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on Forecasting |
| By: | Loermann, Julius; Maas, Benedikt |
| Abstract: | We use a machine learning approach to forecast the US GDP value of the current quarter and several quarters ahead. Within each quarter, the contemporaneous value of GDP growth is unavailable but can be estimated using higher-frequency variables that are published in a more timely manner. Using the monthly FRED-MD database, we compare the feedforward artificial neural network forecasts of GDP growth to forecasts of state of the art dynamic factor models and the Survey of Professional Forecasters, and we evaluate the relative performance. The results indicate that the neural network outperforms the dynamic factor model in terms of now- and forecasting, while it generates at least as good now- and forecasts as the Survey of Professional Forecasters. |
| Keywords: | Nowcasting; Machine learning; Neural networks; Big data |
| JEL: | C32 C53 E32 |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:95459&r=all |
| By: | Franses, Ph.H.B.F. |
| Abstract: | Many forecasting studies compare the forecast accuracy of new methods or models against a benchmark model. Often, this benchmark is the random walk model. In this note I argue that for various reasons an IMA(1,1) model is a better benchmark in many cases. |
| Keywords: | One-step-ahead forecasts, Benchmark model |
| JEL: | C53 |
| Date: | 2019–08–01 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureir:118657&r=all |
| By: | Franses, Ph.H.B.F. |
| Abstract: | Each month various professional forecasters give forecasts for next year's real GDP growth and many other variables. In terms of forecast updates, January is a special month, as then the forecast horizon moves to the following calendar year, and as such the observation is not a revision. Instead of deleting the January data when analyzing forecast updates, this paper proposes a periodic version of an often considered test regression, to explicitly include and model the January data. An application of this periodic model for many forecasts across a range of countries learns that apparently there is a January optimism effect. In fact, in January, GDP forecast updates are suddenly positive, and at the same time the forecast updates for unemployment are likewise negative. This optimism about the new year of the professional forecasters is however found to be detrimental to forecast accuracy. The main conclusion is that forecasts created in January for the next year need to be treated with care. |
| Keywords: | Professional forecasters, macroeconomic forecasting, weak-form efficiency, periodic, regression model, forecast updates, January effect |
| JEL: | C53 E27 E37 |
| Date: | 2019–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureir:118666&r=all |
| By: | Daniel Borup (Aarhus University and CREATES); Erik Christian Montes Schütte (Aarhus University and CREATES) |
| Abstract: | We show that Google search activity on relevant terms is a strong out-of-sample predictor for future employment growth in the US over the period 2004-2018 at both short and long horizons. Using a subset of ten keywords associated with “jobs”, we construct a large panel of 173 variables using Google’s own algorithms to find related search queries. We find that the best Google Trends model achieves an out-of-sample R2 between 26% and 59% at horizons spanning from one month to a year ahead, strongly outperforming benchmarks based on a large set of macroeconomic and financial predictors. This strong predictability extends to US state-level employment growth, using state-level specific Google search activity. Encompassing tests indicate that when the Google Trends panel is exploited using a non-linear model it fully encompasses the macroeconomic forecasts and provides significant information in excess of those. |
| Keywords: | Google Trends, Forecast comparison, US employment growth, Targeting predictors, Random forests, Keyword search. |
| JEL: | C22 C53 E17 E24 |
| Date: | 2019–08–22 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2019-13&r=all |
| By: | Bucci, Andrea |
| Abstract: | In the last few decades, a broad strand of literature in finance has implemented artificial neural networks as forecasting method. The major advantage of this approach is the possibility to approximate any linear and nonlinear behaviors without knowing the structure of the data generating process. This makes it suitable for forecasting time series which exhibit long memory and nonlinear dependencies, like conditional volatility. In this paper, I compare the predictive performance of feed-forward and recurrent neural networks (RNN), particularly focusing on the recently developed Long short-term memory (LSTM) network and NARX network, with traditional econometric approaches. The results show that recurrent neural networks are able to outperform all the traditional econometric methods. Additionally, capturing long-range dependence through Long short-term memory and NARX models seems to improve the forecasting accuracy also in a highly volatile framework. |
| Keywords: | Neural Networks; Realized Volatility; Forecast |
| JEL: | C22 C45 C53 G17 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:95443&r=all |
| By: | Jonathan Benchimol (Bank of Israel); Makram El-Shagi (Henan University, Kaifeng, China) |
| Abstract: | Abstract Governments, central banks, and private companies make extensive use of expert and market-based forecasts in their decision-making processes. These forecasts can be affected by terrorism, a factor that should be considered by decision-makers. We focus on terrorism as a mostly endogenously driven form of political uncertainty and assess the forecasting performance of market-based and professional inflation and exchange rate forecasts in Israel. We show that expert forecasts are better than market-based forecasts, particularly during periods of terrorism. However, the performance of both market-based and expert forecasts is significantly worse during such periods. Thus, policymakers should be particularly attentive to terrorism when considering inflation and exchange rate forecasts. |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:boi:wpaper:2019.08&r=all |
| By: | Shen, Ze; Wan, Qing; Leatham, David J. |
| Keywords: | Agribusiness |
| Date: | 2019–06–25 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aaea19:290696&r=all |
| By: | Fokin, Nikita; Polbin, Andrey |
| Abstract: | This paper estimates a bivariate econometric model to describe Russia’s real GDP while taking account of the Russian economy’s high dependence on oil prices, monetary policy regime change, and economic growth slowdown. We follow the theory of long-run neutrality of monetary policy and assume that the Bank of Russia’s monetary policy regime change in late 2014 has influenced only the short-run relationship between Russia’s GDP and oil prices, but long-run multiplier is invariant to monetary policy. The paper also attempts to take account of the economic growth slowdown in last decade. The model has demonstrated good forecasting performance. |
| Keywords: | monetary policy, Russian economy, terms of trade, ARX model, ECM model, structural breaks |
| JEL: | E32 E37 E52 |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:95306&r=all |
| By: | Fatima Zahra Azayite; Said Achchab |
| Abstract: | Predicting firm's failure is one of the most interesting subjects for investors and decision makers. In this paper, a bankruptcy prediction model is proposed based on Artificial Neural networks (ANN). Taking into consideration that the choice of variables to discriminate between bankrupt and non-bankrupt firms influences significantly the model's accuracy and considering the problem of local minima, we propose a hybrid ANN based on variables selection techniques. Moreover, we evolve the convergence of Particle Swarm Optimization (PSO) by proposing a training algorithm based on an improved PSO and Simulated Annealing. A comparative performance study is reported, and the proposed hybrid model shows a high performance and convergence in the context of missing data. |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1907.12179&r=all |
| By: | Zahra Saki; Lori Rothenberg; Marguerite Moor; Ivan Kandilov; A. Blanton Godfrey |
| Abstract: | To establish an updated understanding of the U.S. textile and apparel (TAP) industrys competitive position within the global textile environment, trade data from UN-COMTRADE (1996-2016) was used to calculate the Normalized Revealed Comparative Advantage (NRCA) index for 169 TAP categories at the four-digit Harmonized Schedule (HS) code level. Univariate time series using Autoregressive Integrated Moving Average (ARIMA) models forecast short-term future performance of Revealed categories with export advantage. Accompanying outlier analysis examined permanent level shifts that might convey important information about policy changes, influential drivers and random events. |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1908.04852&r=all |