|
on Econometric Time Series |
| By: | David Kohns; Noa Kallionen; Yann McLatchie; Aki Vehtari |
| Abstract: | We present the ARR2 prior, a joint prior over the auto-regressive components in Bayesian time-series models and their induced $R^2$. Compared to other priors designed for times-series models, the ARR2 prior allows for flexible and intuitive shrinkage. We derive the prior for pure auto-regressive models, and extend it to auto-regressive models with exogenous inputs, and state-space models. Through both simulations and real-world modelling exercises, we demonstrate the efficacy of the ARR2 prior in improving sparse and reliable inference, while showing greater inference quality and predictive performance than other shrinkage priors. An open-source implementation of the prior is provided. |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2405.19920&r= |
| By: | Francq, Christian; Zakoian, Jean-Michel |
| Abstract: | We investigate the problem of testing the finiteness of moments for a class of semi-parametric time series encompassing many commonly used specifications. The existence of positive-power moments of the strictly stationary solution is characterized by the Moment Determining Function (MDF) of the model, which depends on the parameter driving the dynamics and on the distribution of the innovations. We establish the asymptotic distribution of the empirical MDF, from which tests of moments are deduced. Alternative tests based on estimation of the Maximal Moment Exponent (MME) are studied. Power comparisons based on local alternatives and the Bahadur approach are proposed. We provide an illustration on real financial data and show that semi-parametric estimation of the MME provides an interesting alternative to Hill's nonparametric estimator of the tail index. |
| Keywords: | Efficiency comparisons of tests; maximal moment exponent; stochastic recurrence equation; tail index |
| JEL: | C12 C32 C58 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:121193&r= |
| By: | Demian Pouzo; Zacharias Psaradakis; Martín Sola |
| Abstract: | We consider general hidden Markov models that may include exogenous covariates and whose discrete-state-space regime sequence has transition probabilities that are functions of observable variables. We show that the parameters of the observation conditional distribution are consistently estimated by quasi-maximum-likelihood even if the Markov dependence of the hidden regime sequence is not taken into account. Some related numerical results are also discussed. |
| Keywords: | Consistency; covariate-dependent transition probabilities; hidden Markov model; mixture model; quasi-maximum-likelihood; misspecified model. |
| JEL: | C22 C32 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:udt:wpecon:2024_04&r= |
| By: | Ippei Fujiwara; Adrian Pagan |
| Abstract: | McKay and Wolf (2023) describe a method for ï¬ nding counterfactuals which only requires that one know the impulse responses of shocks from a baseline structural model generating the data. A key feature in their work is the use of news shocks. This an elegant piece of theory and they indicate it can be applied empirically. We argue that one cannot recover the impulse responses from data generated by the structural model when there are news shocks as there are more shocks than observables in that case. We investigate an alternative proposal whereby some off model variables are used to ï¬ nd the requisite impulse responses and ï¬ nd that there are issues with doing that. Their theoretical result also relies upon the baseline structural model only having monetary policy operating via the interest rate channel so it excludes models that might be thought relevant for capturing data. |
| Keywords: | counterfactual, news shocks, shock recovery, local projection |
| JEL: | C3 E3 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2024-44&r= |
| By: | Jin Seo Cho (Yonsei University) |
| Abstract: | This study examines the large sample behavior of an ordinary least squares (OLS) estimator when a nonlinear autoregressive distributed lag (NARDL) model is correctly specified for nonstationary data. Although the OLS estimator suffers from an asymptotically singular matrix problem, it is consistent for unknown model parameters, and follows a mixed normal distribution asymptotically. We also examine the large sample behavior of the standard Wald test defined by the OLS estimator for asymmetries in long- and short-run NARDL parameters, and further supplement it by noting that the long-run parameter estimator is not super-consistent. Using Monte Carlo simulations, we then affirm the theory on the Wald test. Finally, using the U.S. GDP and exogenous fiscal shock data provided by Romer and Romer (2010, American Economic Review), we find statistical evidence for long-and short-run symmetries between tax increase and decrease in relation to the U.S. GDP. |
| Keywords: | Nonlinear autoregressive distributed lag model; OLS estimation; Singular matrix; Limit distribution; Wald test; Exogenous fiscal shocks; GDP. |
| JEL: | C12 C13 C22 E62 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:yon:wpaper:2024rwp-227&r= |
| By: | Andrea Bucci |
| Abstract: | This paper proposes a sequential test procedure for determining the number of regimes in nonlinear multivariate autoregressive models. The procedure relies on linearity and no additional nonlinearity tests for both multivariate smooth transition and threshold autoregressive models. We conduct a simulation study to evaluate the finite-sample properties of the proposed test in small samples. Our findings indicate that the test exhibits satisfactory size properties, with the rescaled version of the Lagrange Multiplier test statistics demonstrating the best performance in most simulation settings. The sequential procedure is also applied to two empirical cases, the US monthly interest rates and Icelandic river flows. In both cases, the detected number of regimes aligns well with the existing literature. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.02152&r= |
| By: | del Barrio Castro, Tomas; Escribano, Alvaro; Sibbertsen, Philipp |
| Abstract: | This paper identifies and estimates the relevant cycles in paleoclimate data of earth temperature, ice volume and CO2. Cyclical cointegration analysis is used to connect these cycles to the earth eccentricity and obliquity and to see that the earth surface temperature and ice volume are closely connected. These findings are used to build a forecasting model including the cyclical component as well as the relevant earth and climate variables which outperforms models ignoring the cyclical behaviour of the data. Especially the turning points can be predicted accurately using the proposed approach. Out of sample forecasts for the turning points of earth temperature, ice volume and CO2 are derived. |
| Keywords: | Paleoclimate Cycles, Cyclical Fractional Cointegration, Forecasting Climate Data |
| JEL: | C22 C51 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:han:dpaper:dp-722&r= |
| By: | Bin Peng; Liangjun Su; Yayi Yan |
| Abstract: | In this paper, we propose an easy-to-implement residual-based specification testing procedure for detecting structural changes in factor models, which is powerful against both smooth and abrupt structural changes with unknown break dates. The proposed test is robust against the over-specified number of factors, and serially and cross-sectionally correlated error processes. A new central limit theorem is given for the quadratic forms of panel data with dependence over both dimensions, thereby filling a gap in the literature. We establish the asymptotic properties of the proposed test statistic, and accordingly develop a simulation-based scheme to select critical value in order to improve finite sample performance. Through extensive simulations and a real-world application, we confirm our theoretical results and demonstrate that the proposed test exhibits desirable size and power in practice. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.00941&r= |