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on Forecasting |
| By: | Tom Boot (Erasmus University Rotterdam, the Netherlands); Didier Nibbering (Erasmus University Rotterdam, the Netherlands) |
| Abstract: | Random subspace methods are a novel approach to obtain accurate forecasts in high-dimensional regression settings. We provide a theoretical justification of the use of random subspace methods and show their usefulness when forecasting monthly macroeconomic variables. We focus on two approaches. The first is random subset regression, where random subsets of predictors are used to construct a forecast. Second, we discuss random projection regression, where artificial predictors are formed by randomly weighting the original predictors. Using recent results from random matrix theory, we obtain a tight bound on the mean squared forecast error for both randomized methods. We identify settings in which one randomized method results in more precise forecasts than the other and than alternative regularization strategies, such as principal component regression, partial least squares, lasso, and ridge regression. The predictive accuracy on the high-dimensional macroeconomic FRED-MD data set increases substantially when using the randomized methods, with random subset regression outperforming any one of the above mentioned competing methods for at least 66\% of the series. |
| Keywords: | dimension reduction; random projections; random subset regression; principal components analysis; forecasting |
| JEL: | C32 C38 C53 C55 |
| Date: | 2016–09–06 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20160073&r=for |
| By: | Stelios D. Bekiros; Roberta Cardani; Alessia Paccagnini; Stefania Villa |
| Abstract: | In the dynamic stochastic general equilibrium (DSGE) literature there has been an increasing awareness on the role that the banking sector can play in macroeconomic activity. We present a DSGE model with financial intermediation as in Gertler and Karadi (2011). The estimation of shocks and of the structural parameters shows that time-variation should be crucial in any attempted empirical analysis. Since DSGE modelling usually fails to take into account inherent nonlinearities of the economy, we propose a novel time-varying parameter (TVP) state-space estimation method for VAR processes both for homoskedastic and heteroskedastic error structures. We conduct an exhaustive empirical exercise to compare the out-of-sample predictive performance of the estimated DSGE model with that of standard ARs, VARs, Bayesian VARs and TVP-VARs. We find that the TVP-VAR provides the best forecasting performance for the series of GDP and net worth of financial intermediaries for all steps-ahead, while the DSGE model outperforms the other specifications in forecasting inflation and the federal funds rate at shorter horizons. |
| Keywords: | Financial frictions; DSGE; Time-varying coefficients; Extended Kalman filter; Banking sector |
| JEL: | C11 C13 C32 E37 |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:ucn:wpaper:201611&r=for |
| By: | Stefan Laseen; Marzie Taheri Sanjani |
| Abstract: | Inflation dynamics, as well as its interaction with unemployment, have been puzzling since the Global Financial Crisis (GFC). In this empirical paper, we use multivariate, possibly time-varying, time-series models and show that changes in shocks are a more salient feature of the data than changes in coefficients. Hence, the GFC did not break the Phillips curve. By estimating variations of a regime-switching model, we show that allowing for regime switching solely in coefficients of the policy rule would maximize the fit. Additionally, using a data-rich reduced-form model we compute conditional forecast scenarios. We show that financial and external variables have the highest forecasting power for inflation and unemployment, post-GFC. |
| Keywords: | Global Financial Crisis 2008-2009;Inflation;Unemployment;Time series;Vector autoregression;Econometric models;Phillips curve, Inflation, Unemployment, Financial Frictions, Conditional Forecast, Regime Switching and Bayesian Estimation. |
| Date: | 2016–07–05 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:16/126&r=for |
| By: | Carl Bonham (UH-Manoa Department of Economics, University of Hawaii Economic Research Organization); Peter Fuleky (UH-Manoa Department of Economics and University of Hawaii Economic Research Organization); James Jones (UH-Manoa Department of Economics and University of Hawaii Economic Research Organization); Ashley Hirashima (UH-Manoa Department of Economics and University of Hawaii Economic Research Organization) |
| Abstract: | We evaluate the short term forecasting performance of methods that systematically incorporate high frequency information via covariates. Our study provides a thorough introduction of these methods. We highlight the distinguishing features and limitations of each tool and evaluate their forecasting performance in two tourism-specific applications. The first uses monthly indicators to predict quarterly tourist arrivals to Hawaii; the second predicts quarterly labor income in the accommodations and food services sector. Our results indicate that compared to the exclusive use of low frequency aggregates, including timely intra-period data in the forecasting process results in significant gains in predictive accuracy. Anticipating growing popularity of these techniques among empirical analysts, we present practical implementation guidelines to facilitate their adoption. |
| Keywords: | Nowcast, Ragged edge, Mixed frequency models |
| JEL: | H51 I12 Q51 Q53 |
| Date: | 2015–09 |
| URL: | http://d.repec.org/n?u=RePEc:hae:wpaper:2015-13r&r=for |
| By: | Anufriev, M. (University of Technology, Sydney); Hommes, C.H. (University of Amsterdam); Makarewicz, T.A. (University of Amsterdam) |
| Abstract: | We study a model in which individual agents use simple linear first order price forecasting rules, adapting them to the complex evolving market environment with a smart Genetic Algorithm optimization procedure. The novelties are: (1) a parsimonious experimental foundation of individual forecasting behaviour; (2) an explanation of individual and aggregate behavior in four different experimental settings, (3) improved one-period and 50-period ahead forecasting of lab experiments, and (4) a characterization of the mean, median and empirical distribution of forecasting heuristics. The median of the distribution of GA forecasting heuristics can be used in designing or validating simple Heuristic Switching Model. |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:ams:ndfwpp:15-07&r=for |
| By: | Jiahan Li (University of Notre Dame, USA); Ilias Tsiakas (Department of Economics and Finance, University of Guelph, Canada; The Rimini Centre for Economic Analysis, Italy) |
| Abstract: | This paper shows that the equity premium is predictable out of sample when we use a predictive regression that conditions on a large set of economic fundamentals, subject to: (i) economic constraints on the sign of coefficients and return forecasts, and (ii) statistical constraints imposed by shrinkage estimation. Equity premium predictability delivers a certainty equivalent return of about 2:7% per year over the benchmark for a mean-variance investor. Our predictive framework outperforms a large group of competing models that also condition on economic fundamentals as well as models that condition on technical indicators. |
| Keywords: | Equity Premium; Out-of-Sample Prediction; Economic Fundamentals; Technical Indicators; Shrinkage Estimation |
| JEL: | G11 G14 G17 |
| Date: | 2016–09 |
| URL: | http://d.repec.org/n?u=RePEc:rim:rimwps:16-25&r=for |