This paper considers the theoretical justifications of Lütkpohl’s (1988) test statistics when the data-generating process is relaxed to be a stationary ARFIMA process. Under suitable regularity conditions, we prove the applicability of Lütkpohl’s (1988) method to the stationary ARFIMA (p,...

In this paper, we consider a model where producers set their prices based on their prediction of the aggregated price level and an exogenous variable, which can be a demand or a cost-push shock. To form their expectations, they use OLS-type econometric learning with bounded memory. We show that...

This paper evaluates weekly out-of-sample volatility forecast performance of univariate Mixed Data Sampling (MIDAS) model compared to the benchmark model of GARCH(1,1) for ten emerging stock markets. The results show that the MIDAS model offers a statistically better forecasting precision during...

This note presents a simple generalization of the adaptive expectations mechanism in which the learning parameter is time variant. Expectations generated in this way minimize mean squared forecast errors for any linear state space model.

Since the mid-1980s, Phillips curve forecasts of US inflation have been inferior to those of a conventional causal autoregression. However, little change in forecast accuracy is detected against the benchmark of a noncausal autoregression, more accurately characterizing US inflation dynamics.

This paper revisits the generalized adaptive expectations (GAE) mechanism presented by Shepherd (2012) [When are adaptive expectations rational? A generalization, Economics Letters, 115, 4–6]. It provides the precise conditions under which GAE hold, and also discusses its implications for the...

This paper suggests using a unit t-value criterion in imposing restrictions on lags to formulate a subset vector autoregressive (VAR) model for the purpose of point forecasts. Among any other alternative models nested to the initial VAR model, this less restrictive modeling strategy produces the...

We examine the efficiency of German forecasts for output growth and inflation allowing for an asymmetric loss function of the forecasters. We find the loss of output growth forecasts to be approximately symmetric while there is an asymmetry in the loss of the inflation forecasts. The information...

We transpose the Generalized Impulse-Response Function (GIRF) developed by Koop et al. (1996) to Markov-Switching structural VARs. As the algorithm displays an exponentially increasing complexity as regards the prediction horizon, we use the collapsing technique to easily obtain simulated...

A new method of assessing the comparative quality of forecasting models is introduced. This method focuses on the quality of forecasting models over a set of series (cf. the traditionally adopted series-by-series approach)–with a forecasting model that produces good forecasts over a series set...