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- Pitarakis, Jean-Yves (2019): Predictive Regressions
Predictive regressions are a widely used econometric environment for assessing the predictability of economic and financial variables using past values of one or more predictors. The nature of the applications considered by practitioners often involve the use of predictors that have highly persistent smoothly varying dynamics as opposed to the much noisier nature of the variable being predicted. ... The frequent use of these predictive regressions in applied work has also led practitioners to question the validity of viewing predictability within a linear setting that ignores the possibility that predictability may occasionally be switched off. This in turn has generated a new stream of research aiming at introducing regime specific behaviour within predictive regressions in order to explicitly capture phenomena such as episodic predictability.
RePEc:cte:werepe:28554 Save to MyIDEAS - Lee, Dae-Jin (2017): A general framework for prediction in penalized regression
We present several methods for prediction of new observations in penalized regression using different methodologies, based on the methods proposed in: i) Currie et al. (2004), ii) Gilmour et al. (2004) and iii) Sacks et al. (1989). We extend the method introduced by Currie et al. (2004) to consider the prediction of new observations in the mixed model framework. ... We also introduce the concept memory of a P-spline, this new idea gives us information on how much past information we are using to predict.
RePEc:cte:wsrepe:24607 Save to MyIDEAS - Sancetta, A. (2007): Universality of Bayesian Predictions
Given the sequential update nature of Bayes rule, Bayesian methods find natural application to prediction problems. ... Hence, there is a strong case for feasible predictions in a Bayesian framework. This paper studies the theoretical properties of Bayesian predictions and shows that under minimal conditions we can derive finite sample bounds for the loss incurred using Bayesian predictions under the Kullback-Leibler divergence. In particular, the concept of universality of predictions is discussed and universality is established for Bayesian predictions in a variety of settings. These include predictions under almost arbitrary loss functions, model averaging, predictions in a non stationary environment and under model miss-specification.
RePEc:cam:camdae:0755 Save to MyIDEAS - McLean, A. (1999): The Predictive Approach to Teaching Statistics
It is argued here that the underlying purpose, often implicit rather than explicit, of every statistical analysis is to establish a set of probability models which can be used to predict values of one or more variables. Such a model constitutes 'information' only in the sense, and to the extent, that it provides predictions of sufficient quality to be useful for decision making. The quality of the decision making is determined by the quality of the predictions, and hence by that of the models used. Using natural criteria, the 'best predictions' for nominal and numeric variables are respectively the mode and mean. For a nominal variable, the quality of a prediction is measured by the probability of error; for a numeric variable, it is specified using a prediction interval.
RePEc:msh:ebswps:1999-4 Save to MyIDEAS - Lee, Dae-Jin (2019): Out-of-sample prediction in multidimensional P-spline models
Prediction of out-of-sample values is a problem of interest in any regression model. In the context of penalized smooth mixed model regression Carballo et al. (2017) have proposed a general framework for prediction in additive models without interaction terms. The aim of this paper is to extend this work, based on the methodology proposed in Currie et al. (2004), to models that include interaction terms, i.e. prediction is needed in multidimensional setting. Our approach fits the data and predicts the new observations simultaneously and uses constraints to ensure a coherent fit or to impose further restrictions on the predictions. ... To illustrate the methodology two real data sets are used, one to predict log mortality rates in the Spanish population and another to predict aboveground biomass in Populus trees as a smooth function of height and diameter.
RePEc:cte:wsrepe:28630 Save to MyIDEAS - Gonzalo, Jesus & Pitarakis, Jean-Yves (2010): Regime Specific Predictability in Predictive Regressions
Predictive regressions are linear specifications linking a noisy variable such as stock returns to past values of a more persistent regressor with the aim of assessing the presence of predictability. ... In this paper we develop tests for uncovering the presence of predictability in such models when the strength or direction of predictability may alternate across different economically meaningful episodes. An empirical application reconsiders the Dividend Yield based return predictability and documents a strong predictability that is countercyclical, occurring solely during bad economic times
RePEc:pra:mprapa:29190 Save to MyIDEAS - Valentina Corradi & Norman Swanson (2004): Predictive Density Evaluation
This Chapter discusses estimation, specification testing, and model selection of predictive density models. In particular, predictive density estimation is briefly discussed.
RePEc:rut:rutres:200419 Save to MyIDEAS - Devpura, Neluka & Narayan, Paresh Kumar & Sharma, Susan Sunila (2018): Is stock return predictability time-varying?
Using historical data (January 1927 to December 2014), this paper shows that stock return predictability is time-varying based on several well-known predictors from the literature. However, only 7 of 14 predictors exhibit this time-varying predictability pattern. For the remaining predictors, either there is no predictability or predictability is not time-dependent. We also examine the determinants of time-varying predictability. We show that (a) both expected and unexpected shocks emanating from financial variables, and (b) phases of predictability (which capture market volatility) explain return predictability.
RePEc:eee:intfin:v:52:y:2018:i:c:p:152-172 Save to MyIDEAS - Anuchit Ratanaparadorn & Sasivimol Meeampol & Thaneerat Siripachana & Pornthep Anussornnitisarn (2013): Identification of Traffic Prediction Parameters
The current and historical traffic condition could be the factors that could predict traffic condition. ... No thorough study has been conducted to determine if the weather data near the road and time of the day combining with traffic volume and traffic flow speed in multiple periods: weekday, weekend, rush hours could increase prediction result. Even though, the prediction accuracy result is not high as expected but it is possible that current and history speed can be used to predict the future traffic speed. ... The weekday, weekend, and holiday could help prediction accuracy higher, but their significant statistical correlations are minimal.
RePEc:tkp:mklp13:1479-1486 Save to MyIDEAS - A. H. Welsh (2018): Comment on “Confidence, credibility and prediction”
No abstract is available for this item.
RePEc:spr:metron:v:76:y:2018:i:2:d:10.1007_s40300-018-0138-2 Save to MyIDEAS