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on Forecasting |
| By: | Erik Snowberg; Justin Wolfers; Eric Zitzewitz |
| Abstract: | Prediction markets—markets used to forecast future events—have been used to accurately forecast the outcome of political contests, sporting events, and, occasionally, economic outcomes. This chapter summarizes the latest research on prediction markets in order to further their utilization by economic forecasters. We show that prediction markets have a number of attractive features: they quickly incorporate new information, are largely efficient, and impervious to manipulation. Moreover, markets generally exhibit lower statistical errors than professional forecasters and polls. Finally, we show how markets can be used to both uncover the economic model behind forecasts, as well as test existing economic models. |
| Date: | 2012–07 |
| URL: | http://d.repec.org/n?u=RePEc:acb:camaaa:2012-33&r=for |
| By: | Philip Hans Franses; Michael McAleer (University of Canterbury); Rianne Legerstee |
| Abstract: | Macroeconomic forecasts are frequently produced, widely published, inten¬sively discussed and comprehensively used. The formal evaluation of such forecasts has a long research history. Recently, a new angle to the evaluation of forecasts has been addressed, and in this review we analyse some recent developments from that perspective. The literature on forecast evaluation predominantly assumes that macro¬economic forecasts are generated from econometric models. In practice, however, most macroeconomic forecasts, such as those from the IMF, World Bank, OECD, Federal Reserve Board, Federal Open Market Committee (FOMC) and the ECB, are typically based on econometric model forecasts jointly with human intuition. This seemingly inevitable combination renders most of these forecasts biased and, as such, their evaluation becomes non-standard. In this review, we consider the evaluation of two forecasts in which: (i) the two forecasts are generated from two distinct econo¬metric models; (ii) one forecast is generated from an econometric model and the other is obtained as a combination of a model and intuition; and (iii) the two forecasts are generated from two distinct (but unknown) combinations of different models and intu¬ition. It is shown that alternative tools are needed to compare and evaluate the fore-casts in each of these three situations. These alternative techniques are illustrated by comparing the forecasts from the (econometric) Staff of the Federal Reserve Board and the FOMC on inflation, unemployment and real GDP growth. It is shown that the FOMC does not forecast significantly better than the Staff, and that the intuition of the FOMC does not add significantly in forecasting the actual values of the economic fundamentals. This would seem to belie the purported expertise of the FOMC. |
| Keywords: | Macroeconomic forecasts; econometric models; human intuition; biased forecasts; forecast performance; forecast evaluation; forecast comparison |
| JEL: | C22 C51 C52 C53 E27 E37 |
| Date: | 2012–06–08 |
| URL: | http://d.repec.org/n?u=RePEc:cbt:econwp:12/12&r=for |
| By: | Julie K. Smith |
| Abstract: | This paper investigates the forecasting accuracy of the trimmed mean inflation rate of the Personal Consumption Expenditure (PCE) deflator. Earlier works have examined the forecasting ability of limited-influence estimators (trimmed means and the weighted median) of the Consumer Price Index but none have compared the weighted median and trimmed mean of the PCE. Also addressed is the systematic bias that appears due to the differences in the means of inflation measures over the sample. This paper supports earlier results that limited-influence estimators provide better forecasts of future inflation than does the popular measure of core inflation, PCE inflation minus food and energy; therefore, these limited-influence estimators are core inflation. |
| Keywords: | Price levels ; Forecasting |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:fip:feddwp:1203&r=for |
| By: | Rodrigo Mariscal; Andrew Powell |
| Abstract: | There are many sources of inflation forecasts for Latin America. The International Monetary Fund, Latin Focus, the Economist Intelligence Unit and other consulting companies all offer inflation forecasts. However, these sources do not provide any probability measures regarding the risk of inflation. In some cases, Central Banks offer forecast and probability analyses but typically their models are not fully transparent. This technical note attempts to develop a relatively homogeneous set of methodologies and employs them to estimate inflation forecasts, probability distributions for those forecasts and hence probability measures of high inflation. The methodologies are based on both parametric and non-parametric estimation. Results are given for five countries in the region that have inflation targeting regimes. |
| JEL: | C53 E37 |
| Date: | 2012–06 |
| URL: | http://d.repec.org/n?u=RePEc:idb:wpaper:4785&r=for |
| By: | Gregory R. Duffee |
| Abstract: | This chapter discusses what the asset-pricing literature concludes about the forecastability of interest rates. It outlines forecasting methodologies implied by this literature, including dynamic, no-arbitrage term structure models and their macro-finance extensions. It also reviews the empirical evidence concerning the predictability of future yields on Treasury bonds and future excess returns to holding these bonds. In particular, it critically evaluates theory and evidence that variables other than current bond yields are useful in forecasting. |
| Date: | 2012–07 |
| URL: | http://d.repec.org/n?u=RePEc:jhu:papers:599&r=for |
| By: | Mark J Jensen; John M Maheu |
| Abstract: | This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models. In contrast to the parametric literature the return distribution can display general forms of asymmetry and thick tails. An infinite mixture of multivariate normals is given a flexible Dirichlet process prior. The GARCH functional form enters into each of the components of this mixture. We discuss conjugate methods that allow for scale mixtures and nonconjugate methods which provide mixing over both the location and scale of the normal components. MCMC methods are introduced for posterior simulation and computation of the predictive density. Bayes factors and density forecasts with comparisons to GARCH models with Student-t innovations demonstrate the gains from our flexible modeling approach. |
| Keywords: | Dirichlet process mixture, slice sampling |
| JEL: | C11 C14 C32 C53 C58 |
| Date: | 2012–06–29 |
| URL: | http://d.repec.org/n?u=RePEc:tor:tecipa:tecipa-458&r=for |
| By: | Bela Nagy; J. Doyne Farmer; Quan M. Bui; Jessika E. Trancik |
| Abstract: | Forecasting technological progress is of great interest to engineers, policy makers, and private investors. Several models have been proposed for predicting technological improvement, but how well do these models perform? An early hypothesis made by Theodore Wright in 1936 is that cost decreases as a power law of cumulative production. An alternative hypothesis is Moore's law, which can be generalized to say that technologies improve exponentially with time. Other alternatives were proposed by Goddard, Sinclair et al., and Nordhaus. These hypotheses have not previously been rigorously tested. Using a new database on the cost and production of 62 different technologies, which is the most expansive of its kind, we test the ability of six different postulated laws to predict future costs. Our approach involves hindcasting and developing a statistical model to rank the performance of the postulated laws. Wright's law produces the best forecasts, but Moore's law is not far behind. We discover a previously unobserved regularity that production tends to increase exponentially. A combination of an exponential decrease in cost and an exponential increase in production would make Moore's law and Wright's law indistinguishable, as originally pointed out by Sahal. We show for the first time that these regularities are observed in data to such a degree that the performance of these two laws is nearly tied. Our results show that technological progress is forecastable, with the square root of the logarithmic error growing linearly with the forecasting horizon at a typical rate of 2.5% per year. These results have implications for theories of technological change, and assessments of candidate technologies and policies for climate change mitigation. |
| Date: | 2012–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1207.1463&r=for |
| By: | Krieger, Kevin; Fodor, Andy; Mauck, Nathan; Stevenson, Greg |
| Abstract: | We consider which readily observable characteristics of individual stocks (e.g., option implied volatility, accounting data, analyst data) may be used to forecast subsequent extreme price movements. We are the first to explicitly consider the predictive influence of option implied volatility in such a framework, which we unsurprisingly find to be an important indicator of future extreme price movements. However, after controlling for implied volatility levels, other factors, particularly firm age and size, still have additional predictive power of extreme future returns. Furthermore, excluding predicted extreme return stocks leads to a portfolio that has lower risk (standard deviation of returns) without sacrificing performance. |
| Keywords: | Implied volatility; portfolio management |
| JEL: | G11 G00 |
| Date: | 2012–05–14 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:39845&r=for |
| By: | Eric Hillebrand (Aarhus University and CREATES); Marcelo C. Medeiros (PONTIFICAL CATHOLIC UNIVERSITY OF RIO DE JANEIRO) |
| Abstract: | We study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in ARMA time series models and apply our modeling framework to daily realized volatility. Asymptotic theory for parameter estimation is developed and two model building procedures are proposed. The methodology is applied to stocks of the Dow Jones Industrial Average during the period 2000 to 2009. We find strong evidence of nonlinear effects. |
| Keywords: | Smooth transitions, long memory, forecasting, realized volatility. |
| JEL: | C22 |
| Date: | 2012–06–12 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2012-30&r=for |
| By: | Alfredo Di Tillio (Bocconi University - Bocconi University); Itzhak Gilboa (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - GROUPE HEC - CNRS : UMR2959, Tel-Aviv University - Tel-Aviv University); Larry Samuelson (Department of Economics - Yale University) |
| Abstract: | We suggest a model that describes how counterfactuals are constructed and justified. The model can describe how counterfactual beliefs are updated given the unfolding of actual history. It also allows us to examine the use of counterfactuals in prediction, and to show that a logically omniscient reasoner gains nothing from using counterfactuals for prediction. |
| Keywords: | induction, counterfactuals, prediction |
| Date: | 2012–07 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-00712888&r=for |
| By: | Jason Shachat; J. Todd Swarthout; Lijia Wei |
| Abstract: | We propose a statistical model to assess whether individuals strategically use mixed strategies in repeated games. We formulate a hidden Markov model in which the latent state space contains both pure and mixed strategies, and allows switching between these states. We apply the model to data from an experiment in which human subjects repeatedly play a normal form game against a computer that always follows its part of the unique mixed strategy Nash equilibrium profile. Estimated results show significant mixed strategy play and non-stationary dynamics. We also explore the ability of the model to forecast action choice. |
| Keywords: | Mixed Strategy, Nash Equilibrium, Experiment, Hidden Markov Model |
| JEL: | C92 C72 C10 |
| Date: | 2012–07 |
| URL: | http://d.repec.org/n?u=RePEc:exc:wpaper:2012-11&r=for |
| By: | Alan K. Detmeister |
| Abstract: | Consumer price inflation excluding food and energy often performs worse than other measures of underlying inflation in out-of-sample tests of predicting future inflation or tracking an ex-post measure of underlying trend inflation. Nonetheless, inflation excluding food and energy remains popular for its simplicity and transparency. Would excluding different items improve performance while maintaining the simplicity and transparency? Unfortunately, probably not. Averaging across a series of tests suggests that knowing what items to exclude before seeing the data is problematic and excluding food and energy is not a bad ex-ante guess. However, ex-post it is not difficult to construct an index which performs considerably better than excluding food and energy. |
| Date: | 2012 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2012-43&r=for |
| By: | Meghan R. Busse; Devin G. Pope; Jaren C. Pope; Jorge Silva-Risso |
| Abstract: | Projection bias is the tendency to overpredict the degree to which one’s future tastes will resemble one’s current tastes. We test for evidence of projection bias in two of the largest and most important consumer markets – the car and housing markets. Using data for more than forty million vehicle transactions and four million housing purchases, we explore the impact of the weather on purchasing decisions. We find that the choice to purchase a convertible, a 4-wheel drive, or a vehicle that is black in color is highly dependent on the weather at the time of purchase in a way that is inconsistent with classical utility theory. Similarly, we find that the hedonic value that a swimming pool and that central air add to a house is higher when the house goes under contract in the summertime compared to the wintertime. |
| JEL: | D03 D12 L62 |
| Date: | 2012–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:18212&r=for |