Forecasting
http://lists.repec.orgmailman/listinfo/nep-for
Forecasting
2015-11-21
Forecasting with Instabilities: an Application to DSGE Models with Financial Frictions
http://d.repec.org/n?u=RePEc:ucn:wpaper:201523&r=for
This paper examines whether the presence of parameter instabilities in dynamic stochastic general equilibrium (DSGE) models affects their forecasting performance. We apply this analysis to medium-scale DSGE models with and without financial frictions for the US economy. Over the forecast period 2001-2013, the models augmented with financial frictions lead to an improvement in forecasts for inflation and the short term interest rate, while for GDP growth rate the performance depends on the horizon/period. We interpret this finding taking into account parameters instabilities. Fluctuation test shows that models with financial frictions outperform in forecasting inflation but not the GDP growth rate.
Roberta Cardani
Alessia Paccagnini
Stefania Villa
Bayesian estimation; Forecasting; Financial frictions; Parameter instabilities
2015-10
Exponential Smoothing, Long Memory and Volatility Prediction
http://d.repec.org/n?u=RePEc:aah:create:2015-51&r=for
Extracting and forecasting the volatility of financial markets is an important empirical problem. The paper provides a time series characterization of the volatility components arising when the volatility process is fractionally integrated, and proposes a new predictor that can be seen as extension of the very popular and successful forecasting and signal extraction scheme, known as exponential smoothing (ES). First, we derive a generalization of the Beveridge-Nelson result, decomposing the series into the sum of fractional noise processes with decreasing orders of integration. Secondly, we consider three models that are natural extensions of ES: the fractionally integrated first order moving average (FIMA) model, a new integrated moving average model formulated in terms of the fractional lag operator (FLagIMA), and a fractional equal root integrated moving average (FerIMA) model, proposed originally by Hosking. We investigate the properties of the volatility components and the forecasts arising from these specification, which depend uniquely on the memory and the moving average parameters. For statistical inference we show that, under mild regularity conditions, the Whittle pseudo-maximum likelihood estimator is consistent and asymptotically normal. The estimation results show that the log-realized variance series are mean reverting but nonstationary. An out-of-sample rolling forecast exercise illustrates that the three generalized ES predictors improve significantly upon commonly used methods for forecasting realized volatility, and that the estimated model confidence sets include the newly proposed fractional lag predictor in all occurrences.
Tommaso Proietti
Realized Volatility, Volatility Components, Fractional lag models, Fractional equal-root IMA model, Model Confidence Set
2015-06-01
Dissecting Models’ Forecasting Performance
http://d.repec.org/n?u=RePEc:kof:wpskof:15-397&r=for
In this paper we suggest an approach to comparison of models’ forecasting performance in unstable environments. Our approach is based on combination of the Cumulated Sum of Squared Forecast Error Differential (CSSFED) suggested earlier in Welch and Goyal (2008) and the Bayesian change point analysis based on Barry and Hartigan (1993). The latter methodology provides the formal statistical analysis of the CSSFED time series which turned out to be a powerful graphical tool for tracking how the relative forecasting performance of competing models evolves over time. We illustrate the suggested approach by using forecasts of the GDP growth rate in Switzerland.
Boriss Siliverstovs
Forecasting, Forecast Evaluation, Change Point Detection, Bayesian Estimation
2015-11
"Does Higher Test Statistics Imply Better Performance?"
http://d.repec.org/n?u=RePEc:ipk:wpaper:1509&r=for
In Monte Carlo experiment with simulated data, I show that, as a point forecast criterion, the Clark and West's (2006) unconditional test of mean squared prediction errors (MSPE) fails to reflect the relative performance of a superior model over a relatively weaker model. The simulation results show that, even though the MSPE of a superior model is far below a weaker alternative, the Clark and West's (2006) test does not reflect this in their test statistics. Therefore, studies that use this statistics in testing the predictive accuracy of alternative exchange rate models, equity risk premium predictions, stock return predictability, inflation forecasting and unemployment forecasting should not weight too much on the magnitude of the statistically significant Clark and West's (2006) tests statistics.
Levent Bulut
Model comparison, predictive accuracy, point-forecast criterion, the Clark and West test.
2015-11
The Role of Spatial and Temporal Structure for Residential Rent Predictions
http://d.repec.org/n?u=RePEc:usg:sfwpfi:2015:23&r=for
This paper examines the predictive power of five linear hedonic pricing models for the residential market with varying complexity in their spatial and temporal structure. In contrast to similar studies, we extend the out-of-sample forecast evaluation to one-day-ahead predictions with a rolling estimation window, which is a reasonable setting for many practical applications. We can show that in-sample fit and cross-validation prediction accuracy improve significantly when we account for spatial heterogeneity. In particular, for one-day-ahead forecasts, the spatiotemporal autoregressive (STAR) model demonstrates its superiority compared to model specifications with alternating spatial and temporal heterogeneity and dependence structures. In addition, sub-market fixed-effects, constructed on the basis of statistical TREE methods, further improve the results of predefined local rental markets.
Fuess, Roland
Koller, Jan
Classification and Regression Tree (CART) Technique, Forecast Evaluation, Hedonic Pricing Model, Rental Prices, Spatiotemporal Autoregressive (STAR) Model
2015-11
Econometric Analysis of 15-minute Intraday Electricity Prices
http://d.repec.org/n?u=RePEc:usg:sfwpfi:2015:21&r=for
The trading activity in the German intraday electricity market has increased significantly over the last years. This is partially due to an increasing share of renewable energy, wind and photovoltaic, which requires power generators to balance out the forecasting errors in their production. We investigate the bidding behavior in the intraday market by looking at both last prices and continuous bidding, in the context of a fundamental model. A unique data set of 15-minute intraday prices and intraday-updated forecasts of wind and photovoltaic has been employed and price bids are modelled by prior information on fundamentals. We show that intraday prices adjust asymmetrically to both forecasting errors in renewables and to the volume of trades dependent on the threshold variable demand quote, which reflects the expected demand covered by the planned traditional capacity in the day-ahead market. The location of the threshold can be used by market participants to adjust their bids accordingly, given the latest updates in the wind and photovoltaic forecasting errors and the forecasts of the control area balances.
Kiesel, Ruediger
Paraschiv, Florentina
Intraday Electricity Prices, Bidding Behavior, Renewable Energy, Forecasting Model
2015-10
Forecasting Life Expectancy: Evidence from a New Survival Function
http://d.repec.org/n?u=RePEc:hit:hitcei:2015-1&r=for
We propose a new survival function to forecast life expectancies at various ages. The proposed model comprises the youth-to-adulthood component and the old-to-oldest-old component. It is able to closely fit adult survivorship of the US men and women in the period from 1950 to 2010. We find evidence that the forecasting performance of life expectancies by the proposed model compares favorably with those obtained from the popular Lee-Carter model (1992) and the shifting logistic model proposed by Bongaarts (2005).
Wong, Chi Heem
Tsui, Albert K
Lee-Carter model, Life expectancy, Mortality, Survival probability
2015-06
A data-cleaning augmented Kalman filter for robust estimation of state space models
http://d.repec.org/n?u=RePEc:zbw:hohdps:132015&r=for
This article presents a robust augmented Kalman filter that extends the data-cleaning filter (Masreliez and Martin, 1977) to the general state space model featuring nonstationary and regression effects. The robust filter shrinks the observations towards their one-step-ahead prediction based on the past, by bounding the effect of the information carried by a new observation according to an influence function. When maximum likelihood estimation is carried out on the replacement data, an M-type estimator is obtained. We investigate the performance of the robust AKF in two applications using as a modeling framework the basic structural time series model, a popular unobserved components model in the analysis of seasonal time series. First, a Monte Carlo experiment is conducted in order to evaluate the comparative accuracy of the proposed method for estimating the variance parameters. Second, the method is applied in a forecasting context to a large set of European trade statistics series.
Marczak, Martyna
Proietti, Tommaso
Grassi, Stefano
robust filtering,augmented Kalman filter,structural time series model,additive outlier,innovation outlier
2015
The Role of Credit in Predicting US Recessions
http://d.repec.org/n?u=RePEc:aah:create:2015-48&r=for
We study the role of credit in forecasting US recession periods with probit models. We employ both classical recession predictors and common factors based on a large panel of financial and macroeconomic variables as control variables. Our findings suggest that a number of credit variables are useful predictors of US recessions over and above the control variables both in and out of sample. Especially the excess bond premium, capturing the cyclical changes in the relationship between default risk and credit spreads, is found to be a powerful predictor. Overall, models that combine credit variables, common factors, and classic recession predictors, are found to have the best forecasting performance.
Harri Pönkä
Business cycle, Credit Spread, Factor models, Forecasting, Probit models
2015-11-08
The Model Confidence Set package for R
http://d.repec.org/n?u=RePEc:rtv:ceisrp:362&r=for
This paper presents the R package MCS which implements the Model Confidence Set (MCS) procedure recently developed by Hansen, Lunde, and Nason (2011). The Hansen's procedure consists on a sequence of tests which permits to construct a set of "superior" models, where the null hypothesis of Equal Predictive Ability (EPA) is not rejected at a certain confidence level. The EPA statistic tests is calculated for an arbitrary loss function, meaning that we could test models on various aspects, for example punctual forecasts. The relevance of the package is shown using an example which aims at illustrating in details the use of the functions provided by the package. The example compares the ability of different models belonging to the ARCH family to predict large financial losses. We also discuss the implementation of the ARCH{type models and their maximum likelihood estimation using the popular R package rugarch developed by Ghalanos (2014).
Mauro Bernardi
Leopoldo Catania
Hypothesis testing, Model Confidence Set, Value{at{Risk, VaR combination, ARCH- Models, R-CRAN
2015-11-17
Household Forming Inflation Expectations: Why Do They ‘Overreact’?
http://d.repec.org/n?u=RePEc:cdf:wpaper:2015/14&r=for
The purpose of the present paper is to provide a simple model which explains how households (or non-experts) form their inflation forecasts. The paper contributes to the existing literature and the understanding of how inflation expectations are formed in two ways. Firstly, we present an integrated model of how non-experts form their inflation expectations. The paper initially outlines how professionals form inflation forecast. Subsequently, the model presents the non-expert’s expectations formation incorporating the dynamics of the professional’s forecast. Secondly, we explain the prevalent phenomena where non-experts tend to overreact, or overshoot, initially as they revise their inflation forecast.
Easaw, Joshy
Inflation Expectations Formation; Information Rigidities; Over-reaction
2015-10
Eliciting GDP Forecasts from the FOMC’s Minutes Around the Financial Crisis
http://d.repec.org/n?u=RePEc:gwc:wpaper:2015-003&r=for
Stekler and Symington (2016) construct indexes that quantify the Federal Open Market Committee’s views about the U.S. economy, as expressed in the minutes of the FOMC’s meetings. These indexes provide insights on the FOMC’s deliberations, especially at the onset of the Great Recession. The current paper complements Stekler and Symington’s analysis by showing that their indexes reveal relatively minor bias in the FOMC’s views when the indexes are reinterpreted as forecasts. Additionally, these indexes provide a proximate mechanism for inferring the Fed staff’s Greenbook forecasts of the U.S. real GDP growth rate, years before the Greenbook’s public release.
Neil R. Ericsson
Autometrics; bias; Fed; financial crisis; FOMC; forecasts; GDP; Great Recession; Greenbook; impulse indicator saturation; projections; Tealbook; United States.
2015-11
Professionals’ Forecast of the Inflation Gap and its Persistence
http://d.repec.org/n?u=RePEc:cdf:wpaper:2015/13&r=for
The purpose of the present paper is to investigate perceived inflation gap persistence using actual data of professional forecasts. We derive the unobserved perceived inflation gap persistence and using a state dependent model we estimate the non-linear persistence coefficient of inflation gap. Our main result is that for GDP deflator inflation, the estimates of persistence largely confirm the results obtained indirectly using a linear model. However, when we look at CPI inflation, we find that there is strong evidence for state-dependence and time variation.
Easaw, Joshy
Heravi, Saeed
Dixon, Huw David
Perceived Inflation Gaps; Professional’s Survey-based Forecasts; State-Dependent Models
2015-10
Forecasting with Colonel Blotto
http://d.repec.org/n?u=RePEc:unm:umagsb:2015025&r=for
In this paper we design and test a competitive forecasting mechanism based on the Colonel Blotto game. In the game, forecasters allocate a fixed number of resources to different battlefields. Each field is realized with a probability that is determined by a stochastic process. Subjects learn about the underlying process during the course of the experiment and thereby form beliefs about the probability that a field is selected. Once a field is selected, the subject competes for a payoff that is associated with the number of resources allocated to that field. We implement two different payment rules, a lottery and an auction, and find that the lottery outperforms the auction. This relative underperformance of the auction can be attributed to the strategic uncertainty being too high in the auction and the strong incentives to misalign allocations.
Peeters R.J.A.P.
Wolk K.L.
Noncooperative Games; Design of Experiments: Laboratory, Group Behavior; Asymmetric and Private Information; Mechanism Design; Search; Learning; Information and Knowledge; Communication; Belief;
2015
A structural model for policy analysis and forecasting: NZSIM
http://d.repec.org/n?u=RePEc:nzb:nzbdps:2015/05&r=for
We describe the underlying structure of the new forecasting and policy model used at the Reserve Bank of New Zealand. This paper outlines the dynamic stochastic general equilibrium part of the model, which is deliberately kept small so that it is easily understood and applied in the forecasting context. We also discuss the key transmission channels in the model, estimate the model's parameters and evaluate its ability to explain New Zealand data. macroprudential policies.
Güneş Kamber
Chris McDonald
Nicholas Sander
Konstantinos Theodoridis
2015-11