Forecasting
http://lists.repec.orgmailman/listinfo/nep-for
Forecasting
2017-05-07
Improving Forecast Accuracy of Financial Vulnerability: PLS Factor Model Approach
http://d.repec.org/n?u=RePEc:abn:wpaper:auwp2017-03&r=for
We present a factor augmented forecasting model for assessing the financial vulnerability in Korea. Dynamic factor models often extract latent common factors from a large panel of time series data via the method of the principal components (PC). Instead, we employ the partial least squares (PLS) method that estimates target specific common factors, utilizing covariances between predictors and the target variable. Applying PLS to 198 monthly frequency macroeconomic time series variables and the Bank of Korea's Financial Stress Index (KFSTI), our PLS factor augmented forecasting models consistently outperformed the random walk benchmark model in out-of-sample prediction exercises in all forecast horizons we considered. Our models also outperformed the autoregressive benchmark model in short-term forecast horizons. We expect our models would provide useful early warning signs of the emergence of systemic risks in Korea's financial markets.
Hyeongwoo Kim
Kyunghwan Ko
Partial Least Squares; Principal Component Analysis; Financial Stress Index; Out-of-Sample Forecast; RRMSPE
2017-05
A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes
http://d.repec.org/n?u=RePEc:arx:papers:1705.00891&r=for
In this paper we use Gaussian Process (GP) regression to propose a novel approach for predicting volatility of financial returns by forecasting the envelopes of the time series. We provide a direct comparison of their performance to traditional approaches such as GARCH. We compare the forecasting power of three approaches: GP regression on the absolute and squared returns; regression on the envelope of the returns and the absolute returns; and regression on the envelope of the negative and positive returns separately. We use a maximum a posteriori estimate with a Gaussian prior to determine our hyperparameters. We also test the effect of hyperparameter updating at each forecasting step. We use our approaches to forecast out-of-sample volatility of four currency pairs over a 2 year period, at half-hourly intervals. From three kernels, we select the kernel giving the best performance for our data. We use two published accuracy measures and four statistical loss functions to evaluate the forecasting ability of GARCH vs GPs. In mean squared error the GP's perform 20% better than a random walk model, and 50% better than GARCH for the same data.
Syed Ali Asad Rizvi
Stephen J. Roberts
Michael A. Osborne
Favour Nyikosa
2017-05
Forecasting with many predictors using message passing algorithms
http://d.repec.org/n?u=RePEc:esy:uefcwp:19565&r=for
Machine learning methods are becoming increasingly popular in economics, due to the increased availability of large datasets. In this paper I evaluate a recently proposed algorithm called Generalized Approximate Message Passing (GAMP) , which has been very popular in signal processing and compressive sensing. I show how this algorithm can be combined with Bayesian hierarchical shrinkage priors typically used in economic forecasting, resulting in computationally efficient schemes for estimating high-dimensional regression models. Using Monte Carlo simulations I establish that in certain scenarios GAMP can achieve estimation accuracy comparable to traditional Markov chain Monte Carlo methods, at a tiny fraction of the computing time. In a forecasting exercise involving a large set of orthogonal macroeconomic predictors, I show that Bayesian shrinkage estimators based on GAMP perform very well compared to a large set of alternatives.
Korobilis, Dimitris
high-dimensional inference; compressive sensing; belief propagation; Bayesian shrinkage; dynamic factor models
2017-01
Short-Term Forecasting Analysis for Municipal Water Demand
http://d.repec.org/n?u=RePEc:pra:mprapa:78259&r=for
Short-term water demand forecasts inform decisions regarding budgeting, rate design, water supply system operations, and effective implementation of conservation policies. This study develops a Linear Transfer Function (LTF) forecasting model for El Paso, Texas, a growing city located in the desert Southwest region of the United States. The model is used to generate monthly-frequency out-of-sample simulations of water demand for periods when actual demand is known. To measure the accuracy of the LTF projections against viable alternatives, a set of benchmark forecasts is also developed. Both descriptive accuracy metrics and formal statistical tests are used to analyze predictive performance. The LTF model outperforms the alternatives in predicting demand per customer but falls a little short in projecting growth in the customer base. Changes in climatic and economic conditions are found to impact consumption per customer more rapidly than changes in water rates.
Fullerton, Thomas M., Jr.
Ceballos, Alejandro
Walke, Adam G.
Water demand models; water conservation; forecast accuracy
2015-06-26
Reading between the Lines: Using Media to Improve German Inflation Forecasts
http://d.repec.org/n?u=RePEc:diw:diwwpp:dp1665&r=for
In this paper, we examine the predictive ability of automatic and expert-rated media sentiment indicators for German inflation. We find that sentiment indicators are competitive in providing inflation forecasts against a large set of common macroeconomic and financial predictors. Sophisticated linguistic sentiment algorithms and business cycle news rated by experts perform best and are superior to simple word-count indicators and autoregressive forecasts.
Benjamin Beckers
Konstantin A. Kholodilin
Dirk Ulbricht
Inflation prediction, media sentiment indicators, news reports, real-time forecasting
2017
Forecasting with FAVAR: macroeconomic versus financial factors
http://d.repec.org/n?u=RePEc:nbp:nbpmis:256&r=for
We assess the predictive power of macroeconomic and financial latent factors on the key variables for the US economy before and after the recent Great Recession. We implement a forecasting horserace among Factor Augmented VAR (FAVAR), Classical, and Bayesian VAR models. FAVAR models outperform others. Focusing only on macroeconomic or on nancial latent factors,we nd how the nancial variables have not a driver role in forecasting the US economy including the Great Recession.
Alessia Paccagnini
Factor Models, Factor Augmented VAR, VAR models, Bayesian VAR models,Forecasting
2017
Economic Forecasting Based on the Relationship between GDP and Real Money Supply
http://d.repec.org/n?u=RePEc:pra:mprapa:78717&r=for
Forecasts showing how the economy will be developing are very important both for the government and for all the economic agents, including citizens. In Russia, the common practice is to forecast based on price assumptions for hydrocarbons, primarily oil. Such an approach causes serious errors. This paper proposes a different approach driven by the close linkage between the GDP and the real money supply. By way of an example, forecast scenarios for Russia’s GDP in 2017 are adduced. Options for using the proposed methodology in economic policy (including anti-crisis policy) are suggested
BLINOV, Sergey
Monetary Policy; Business Cycles; Forecasting and Prediction Methods; Energy and the Macroeconomy
2017-04-22
Dynamic term structure models with score-driven time-varying parameters: estimation and forecasting
http://d.repec.org/n?u=RePEc:nbp:nbpmis:258&r=for
We consider score-driven time-varying parameters in dynamic yield curve models and investigate their in-sample and out-of-sample performance for two data sets. In a univariate setting, score-driven models were shown to offer competitive performance to parameter-driven models in terms of in-sample fit and quality of out-of-sample forecasts but at a lower computational cost. We investigate whether this performance and the related advantages extend to more general and higher-dimensional models. Based on an extensive Monte Carlo study, we show that in multivariate settings the advantages of score-driven models can even be more pronounced than in the univariate setting. We also show how the score-driven approach can be implemented in dynamic yield curve models and extend them to allow for the fat-tailed distributions of the disturbances and the time-variation of variances (heteroskedasticity) and covariances.
Siem Koopman
André Lucas
Marcin Zamojski
term-structure, dynamic Nelson-Siegel models, non-Gaussian distributions, time-varying parameters, observation-driven models, parameter-driven models
2017