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Creator: Rich, Robert W., 1958- and Tracy, Joseph S., 1956- Series: Joint committee on business and financial analysis Abstract:
This paper examines data on point and probabilistic forecasts of inflation from the Survey of Professional Forecasters. We use this data to evaluate current strategies for the empirical modeling of forecast behavior. In particular, the analysis principally focuses on the relationship between ex post forecast errors and ex ante measures of uncertainty in order to assess the reliability of using proxies based on predictive accuracy to describe changes in predictive confidence. After we adjust the data to account for certain features in the conduct and construct of the survey, we find a significant and robust correlation between observed heteroskedasticity in the consensus forecast errors and forecast uncertainty. We also document that significant compositional effects are present in the data that are economically important in the case of forecast uncertainty, and may be related to differences in respondents' access to information.
Palavra-chave: Forecasting, Inflation, Uncertainty, Disagreement, and Conditional heteroskedasticity Sujeito: C12 - Econometric and statistical methods : General - Hypothesis testing, C22 - Single equation models ; Single variables - Time-series models ; Dynamic quantile regressions, and E37 - Prices, business fluctuations, and cycles - Forecasting and simulation
Creator: Christiano, Lawrence J. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 303 Abstract:
This paper investigates—in the context of a simple example—the accuracy of an econometric technique recently proposed by Kydland and Prescott. We consider a hypothetical econometrician who has a large sample of data, which is known to be generated as a solution to an infinite horizon, stochastic optimization problem. The form of the optimization problem is known to the econometrician. However, the values of some of the parameters need to be estimated. The optimization problem—presented in a recent paper by Long and Plosser—is not linear quadratic. Nevertheless, its closed form solution is known, although not to the hypothetical econometrician of this paper. The econometrician uses Kydland and Prescott’s method to estimate the unknown structural parameters. Kydland and Prescott’s approach involves replacing the given stochastic optimization problem by another which approximates it. The approximate problem is a element of the class of linear quadratic problems, whose solution is well-known—even to the hypothetical econometrician of this paper. After examining the probability limits of the econometrician’s estimators under “reasonable” specifications of model parameters, we conclude that the Kydland and Prescott method works well in the example considered. It is left to future research to determine the extent to which the results obtained for the example in this paper applies to a broader class of models.
Creator: Geweke, John Series: Staff report (Federal Reserve Bank of Minneapolis. Research Department) Number: 148 Abstract:
Data augmentation and Gibbs sampling are two closely related, sampling-based approaches to the calculation of posterior moments. The fact that each produces a sample whose constituents are neither independent nor identically distributed complicates the assessment of convergence and numerical accuracy of the approximations to the expected value of functions of interest under the posterior. In this paper methods for spectral analysis are used to evaluate numerical accuracy formally and construct diagnostics for convergence. These methods are illustrated in the normal linear model with informative priors, and in the Tobit-censored regression model.
Creator: McGrattan, Ellen R. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 514 Palavra-chave: Finite element method, Computational time, Accuracy, Stochastic growth model, Applied economics, and Computational method Sujeito: C52 - Model Evaluation, Validation, and Selection and C63 - Computational Techniques; Simulation Modeling
Creator: Christiano, Lawrence J. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 415 Abstract:
This article studies the accuracy of two versions of Kydland and Prescott's (1980, 1982) procedure for approximating optimal decision rules in problems in which the objective fails to be quadratic and the constraints fail to be linear. The analysis is carried out using a version of the Brock-Mirman (1972) model of optimal economic growth. Although the model is not linear quadratic, its solution can nevertheless be computed with arbitrary accuracy using a variant of existing value-function iteration procedures. I find the Kydland-Prescott approximate decision rules are very similar to those implied by value-function iteration.
Palavra-chave: Production function, Optimization, Growth model, Markov chain, State space, and Decision rule Sujeito: C40 - Econometric and Statistical Methods: Special Topics: General
Creator: Turdaliev, Nurlan Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 596 Abstract:
In a repeated game of incomplete information, myopic players form beliefs on next-period play and choose strategies to maximize next-period payoffs. Beliefs are treated as forecast of future plays. Forecast accuracy is assessed using calibration tests, which measure asymptotic accuracy of beliefs against some realizations. Beliefs are calibrated if they pass all calibration tests. For a positive Lebesgue measure of payoff vectors, beliefs are not calibrated. But, if payoff vector and calibration test are drawn from a suitable product measure, beliefs pass the calibration test almost surely.
Sujeito: C10 - Econometric and Statistical Methods and Methodology: General, C72 - Noncooperative Games, and C70 - Game Theory and Bargaining Theory: General
Creator: Beauchemin, Kenneth Ronald Series: Staff report (Federal Reserve Bank of Minneapolis. Research Department) Number: 493 Abstract:
This paper describes recent modifications to the mixed-frequency model vector autoregression (MF-VAR) constructed by Schorfheide and Song (2012). The changes to the model are restricted solely to the set of variables included in the model; all other aspects of the model remain unchanged. Forecast evaluations are conducted to gauge the accuracy of the revised model to standard benchmarks and the original model.
Palavra-chave: Forecasting and Bayesian Vector Autoregression Sujeito: C32 - Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models, C11 - Bayesian Analysis: General, and C53 - Forecasting Models; Simulation Methods
Creator: Geweke, John Series: Staff report (Federal Reserve Bank of Minneapolis. Research Department) Number: 192 Abstract:
This is a survey of simulation methods in economics, with a specific focus on integration problems. It describes acceptance methods, importance sampling procedures, and Markov chain Monte Carlo methods for simulation from univariate and multivariate distributions and their application to the approximation of integrals. The exposition gives emphasis to combinations of different approaches and assessment of the accuracy of numerical approximations to integrals and expectations. The survey illustrates these procedures with applications to simulation and integration problems in economics.
Creator: Baxter, Marianne, 1956- Series: Nonlinear rational expectations modeling group Abstract:
This paper develops a new method for approximating dynamic competitive equilibria in economies in which competitive equilibrium is not necessarily Pareto optimal. The method involves finding approximate equilibrium policy functions by iterating on the stochastic Euler equations which characterize the economy's equilibrium. Two applications are presented: the stochastic growth model of Brock and Mirman (1971) modified to allow distortionary taxation, and a model of inflation and capital accumulation based on Stockman (1981). The computational speed and accuracy of this approach suggests that it may be a feasible method for studying suboptimal economies with large state spaces.
Sujeito: C61 - Mathematical methods and programming - Optimization techniques ; Programming models ; Dynamic analysis, E51 - Monetary policy, central banking, and the supply of money and credit - Money supply ; Credit ; Money multipliers, and C63 - Mathematical methods and programming - Computational techniques ; Simulation modeling
Creator: Anderson, Paul A. and Supel, Thomas M. Series: Working paper (Federal Reserve Bank of Minneapolis. Research Department) Number: 039 Abstract:
This paper puts forward a method for improving the forecasting accuracy of an existing macroeconometric model without changing its policy response characteristics. The procedure is an extension and formalization of the practice of additive adjustments currently used by most forecasters. The method should be of special interest to forecasters who use models built by other investigators because it does not involve reestimation of the original model and uses only information routinely included in the documentation available to model users. The paper ends with a demonstration of the prediction improvement realized by application of this method to a version of the MIT-Penn-SSRC (MPS) model.
Palavra-chave: Multiperiod forecasting, MIT-Penn-SSRC model, MIT-Penn-MPS model, and Prediction Sujeito: C53 - Forecasting Models; Simulation Methods and C52 - Model Evaluation, Validation, and Selection