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on Forecasting |
| By: | Andrea Carriero; Todd E. Clark; Massimiliano Marcellino; Elmar Mertens |
| Abstract: | Incoming data in 2020 posed sizable challenges for the use of VARs in economic analysis: Enormous movements in a number of series have had strong effects on parameters and forecasts constructed with standard VAR methods. We propose the use of VAR models with time-varying volatility that include a treatment of the COVID extremes as outlier observations. Typical VARs with time-varying volatility assume changes in uncertainty to be highly persistent. Instead, we adopt an outlier-adjusted stochastic volatility (SV) model for VAR residuals that combines transitory and persistent changes in volatility. In addition, we consider the treatment of outliers as missing data. Evaluating forecast performance over the last few decades in quasi-real time, we find that the outlier-augmented SV scheme does at least as well as a conventional SV model, while both outperform standard homoskedastic VARs. Point forecasts made in 2020 from heteroskedastic VARs are much less sensitive to outliers in the data, and the outlier-adjusted SV model generates more reasonable gauges of forecast uncertainty than a standard SV model. At least pre-COVID, a close alternative to the outlier-adjusted model is an SV model with t-distributed shocks. Treating outliers as missing data also generates better-behaved forecasts than the conventional SV model. However, since uncertainty about the incidence of outliers is ignored in that approach, it leads to strikingly tight predictive densities. |
| Keywords: | Bayesian VARs; stochastic volatility; outliers; pandemics; forecasts |
| JEL: | C53 E17 E37 F47 |
| Date: | 2021–02–02 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedcwq:89757&r=all |
| By: | Ding, Y. |
| Abstract: | We propose a model that extends Smetanina's (2017) original RT-GARCH model by allowing conditional heteroskedasticity in the variance of volatility process. We show we are able to filter and forecast both volatility and volatility of volatility simultaneously in this simple setting. The volatility forecast function follows a second-order difference equation as opposed to first-order under GARCH(1,1) and RT-GARCH(1,1). Empirical studies confirm the presence of conditional heteroskedasticity in the volatility process and the standardised residuals of return are close to Gaussian under this model. We show we are able to obtain better in-sample nowcast and out-of-sample forecast of volatility. |
| Keywords: | GARCH, diffusion limit, forecasting, volatility of volatility |
| JEL: | C22 C32 C53 C58 |
| Date: | 2021–02–16 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:2112&r=all |
| By: | Jonathan Benchimol (Bank of Israel); Makram El-Shagi (Henan University); Yossi Saadon (Bank of Israel) |
| Abstract: | Each person's characteristics may influence that person's behaviors and their outcomes. We build and use a new database to estimate experts' performance and boldness based on their experience and characteristics. We classify experts providing inflation forecasts based on their education, experience, gender, and environment. We provide alternative interpretations of factors affecting experts' inflation forecasting performance, boldness, and pessimism by linking behavioral economics, the economics of education, and forecasting literature. An expert with previous experience at a central bank appears to have a lower propensity for predicting deflation. |
| Keywords: | expert forecast, behavioral economics, survival analysis, panel estimation, global financial crisis |
| JEL: | C53 E37 E70 |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:boi:wpaper:2020.11&r=all |
| By: | Yoonseok Lee (Center for Policy Research, Maxwell School, Syracuse University, 426 Eggers Hall, Syracuse, NY 13244); Donggyu Sul (Department of Economics, University of Texas at Dallas) |
| Abstract: | We develop a novel forecast combination based on the order statistics of individual predictability when many forecasts are available. To this end, we define the notion of forecast depth, which measures the size of forecast errors during the training period and provides a ranking among different forecast models. The forecast combination is in the form of a depth-weighted trimmed mean, where the group of models with the worst forecasting performance during the training period is dropped. We derive the limiting distribution of the depth-weighted forecast combination, based on which we can readily construct forecast confidence intervals. Using this novel forecast combination, we forecast the national level of new COVID-19 cases in the U.S. and compare it with other approaches including the ensemble forecast from the Centers for Disease Control and Prevention. We find that the depth-weighted forecast combination yields more accurate predictions compared with other forecast combinations. |
| Keywords: | Forecast Combination, Forecast depth, Depth-weighted trimmed mean, COVID-19 |
| JEL: | C32 C53 |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:max:cprwps:238&r=all |
| By: | Fischer, Manfred M.; Hauzenberger, Niko; Huber, Florian; Pfarrhofer, Michael |
| Abstract: | Time-varying parameter (TVP) regressions commonly assume that time-variation in the coefficients is determined by a simple stochastic process such as a random walk. While such models are capable of capturing a wide range of dynamic patterns, the true nature of time variation might stem from other sources, or arise from different laws of motion. In this paper, we propose a flexible TVP VAR that assumes the TVPs to depend on a panel of partially latent covariates. The latent part of these covariates differ in their state dynamics and thus capture smoothly evolving or abruptly changing coefficients. To determine which of these covariates are important, and thus to decide on the appropriate state evolution, we introduce Bayesian shrinkage priors to perform model selection. As an empirical application, we forecast the US term structure of interest rates and show that our approach performs well relative to a set of competing models. We then show how the model can be used to explain structural breaks in coefficients related to the US yield curve. |
| Keywords: | Bayesian shrinkage, interest rate forecasting, latent effect modifers, MCMC sampling, time-varying parameter regression |
| Date: | 2021–02–22 |
| URL: | http://d.repec.org/n?u=RePEc:wiw:wus046:8006&r=all |
| By: | Zhen Zeng; Tucker Balch; Manuela Veloso |
| Abstract: | Time series forecasting is essential for decision making in many domains. In this work, we address the challenge of predicting prices evolution among multiple potentially interacting financial assets. A solution to this problem has obvious importance for governments, banks, and investors. Statistical methods such as Auto Regressive Integrated Moving Average (ARIMA) are widely applied to these problems. In this paper, we propose to approach economic time series forecasting of multiple financial assets in a novel way via video prediction. Given past prices of multiple potentially interacting financial assets, we aim to predict the prices evolution in the future. Instead of treating the snapshot of prices at each time point as a vector, we spatially layout these prices in 2D as an image, such that we can harness the power of CNNs in learning a latent representation for these financial assets. Thus, the history of these prices becomes a sequence of images, and our goal becomes predicting future images. We build on a state-of-the-art video prediction method for forecasting future images. Our experiments involve the prediction task of the price evolution of nine financial assets traded in U.S. stock markets. The proposed method outperforms baselines including ARIMA, Prophet, and variations of the proposed method, demonstrating the benefits of harnessing the power of CNNs in the problem of economic time series forecasting. |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2102.12061&r=all |
| By: | Carlos Medel |
| Abstract: | Despite that the Brazilian economy is a small-open economy to the world's eye, it is still the largest of South America and, thus, it acts as a big source of financial and macroeconomic spillovers to its trading partners abroad in a number of macro-financial variables including inflationary shocks. Consequently, a comprehensive but parsimonious inflation rate modelling yields important advantages. In this line, the aim of this article is threefold. First, to document if the Brazilian inflation follows the Hybrid New Keynesian Phillips Curve (HNKPC) model, tested by econometric means. Second, to extend the scope of the HNKPC from a close- to an open-economy version through a Global Vector Autoregression (GVAR) specification; aiming to quantify the influence of trading partners in forecast accuracy. Third, to compare the multi-horizon predictive ability of the HNKPC in such a way as to identify the predictive gain (or loss) provided by the trading partners and discriminate between them. The HNKPC forecasts are evaluated in a traditional way and compared with several robustness specifications and country-weighting schemes with the GVAR version. The in-sample results do not reject the baseline hypothesis posed by the HNKPC for the Brazilian economy and its main trading partners. However, to a large extent, the evidence favouring the HNKPC at the end of sample plainly weakens. In predictive terms, the results show that the proposed open-economy version of the HNKPC is the best predictive device for Brazilian inflation in a compacted form in the long run. Notably, the euro area and Japan contribute most to forecast accuracy despite the use of a distance-based weighting scheme favouring closer South American trading partners such as Argentina and Chile. |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:chb:bcchwp:900&r=all |