Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components? PDF Download
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Author: Christine De Mol
Publisher:
ISBN: 9783865582089
Category :
Languages : en
Pages : 36
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Book Description
Author: Christine De Mol
Publisher:
ISBN: 9783865582089
Category :
Languages : en
Pages : 36
Get Book Here
Book Description
Author: Rachida Ouysse
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
We study the performance of Bayesian model averaging as a forecasting method for a large panel of time series and compare its performance to principal components regression (PCR). We show empirically that these forecasts are highly correlated implying similar mean-square forecast errors. Applied to forecasting Industrial production and inflation in the United States, we find that the set of variables deemed informative changes over time which suggest temporal instability due to collinearity and to the of Bayesian variable selection method to minor perturbations of the data. In terms of mean-squared forecast error, principal components based forecasts have a slight marginal advantage over BMA. However, this marginal edge of PCR in the average global out-of-sample performance hides important changes in the local forecasting power of the two approaches. An analysis of the Theil index indicates that the loss of performance of PCR is due mainly to its exuberant biases in matching the mean of the two series especially the inflation series. BMA forecasts series matches the first and second moments of the GDP and inflation series very well with practically zero biases and very low volatility. The fluctuation statistic that measures the relative local performance shows that BMA performed consistently better than PCR and the naive benchmark (random walk) over the period prior to 1985. Thereafter, the performance of both BMA and PCR was relatively modest compared to the naive benchmark.
Author: Alessandro Giovannelli
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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This paper aims to introduce a nonlinear model to forecast macroeconomic time series using a large number of predictors. The technique used to summarize the predictors in a small number of variables is Principal Component Analysis (PC), while the method used to capture nonlinearity is artificial neural network, specifically Feedforward Neural Network (FNN). Commonly in principal component regression forecasts are made using linear models. However linear techniques are often misspecified providing only a poor approximation to the best possible forecast. In an effort to address this issue, the FNN-PC technique is proposed. To determine the practical usefulness of the model, several pseudo forecasting exercises on 8 series of the United States economy, grouped in real and nominal categories, are conducted. This method was used to construct the forecasts at 1-, 3-, 6-, and 12-month horizons for monthly US economic variables using 131 predictors. The empirical study shows that FNN-PC has good ability to predict the variables under study in the period before the start of the "Great Moderation", namely 1984. After 1984, FNN-PC has the same accuracy in forecasting with respect to the benchmark.
Author: Rob J Hyndman
Publisher: OTexts
ISBN: 0987507117
Category : Business & Economics
Languages : en
Pages : 380
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Book Description
Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Author: Christine De Mol
Publisher:
ISBN:
Category :
Languages : en
Pages : 26
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Author: Yoshimasa Uematsu
Publisher:
ISBN:
Category :
Languages : en
Pages : 48
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Book Description
This paper studies macroeconomic forecasting and variable selection using a folded-concave penalized regression with a very large number of predictors. The penalized regression approach leads to sparse estimates of the regression coefficients, and is applicable even if the dimensionality of the model is much larger than the sample size. The first half of the paper discusses the theoretical aspects of a folded-concave penalized regression when the model exhibits time series dependence. Specifically, we show the oracle inequality and the oracle property for ultrahigh-dimensional time-dependent regressors. The latter half of the paper shows the validity of the penalized regression using two motivating empirical applications. The first forecasts U.S. GDP with the FRED-MD data using the MIDAS regression framework, where there are more than 1000 covariates, while the sample size is at most 200. The second examines how well the penalized regression screens the hidden portfolio with around 40 stocks from more than 1800 potential stocks using NYSE stock price data. Both applications reveal that the penalized regression provides remarkable results in terms of forecasting performance and variable selection.
Author: Andreas Graefe
Publisher:
ISBN:
Category :
Languages : en
Pages : 20
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Book Description
The usual procedure for developing linear models to predict any kind of target variable is to identify a subset of most important predictors and to estimate weights that provide the best possible solution for a given sample. The resulting “optimally” weighted linear composite is then used when predicting new data. This approach is useful in situations with large and reliable datasets and few predictor variables. However, a large body of analytical and empirical evidence since the 1970s shows that the weighting of variables is of little, if any, value in situations with small and noisy datasets and a large number of predictor variables. In such situations, including all relevant variables is more important than their weighting. These findings have yet to impact many fields. This study uses data from nine established U.S. election-forecasting models whose forecasts are regularly published in academic journals to demonstrate the value of weighting all predictors equally and including all relevant variables in the model. Across the ten elections from 1976 to 2012, equally weighted predictors reduced the forecast error of the original regression models on average by four percent. An equal-weights model that includes all variables provided well-calibrated forecasts that reduced the error of the most accurate regression model by 29% percent.
Author: Seung C. Ahn
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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We consider Partial Least Squares (PLS) estimation of a time-series forecasting model with the data containing a large number (T) of time series observations on each of a large number (N) of predictor variables. In the model, a subset or a whole set of the latent common factors in predictors are determinants of a single target variable to be forecasted. The factors relevant for forecasting the target variable, which we refer to as PLS factors, can be sequentially generated by a method called "Nonlinear Iterative Partial Least Squares" (NIPLS) algorithm. Two main findings from our asymptotic analysis are the following. First, the optimal number of the PLS factors for forecasting could be much smaller than the number of the common factors in the original predictor variables relevant for the target variable. Second, as more than the optimal number of PLS factors is used, the out-of-sample forecasting power of the factors could rather decrease while their in-sample explanatory power may increase. Our Monte Carlo simulation results confirm these asymptotic results. In addition, our simulation results indicate that unless very large samples are used, the out-of-sample forecasting power of the PLS factors is often higher when a smaller than the asymptotically optimal number of factors are used. We find that the out-of-sample forecasting power of the PLS factors often decreases as the second, third, and more factors are added, even if the asymptotically optimal number of the factors is greater than one.
Author: Fanning Meng
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 104
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Book Description
The dissertation is focused on the analysis of economic forecasting with a large number of predictors. The first chapter develops a novel forecasting method that minimizes the effects of weak predictors and estimation errors on the accuracy of equity premium forecasts. The proposed method is based on an averaging scheme applied to quantiles conditional on predictors selected by LASSO. The resulting forecasts outperform the historical average, and other existing models, by statistically and economically meaningful margins. In the second chapter, we find that incorporating distributional and high-frequency information into a forecasting model can produce substantial accuracy gains. Distributional information is included through a quantile combination approach, but estimation of quantile regressions with mixed-frequency data leads to a parameter proliferation problem. We consider extensions of the MIDAS and soft (hard) thresholding methods towards quantile regression. Our empirical study on GDP growth rate reveals a strong predictability gain when high-frequency and distributional information are adequately incorporated into the same forecasting model. The third chapter analyzes the wage effects of college enrollment for returning adults based on the NLSY79 data. To improve the estimation efficiency, we apply the double-selection model among time-varying features and individual fixed effects. The empirical results on hourly wage predictions show evidences towards the superiority of double-selection model over a fixed-effect model. Based on the double-selection model, we find significant and positive returns on years of college enrollment for the returning adults. On average, one more year's college enrollment can increase hourly wage of returning adults by $1.12, an estimate that is about 7.7% higher than that from the fixed-effect model.
Author: Sonia Leva
Publisher: MDPI
ISBN: 3036510303
Category : Science
Languages : en
Pages : 188
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Book Description
Nowadays, forecast applications are receiving unprecedent attention thanks to their capability to improve the decision-making processes by providing useful indications. A large number of forecast approaches related to different forecast horizons and to the specific problem that have to be predicted have been proposed in recent scientific literature, from physical models to data-driven statistic and machine learning approaches. In this Special Issue, the most recent and high-quality researches about forecast are collected. A total of nine papers have been selected to represent a wide range of applications, from weather and environmental predictions to economic and management forecasts. Finally, some applications related to the forecasting of the different phases of COVID in Spain and the photovoltaic power production have been presented.
