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on Econometric Time Series |
| By: | Zacharias Psaradakis; Martin Sola; Nicola Spagnolo; Patricio Yunis |
| Abstract: | We examine the small-sample accuracy of impulse responses obtained using local projections (LP) and vector autoregressive (VAR) models. In view of the fact that impulse responses are differences between multistep predictors, we propose to assess the relative performance of impulse-response estimators using tests for equal predictive accuracy. In our Monte Carlo experiments, LP-based and VAR-based estimators are found to be equally accurate in large samples under a mean squared error risk function. VAR-based estimators tend to have an advantage over LP-based ones in small and moderately sized samples, particularly at long horizons. |
| Keywords: | Local projections, Predictive accuracy, VAR models. |
| JEL: | C32 C53 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:udt:wpecon:2024_01&r= |
| By: | Emanuele Bacchiocchi; Toru Kitagawa |
| Abstract: | In this paper we propose a class of structural vector autoregressions (SVARs) characterized by structural breaks (SVAR-WB). Together with standard restrictions on the parameters and on functions of them, we also consider constraints across the different regimes. Such constraints can be either (a) in the form of stability restrictions, indicating that not all the parameters or impulse responses are subject to structural changes, or (b) in terms of inequalities regarding particular characteristics of the SVAR-WB across the regimes. We show that all these kinds of restrictions provide benefits in terms of identification. We derive conditions for point and set identification of the structural parameters of the SVAR-WB, mixing equality, sign, rank and stability restrictions, as well as constraints on forecast error variances (FEVs). As point identification, when achieved, holds locally but not globally, there will be a set of isolated structural parameters that are observationally equivalent in the parametric space. In this respect, both common frequentist and Bayesian approaches produce unreliable inference as the former focuses on just one of these observationally equivalent points, while for the latter on a non-vanishing sensitivity to the prior. To overcome these issues, we propose alternative approaches for estimation and inference that account for all admissible observationally equivalent structural parameters. Moreover, we develop a pure Bayesian and a robust Bayesian approach for doing inference in set-identified SVAR-WBs. Both the theory of identification and inference are illustrated through a set of examples and an empirical application on the transmission of US monetary policy over the great inflation and great moderation regimes. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.04973&r= |
| By: | Veldhuis, Sebastian (Department of Economics, University of Klagenfurt); Wagner, Martin (Bank of Slovenia, Ljubljana and Institute for Advanced Studies, Vienna) |
| Abstract: | We consider integrated modiï¬ ed least squares estimation for systems of cointegrating multivariate polynomial regressions, i. e., systems of regressions that include deterministic variables, integrated processes and products of these variables as regressors. The errors are allowed to be correlated across equations, over time and with the regressors. Since, under restrictions on the parameters or in case of non-identical regressors across equations, integrated modiï¬ ed OLS and GLS estimation do not, in general, coincide, we discuss in detail restricted integrated generalized least squares estimators and inference based upon them. Furthermore, we develop asymptotically pivotal ï¬ xed-b inference, available only in case of full design and for speciï¬ c hypotheses. |
| Keywords: | Integrated modiï¬ ed estimation, cointegrating multivariate polynomial regression, ï¬ xed-b inference, generalized least squares |
| JEL: | C12 C13 C32 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:ihs:ihswps:number54&r= |
| By: | H. Peter Boswijk; Jun Yu; Yang Zu |
| Abstract: | Based on a continuous-time stochastic volatility model with a linear drift, we develop a test for explosive behavior in financial asset prices at a low frequency when prices are sampled at a higher frequency. The test exploits the volatility information in the high-frequency data. The method consists of devolatizing log-asset price increments with realized volatility measures and performing a supremum-type recursive Dickey-Fuller test on the devolatized sample. The proposed test has a nuisance-parameter-free asymptotic distribution and is easy to implement. We study the size and power properties of the test in Monte Carlo simulations. A real-time date-stamping strategy based on the devolatized sample is proposed for the origination and conclusion dates of the explosive regime. Conditions under which the real-time date-stamping strategy is consistent are established. The test and the date-stamping strategy are applied to study explosive behavior in cryptocurrency and stock markets. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.02087&r= |
| By: | Battulga Gankhuu |
| Abstract: | This study introduces marginal density functions of the general Bayesian Markov-Switching Vector Autoregressive (MS-VAR) process. In the case of the Bayesian MS-VAR process, we provide closed--form density functions and Monte-Carlo simulation algorithms, including the importance sampling method. The Monte--Carlo simulation method departs from the previous simulation methods because it removes the duplication in a regime vector. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.11235&r= |
| By: | Savi Virolainen |
| Abstract: | Linear structural vector autoregressive models can be identified statistically without imposing restrictions on the model if the shocks are mutually independent and at most one of them is Gaussian. We show that this result extends to structural threshold and smooth transition vector autoregressive models incorporating a time-varying impact matrix defined as a weighted sum of the impact matrices of the regimes. Our empirical application studies the effects of the climate policy uncertainty shock on the U.S. macroeconomy. In a structural logistic smooth transition vector autoregressive model consisting of two regimes, we find that a positive climate policy uncertainty shock decreases production in times of low economic policy uncertainty but slightly increases it in times of high economic policy uncertainty. The introduced methods are implemented to the accompanying R package sstvars. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.19707&r= |
| By: | Joshua Brault |
| Abstract: | In this paper, I develop a population-based Markov chain Monte Carlo (MCMC) algorithm known as parallel tempering to estimate dynamic stochastic general equilibrium (DSGE) models. Parallel tempering approximates the posterior distribution of interest using a family of Markov chains with tempered posteriors. At each iteration, two randomly selected chains in the ensemble are proposed to swap parameter vectors, after which each chain mutates via Metropolis-Hastings. The algorithm results in a fast-mixing MCMC, particularly well suited for problems with irregular posterior distributions. Also, due to its global nature, the algorithm can be initialized directly from the prior distributions. I provide two empirical examples with complex posteriors: a New Keynesian model with equilibrium indeterminacy and the Smets-Wouters model with more diffuse prior distributions. In both examples, parallel tempering overcomes the inherent estimation challenge, providing extremely consistent estimates across different runs of the algorithm with large effective sample sizes. I provide code compatible with Dynare mod files, making this routine straightforward for DSGE practitioners to implement. |
| Keywords: | Econometric and statistical methods, Economic models |
| JEL: | C11 C15 E10 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:24-13&r= |