at H = 3/4 and involve non-standard limiting distributions. The simulation results indicate that the ML method provides more accurate parameter estimates than all the existing methods, and the proposed optimal forecast formula offers a more precise forecast than the existing formula. The fOU process is applied to fit daily realized volatility (RV) and daily trading volume series. When forecasting RVs, it is found that the forecasts generated using the optimal forecast formula together with the ML estimates outperform those generated from all possible combinations of alternative estimation methods and alternative forecast formula. |
| Keywords: | Fractional Ornstein-Uhlenbeck process; Hurst parameter; Out-of-sample forecast; Maximum likelihood; Whittle likelihood; Composite likelihood |
| JEL: | C15 C22 C32 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:boa:wpaper:202527 |
| By: | Grzegorz Wesołowski (University of Warsaw, Faculty of Economic Sciences); Oleg Gurshev (University of Warsaw, Faculty of Economic Sciences) |
| Abstract: | This paper demonstrates that key macroeconomic shocks originating in the United States contribute significantly to business cycle synchronization between the US and other economies. These shocks also account for a substantial part of output fluctuations in these economies. Using panel local projection regressions with small sample refinements, we find that six major US shocks explain 21% - 28% of the forecast error variance in the GDP of open economies over a three-year horizon. Considering individual shock contributions, we document that technology and monetary policy innovations are of the highest relevance. |
| Keywords: | Macroeconomic shocks, International spillovers, International business cycles, Technology shocks, Monetary policy, Financial shocks, Fiscal policy, Investment shocks |
| JEL: | E23 E32 E52 E62 F44 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:war:wpaper:2025-06 |
| By: | Luca Benati |
| Abstract: | I use Bayesian VARs with stochastic volatility to forecast global temperatures and sea level and ice cover in the Northern hemisphere until 2100, by exploiting (i) their long-run equilibrium relationship with climate change drivers (CCDs) and (ii) the relationship between world GDP and anthropogenic CCDs. Assuming that trend GDP growth will remain unchanged after 2024, and the world economy will fully decarbonize by 2050, global temperatures and sea level are projected to increase by 2.3 Celsius degrees and 38 centimeters respectively compared to pre-industrial times. Further, uncertainty is substantial, pointing to significant upward risks. Because of this, bringing climate change under control will require massive programme of carbon removal from the atmosphere, in order to bring anthropogenic CCDs back to the levels of the end of the XX century. |
| Keywords: | Climate Change; Bayesian VARs; stochastic volatility; cointegration; forecasting; conditional forecasts |
| JEL: | E2 E3 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:ube:dpvwib:dp2503 |