Properties of Optimal Forecasts under Asymmetric Loss and Nonlinearity

By Andrew J. Patton, Allan G. Timmermann. 2007

197 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/96d6c1321fe014f52ac2eefc00fd93af8297ab66

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In Economics and Econometrics

In Management Science and Operations Research

Citations in the corpus

Exchange Rate Predictability (2013, unknown): “The former tests take this into account in evaluating the relative predictive ability of competing models by re-centering the MSFE di¤erential in the test statistic by a term 68See Mincer and Zarnowitz (1969), West and McCracken (1998), and Patton and Timmermann (2006, 2007).”
Focused Information Criteria, Model Selection, and Model Averaging in a Tobit Model With a Nonzero Threshold (2012, background): “The recent related work of Pötscher (2006) may serve as a useful guide in this regard.”
Bias in Federal Reserve Inflation Forecasts: Is the Federal Reserve Irrational or Just Cautious? (2005, unknown): “…condition (FOC): E [ L ′ ( πt+l − πTt+l,t, φ ) | Ωt ] = 0, (17) where L ′ ( πt+l − πTt+l,t, φ ) denotes the derivative of the loss function with respect to the control error.47 Following Granger (1999) and Patton and Timmermann (2004), this derivative will be called the generalized error.”
Volatility Forecast Comparison Using Imperfect Volatility Proxies (2006, unknown): “…common approach is to take the forecast user's loss function as given and derive the optimal forecast for that loss function; related papers here are Granger (1969), Engle (1993), Christoffersen and Diebold (1997), Christoffersen and Jacobs (2004) and Patton and Timmermann (2007), amongst others.”
Testing Forecast Optimality Under Unknown Loss (2007, unknown): “For example, assuming linex loss and a first-order Markov Gaussian mixture model, Patton and Timmermann (2006) derived the properties of the optimal forecast and forecast errors.”
Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets (2010, unknown): “Under the assumption of correct specification and a mean squared error (MSE) loss function, the optimal forecast of y, given the information set, is its conditional expectation (see Patton and Timmermann (2007a, 2007b)).”
Out-of-Sample Forecast Tests Robust to the Choice of Window Size (2011, unknown): “In those situations, Patton and Timmermann (2007) showed that a “generalized forecast error” does satisfy the same properties.”
Comparing Possibly Misspecified Forecasts (2020, background): “Similarly, in the literature on economic forecasting under asymmetric loss (see, e.g., Granger 1969; Christoffersen and Diebold 1997; Patton and Timmermann 2007), it is generally thought that asymmetric loss functions necessarily lead to optimal forecasts that differ from the conditional mean (they…”