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,...

While the yield spread has long been recognized as a good predictor of recessions, it seems to have been largely overlooked by professional forecasters. We examine this puzzle, established by Rudebusch and Williams (2009), in a data-rich environment including not just the yield spread but many...

Temporal aggregation is known to affect the persistence of time series. We study the aggregation of flow variables as well as stock data, and difference-stationarity is allowed for. Moreover, moving averages encountered when computing annual growth rates (seasonal differences) are investigated....

In this paper we present new evidence on the aggregate effect of the euro on trade using data for 26 OECD countries for the period 1967–2008. We strive to fill the gaps present in the previous literature through a second-generation panel cointegration tests and estimators that account for both...

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 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 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 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 study shows that China’s Consumer Expectation Index contains useful information about pure expectation shocks, which are unrelated to economic fundamentals. It turns out that such shocks are likely to be an important independent driver of industrial output growth.