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The return forecasting factor is a linear combination of forward rates that seems to predict 1-year excess bond returns of bond of all maturities better than traditional measures obtained from the yield curve. If this single factor actually captures all the relevant fluctuations in bond risk premia, then it should also summarize all the economically relevant variations in excess returns considering different holding periods. We find that it does not. We conclude that including the return forecasting factor as the main driver of risk premia in a term structure model, as has been suggested, is not supported by the data.

Model fitting for count time series is of great relevance for many economic applications. Here, we focus on the step of model selection, where information criteria like AIC and BIC are commonly used in practice. Previous studies about their model selection abilities concentrated on real-valued time series, but here, we comprehensively investigate AIC and BIC in a count time series context. In our simulations, we consider diverse scenarios of model selection, like the identification of serial (in)dependence, overdispersion, zero inflation or a trend, the order selection within a given model family as well as the model selection also across model families. We apply our findings to economic count time series about monthly numbers of strikes in the US, and about monthly numbers of corporate insolvencies in the districts of Rhineland-Palatinate.

This paper shows that the trimmed Whittle estimation of the SVAR is superior to filtering (or differencing) undesired, low-frequency fluctuations that may arise in macroeconomic data. Pre-filtering destroys the low-frequency range of the spectrum, thus biasing the estimated parameters and the responses of the variables to shocks. The proposed method, by contrast, accounts for the undesired fluctuations while overcoming these drawbacks. Furthermore, the method remains reliable even when the observed low-frequency variability has been incorrectly considered as external to the SVAR. An empirical application that examines the effect of technology shocks on hours worked is provided to illustrate the results. We find the response of hours positive and similar using both long and short-run identification restrictions, thus providing a solution to a wide debate in the business cycle literature.

Markov regime-switching (MRS) autoregressive model is a widely used approach to model the economic and financial data with potential structural breaks. The innovation series of such MRS-type models are usually assumed to follow a Normal distribution, which cannot accommodate fat-tailed properties commonly present in empirical data. Many theoretical studies suggest that this issue can lead to inconsistent estimates. In this paper, we consider the tempered stable distribution, which has the attractive stability under aggregation property missed in other popular alternatives like Student’s t-distribution and General Error Distribution (GED). Through systematically designed simulation studies with the MRS autoregressive models, our results demonstrate that the model with tempered stable distribution uniformly outperforms those with Student’s t-distribution and GED. Our empirical study on the implied volatility of the S&P 500 options (VIX) also leads to the same conclusions. Therefore, we argue that the tempered stable distribution could be widely used for modelling economic and financial data in general contexts with an MRS-type specification.

In this paper, we develop new threshold cointegration tests with SETAR and MTAR adjustment allowing for the presence of structural breaks in the equilibrium equation. We propose a simple procedure to simultaneously estimate the previously unknown breakpoint and test the null hypothesis of no cointegration. Thereby, we extend the well-known residual-based cointegration test with regime shift introduced by (Gregory, A. W., and B. E. Hansen. 1996a. “Residual-based Tests for Cointegration in Models with Regime Shifts.” Journal of Econometrics 70: 99–126) to include forms of nonlinear adjustment. We derive the asymptotic distribution of the test statistics and demonstrate the finite-sample performance of the tests in a series of Monte Carlo experiments. We find a substantial decrease of power of the conventional threshold cointegration tests caused by a shift in the slope coefficient of the equilibrium equation. The proposed tests perform superior in these situations. An application to the “rockets and feathers” hypothesis of price adjustment in the US gasoline market provides empirical support for this methodology.

The decisions to reduce, leave unchanged, or increase a choice variable (such as policy interest rates) are often characterized by abundant status quo outcomes that can be generated by different processes. The decreases and increases may also be driven by distinct decision-making paths. Neither conventional nor zero-inflated models for ordinal responses adequately address these issues. This paper develops a flexible endogenously switching model with three latent regimes, which create separate processes for interest rate hikes and cuts and overlap at a no-change outcome, generating three different types of status quo decisions. The model is not only favored by statistical tests but also produces economically more meaningful inference with respect to the existing models, which deliver biased estimates in the simulations.