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Evolution of forecast disagreement in a Bayesian learning model
 Journal of Econometrics
, 2008
"... Abstract: We estimate a Bayesian learning model with heterogeneity aimed at explaining expert forecast disagreement and its evolution over horizons. Disagreement is postulated to have three components due to differences in: i) the initial prior beliefs, ii) the weights attached on priors, and iii) i ..."
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Cited by 14 (4 self)
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Abstract: We estimate a Bayesian learning model with heterogeneity aimed at explaining expert forecast disagreement and its evolution over horizons. Disagreement is postulated to have three components due to differences in: i) the initial prior beliefs, ii) the weights attached on priors, and iii) interpreting public information. The fixedtarget, multihorizon, crosscountry feature of the panel data allows us to estimate the relative importance of each component precisely. The first component explains nearly all to 30% of forecast disagreement as the horizon decreases from 24 months to 1 month. This finding firmly establishes the role of initial prior beliefs in generating expectation stickiness. We find the second component to have barely any effect on the evolution of forecast disagreement among experts. The importance of the third component increases from almost nothing to 70 % as the horizon gets shorter via its interaction with the quality of the incoming news. We propose a new test of forecast efficiency in the context of Bayesian information processing and find significant heterogeneity in the nature of inefficiency across horizons and countries.
Testing Slope Homogeneity in Large Panels ∗
, 2007
"... This paper proposes a standardized version of Swamy’s test of slope homogeneity for panel data models where the cross section dimension (N) could be large relative to the time series dimension (T). The proposed test, denoted by ˜ ∆, exploits the cross section dispersion of individual slopes weighted ..."
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Cited by 10 (2 self)
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This paper proposes a standardized version of Swamy’s test of slope homogeneity for panel data models where the cross section dimension (N) could be large relative to the time series dimension (T). The proposed test, denoted by ˜ ∆, exploits the cross section dispersion of individual slopes weighted by their relative precision. In the case of models with strictly exogenous regressors, but with nonnormally distributed errors, the test is shown to have a standard normal distribution as (N, T) j → ∞ such that √ N/T 2 → 0. When the errors are normally distributed, a meanvariance bias adjusted version of the test is shown to be normally distributed irrespective of the relative expansion rates of N and T. The test is also applied to stationary dynamic models, and shown to be valid asymptotically so long as N/T → κ, as (N, T) j → ∞, where 0 ≤ κ < ∞. Using Monte Carlo experiments, it is shown that the test has the correct size and satisfactory power in panels with strictly exogenous regressors for various combinations of N and T. Similar results are also obtained for dynamic panels, but only if the autoregressive coefficient is not too close to unity and so long as T ≥ N.
Identifying Distributional Characteristics in Random Coefficients Panel Data Models
, 2009
"... We study the identification of panel models with linear individualspecific coefficients, when T is fixed. We show identification of the variance of the effects under conditional uncorrelatedness. Identification requires restricted dependence of errors, reflecting a tradeoff between heterogeneity a ..."
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Cited by 9 (2 self)
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We study the identification of panel models with linear individualspecific coefficients, when T is fixed. We show identification of the variance of the effects under conditional uncorrelatedness. Identification requires restricted dependence of errors, reflecting a tradeoff between heterogeneity and error dynamics. We show identification of the density of individual effects when errors follow an ARMA process under conditional independence. We discuss GMM estimation of moments of effects and errors, and introduce a simple density estimator of a slope effect in a special case. As an application we estimate the effect that a mother smokes during pregnancy on child’s birth weight.
Econometrics: A Bird’s Eye View ∗
, 2006
"... As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly over the past few decades. Major advances have taken place in the analysis of cross sectional data by means of semiparametric and nonparametric techniques. Heterogeneity of economic ..."
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As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly over the past few decades. Major advances have taken place in the analysis of cross sectional data by means of semiparametric and nonparametric techniques. Heterogeneity of economic relations across individuals, firms and industries is increasingly acknowledged and attempts have been made to take them into account either by integrating out their effects or by modeling the sources of heterogeneity when suitable panel data exists. The counterfactual considerations that underlie policy analysis and treatment evaluation have been given a more satisfactory foundation. New time series econometric techniques have been developed and employed extensively in the areas of macroeconometrics and finance. Nonlinear econometric techniques are used increasingly in the analysis of cross section and time series observations. Applications of Bayesian techniques to econometric problems have been given new impetus largely thanks to advances in computer power and computational techniques. The use of Bayesian techniques have in turn provided the investigators with a unifying framework where the tasks of forecasting, decision making, model evaluation and learning can be considered as parts of the same interactive and iterative process; thus paving the way for establishing the foundation of “real time econometrics”. This paper attempts to provide an overview of some of these developments.
unknown title
"... Where Y t (ω) is the observable random variable, the t ∈ {0, 1, 2,..., T} and ω ∈ {1, 2,..., N} indexes designate the time and the population of individuals respectively. We suppose that: (i) {εt (ω)} is an independent sequence of univariate random variables with mean zero and variance σ2 ω. (ii) Ai ..."
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Where Y t (ω) is the observable random variable, the t ∈ {0, 1, 2,..., T} and ω ∈ {1, 2,..., N} indexes designate the time and the population of individuals respectively. We suppose that: (i) {εt (ω)} is an independent sequence of univariate random variables with mean zero and variance σ2 ω. (ii) Ai (ω), 1 ≤ i ≤ p are independent random variables with E {A1 (ω),..., Ap (ω)} = (α1,..., αp) ′. (iii) Ai (ω), 1 ≤ i ≤ p are mutually independent of εt (ω) and Yt (ω) for each t. (iv) {A(ω), ω = 1,..., N} is an independent sequence of p × p matrices with
A Bayesian Panel Data Approach to Explaining Market Beta Dynamics
, 2008
"... Our main objective in this paper is to characterize the process that drives the market betas of individual stocks. We set up a hierarchical Bayesian panel data model that allows a flexible specification for beta and estimate this model using a large panel of NYSEAMEX stocks over the period July 196 ..."
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Our main objective in this paper is to characterize the process that drives the market betas of individual stocks. We set up a hierarchical Bayesian panel data model that allows a flexible specification for beta and estimate this model using a large panel of NYSEAMEX stocks over the period July 1964 through December 2006. Because true betas are latent we implement the MIDAS approach developed by Ghysels, SantaClara, and Valkanov (2005) to estimate realized betas using highfrequency data. Furthermore, we determine the optimal weights given to past data in estimating realized betas, which leads to more precise estimates than those produced by traditional rolling window estimators. We find that combining the parametric relationship between betas and conditioning variables specified by economic theory with the robustness of an autoregressive specification for beta delivers superior estimates of market betas. We also provide empirical support for the prediction of conditional asset pricing theory that individual stocks exhibit significantly different risk dynamics. Finally, we document strong crosssectional heterogeneity in firmspecific betas within the 25 sizeB/M portfolios that are commonly used to test asset pricing models.