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on Forecasting |
| By: | Christiane Baumeister; Pierre Guérin |
| Abstract: | This paper evaluates the predictive content of a set of alternative monthly indicators of global economic activity for nowcasting and forecasting quarterly world GDP using mixed-frequency models. We find that a recently proposed indicator that covers multiple dimensions of the global economy consistently produces substantial improvements in forecast accuracy, while other monthly measures have more mixed success. This global economic conditions indicator contains valuable information also for assessing the current and future state of the economy for a set of individual countries and groups of countries. We use this indicator to track the evolution of the nowcasts for the US, the OECD area, and the world economy during the coronavirus pandemic and quantify the main factors driving the nowcasts. |
| JEL: | C22 C52 E37 |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:28014&r=all |
| By: | Capek, Jan; Crespo Cuaresma, Jesus; Hauzenberger, Niko; Reichel, Vlastimil |
| Abstract: | We provide a comprehensive assessment of the predictive ability of combinations of Dynamic Stochastic General Equilibrium (DSGE) models for GDP growth, inflation and the interest rate in the euro area. We employ a battery of static and dynamic pooling weights based on Bayesian model averaging principles, prediction pools and dynamic factor representations, and entertain eight different DSGE specifications and four prediction weighting schemes. Our results indicate that exploiting mixtures of DSGE models tends to achieve superior forecasting performance over individual specifications for both point and density forecasts. The largest improvements in the accuracy of GDP growth forecasts are achieved by the prediction pooling technique, while the results for the weighting method based on dynamic factors partly leads to improvements in the quality of inflation and interest rate predictions. |
| Keywords: | Forecasting, model averaging, prediction pooling, DSGE models |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:wiw:wus005:7849&r=all |
| By: | Jan Capek (Masaryk University); Jesus Crespo Cuaresma (Department of Economics, Vienna University of Economics and Business); Niko Hauzenberger (University of Salzburg); Vlastimil Reichel (Masaryk University) |
| Abstract: | We provide a comprehensive assessment of the predictive ability of combinations of Dynamic Stochastic General Equilibrium (DSGE) models for GDP growth, inflation and the interest rate in the euro area. We employ a battery of static and dynamic pooling weights based on Bayesian model averaging principles, prediction pools and dynamic factor representations, and entertain eight different DSGE specifications and four prediction weighting schemes. Our results indicate that exploiting mixtures of DSGE models tends to achieve superior forecasting performance over individual specifications for both point and density forecasts. The largest improvements in the accuracy of GDP growth forecasts are achieved by the prediction pooling technique, while the results for the weighting method based on dynamic factors partly leads to improvements in the quality of inflation and interest rate predictions. |
| Keywords: | Forecasting, model averaging, prediction pooling, DSGE models |
| JEL: | E37 E47 C53 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:wiw:wiwwuw:wuwp305&r=all |
| By: | John Cotter (Michael Smurfit Graduate Business School, University College Dublin); Emmanuel Eyiah-Donkor (Rennes School of Business); Valerio Potí (Michael Smurfit Graduate Business School, University College Dublin) |
| Abstract: | We find out-of-sample predictability of commodity futures excess returns using forecast combinations of 28 potential predictors. Such gains in forecast accuracy translate into economically significant improvements in certainty equivalent returns and Sharpe ratios for a mean-variance investor. Commodity return forecasts are closely linked to the real economy. Return predictability is countercyclical, and the combination forecasts of commodity returns have significantly positive predictive power for future economic activity. Two-factor models featuring innovations in each of the combination forecasts and the market factor explain a substantial proportion of the cross-sectional variation of commodity and equity returns. The associated positive risk prices are consistent with the Intertemporal Capital Asset Pricing Model (ICAPM) of Merton (1973), given how the predictors forecast an increase in future economic activity in the time-series. Overall, combination fore- casts act as state variables within the ICAPM, thus resurrecting a central role for macroeconomic risk in determining expected returns. |
| Keywords: | Commodity futures returns; Predictability; Asset allocation; Macroeconomic risk; Intertemporal pricing |
| JEL: | G10 G12 G15 |
| Date: | 2020–11–12 |
| URL: | http://d.repec.org/n?u=RePEc:ucd:wpaper:202011&r=all |
| By: | Behrens, Christoph |
| Abstract: | I evaluate German export growth and import growth forecasts published by eight professional forecasters for the years 1971 to 2019. The focus of the evaluation is on the weak and strong efficiency as well as the unbiasedness of the forecasts. To this end, I use a novel panel-data set and estimate fixed-effects models taking into account panel-corrected standard errors. For the full time period, I find that both export and import growth forecasts are weakly but not strongly efficient. Unbiasedness depends on the forecast horizon being analyzed, with longer-term four-quarter-ahead forecasts being biased. I, furthermore, check for a possible change in forecasting behavior after incisive economic events in recent German history. I find that the strong efficiency of the forecasts did not change substantially over time. However, there is a change in forecasting behavior regarding the weak form of efficiency after the financial crisis 2008/2009. |
| Keywords: | Trade forecasts,German economic research institutes,Forecast Efficiency,Panel Data |
| JEL: | C53 F17 F47 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:pp1859:26&r=all |
| By: | Ooft, Gavin (The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise) |
| Abstract: | An accurate forecast for inflation is mandatory in the conduction of monetary policy. This paper presents models that forecast monthly inflation utilizing various economic techniques for the economy of Suriname. The paper employs Autoregressive Integrated Moving Average models (ARIMA), Vector Autoregressive models (VAR), Factor Augmented Vector Autoregressive models (FAVAR), Bayesian Vector Autoregressive models (BVAR) and Vector Error Correction (VECM) models to model monthly inflation for Suriname over the period from 2004 to 2018. Consequently, the forecast performance of the models is evaluated by comparison of the Root Mean Square Error and the Mean Average Errors. We also conduct a pseudo out-of-sample forecasting exercise. The VECM yields the best results forecasting up to three months ahead, while thereafter, the FAVAR, which includes more economic information, outperforms the VECM, based on the assessment of the pseudo out-of-sample forecast performance of the models. |
| Keywords: | Inflation; Forecasting; Time-Series Models; Suriname |
| JEL: | C32 E31 |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:ris:jhisae:0144&r=all |
| By: | Bo Zhang; Jamie Cross; Na Guo |
| Abstract: | We investigate whether a class of trend models with various error term structures can improve upon the forecast performance of commonly used time series models when forecasting CPI inflation in Australia. The main result is that trend models tend to provide more accurate point and density forecasts compared to conventional autoregressive and Phillips curve models. The best short term forecasts come from a trend model with stochastic volatility in the transitory component, while medium to long-run forecasts are better made by specifying a moving average component. We also find that trend models can capture various dynamics in periods of significance which conventional models can not. This includes the dramatic reduction in inflation when the RBA adopted inflation targeting, the a one-off 10 per cent Goods and Services Tax inflationary episode in 2000, and the gradually decline in inflation since 2014. |
| Keywords: | trend model, inflation forecast, Bayesian analysis, stochastic volatility |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:bny:wpaper:0092&r=all |
| By: | Francisco Blasques (Vrije Universiteit Amsterdam); Meindert Heres Hoogerkamp (Dutch Ministry of Education, Culture and Science); Siem Jan Koopman (Vrije Universiteit Amsterdam); Ilka van de Werve (Vrije Universiteit Amsterdam) |
| Abstract: | We propose a dynamic factor model which we use to analyze the relationship between education participation and national unemployment, as well as to forecast the number of students across the many different types of education. By clustering the factor loadings associated with the dynamic macroeconomic factor, we can measure to what extent the different types of education exhibit similarities in their relationship with macroeconomic cycles. Since unemployment data is available for a longer time period than our detailed education data panel, we propose a twostep estimation procedure. First, we consider a score-driven model which filters the conditional expectation of the unemployment rate. Second, we consider a multivariate regression model for the number of students featuring the dynamic macroeconomic factor as a regressor, and we further apply the k-means method to estimate the clustered loading matrix. In a Monte Carlo study we analyze the performance of the proposed procedure in its ability to accurately capture clusters and preserve or enhance forecasting accuracy. For a high-dimensional, nation-wide data set from The Netherlands, we empirically investigate the impact of the rate of unemployment on choices in education over time. Our analysis confirms that the number of students in part-time education covaries more strongly with unemployment than those in full-time education. |
| Keywords: | Dynamic Factor Models, Cluster Analysis, Forecasting, Education, Unemployment |
| JEL: | I25 C38 C53 |
| Date: | 2020–11–17 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20200078&r=all |
| By: | Sidra Mehtab; Jaydip Sen; Subhasis Dasgupta |
| Abstract: | Prediction of stock price and stock price movement patterns has always been a critical area of research. While the well-known efficient market hypothesis rules out any possibility of accurate prediction of stock prices, there are formal propositions in the literature demonstrating accurate modeling of the predictive systems can enable us to predict stock prices with a very high level of accuracy. In this paper, we present a suite of deep learning-based regression models that yields a very high level of accuracy in stock price prediction. To build our predictive models, we use the historical stock price data of a well-known company listed in the National Stock Exchange (NSE) of India during the period December 31, 2012 to January 9, 2015. The stock prices are recorded at five minutes interval of time during each working day in a week. Using these extremely granular stock price data, we build four convolutional neural network (CNN) and five long- and short-term memory (LSTM)-based deep learning models for accurate forecasting of future stock prices. We provide detailed results on the forecasting accuracies of all our proposed models based on their execution time and their root mean square error (RMSE) values. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.08011&r=all |
| By: | Deepanshu Sharma; Kritika Phulli |
| Abstract: | The advancement in the field of statistical methodologies to economic data has paved its path towards the dire need for designing efficient military management policies. India is ranked as the third largest country in terms of military spender for the year 2019. Therefore, this study aims at utilizing the Box-Jenkins ARIMA model for time series forecasting of the military expenditure of India in forthcoming times. The model was generated on the SIPRI dataset of Indian military expenditure of 60 years from the year 1960 to 2019. The trend was analysed for the generation of the model that best fitted the forecasting. The study highlights the minimum AIC value and involves ADF testing (Augmented Dickey-Fuller) to transform expenditure data into stationary form for model generation. It also focused on plotting the residual error distribution for efficient forecasting. This research proposed an ARIMA (0,1,6) model for optimal forecasting of military expenditure of India with an accuracy of 95.7%. The model, thus, acts as a Moving Average (MA) model and predicts the steady-state exponential growth of 36.94% in military expenditure of India by 2024. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.06060&r=all |
| By: | Shao, Yongtong; Xiong, Tao; Li, Minghao; Hayes, Dermot; Zhang, Wendong; Xie, Wei |
| Abstract: | Small sample size often limits forecasting tasks such as the prediction of production, yield, and consumption of agricultural products. Machine learning offers an appealing alternative to traditional forecasting methods. In particular, Support Vector Regression has superior forecasting performance in small sample applications. In this article, we introduce Support Vector Regression via an application to China’s hog market. Since 2014, China’s hog inventory data has experienced an abnormal decline that contradicts price and consumption trends. We use Support Vector Regression to predict the true inventory based on the price-inventory relationship before 2014. We show that, in this application with a small sample size, Support Vector Regression out-performs neural networks, random forest, and linear regression. Predicted hog inventory decreased by 3.9% from November 2013 to September 2017, instead of the 25.4% decrease in the reported data. |
| Date: | 2020–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:isu:genstf:202001010800001619&r=all |
| By: | Westler, Richard; Spacek, John |
| Keywords: | Public Economics |
| Date: | 2020–10–22 |
| URL: | http://d.repec.org/n?u=RePEc:ags:cantrf:305955&r=all |
| By: | Hamidreza Arian; Seyed Mohammad Sina Seyfi; Azin Sharifi |
| Abstract: | Credit scoring is an essential tool used by global financial institutions and credit lenders for financial decision making. In this paper, we introduce a new method based on Gaussian Mixture Model (GMM) to forecast the probability of default for individual loan applicants. Clustering similar customers with each other, our model associates a probability of being healthy to each group. In addition, our GMM-based model probabilistically associates individual samples to clusters, and then estimates the probability of default for each individual based on how it relates to GMM clusters. We provide applications for risk managers and decision makers in banks and non-bank financial institutions to maximize profit and mitigate the expected loss by giving loans to those who have a probability of default below a decision threshold. Our model has a number of advantages. First, it gives a probabilistic view of credit standing for each individual applicant instead of a binary classification and therefore provides more information for financial decision makers. Second, the expected loss on the train set calculated by our GMM-based default probabilities is very close to the actual loss, and third, our approach is computationally efficient. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.07906&r=all |