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on Econometric Time Series |
| By: | Leschinski, Christian; Voges, Michelle; Sibbertsen, Philipp |
| Abstract: | There are various competing procedures to determine whether fractional cointegration is present in a multivariate time series, but no standard approach has emerged. We provide a synthesis of this literature and conduct a detailed comparative Monte Carlo study to guide empirical researchers in their choice of appropriate methodologies. Special attention is paid on empirically relevant issues such as assumptions about the form of the underlying process and the ability of the procedures to distinguish between short-run correlation and long-run equilibria. It is found that several approaches are severely oversized in presence of correlated short-run components and that the methods show different performance in terms of power when applied to common-component models instead of triangular systems. |
| Keywords: | Long Memory; Fractional Cointegration; Semiparametric Estimation and Testing |
| JEL: | C14 C32 |
| Date: | 2019–01 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-651&r=all |
| By: | Wenjuan Chen; Aleksei Netsunajev |
| Keywords: | structural vector autoregression; Markov switching; time varying transition probabilities; identification via heteroscedasticity; uncertainty shocks; unemployment dynamics |
| JEL: | C32 D80 E24 |
| Date: | 2018–02–13 |
| URL: | http://d.repec.org/n?u=RePEc:eea:boewps:wp2018-02&r=all |
| By: | Doh, Taeyoung (Federal Reserve Bank of Kansas City); Smith, Andrew Lee (Federal Reserve Bank of Kansas City) |
| Abstract: | This paper proposes a novel Bayesian approach to jointly model realized data and survey forecasts of the same variable in a vector autoregression (VAR). In particular, our method imposes a prior distribution on the consistency between the forecast implied by the VAR and the survey forecast for the same variable. When the prior is placed on unconditional forecasts from the VAR, the prior shapes the posterior of the reduced-form VAR coefficients. When the prior is placed on conditional forecasts (specifically, impulse responses), the prior shapes the posterior of the structural VAR coefficients. {{p}} To implement our prior, we combine importance sampling with a maximum entropy prior for forecast consistency to obtain posterior draws of VAR parameters at low computational cost. We use two empirical examples to illustrate some potential applications of our methodology: (i) the evolution of tail risks for inflation in a time-varying parameter VAR model and (ii) the identification of forward guidance shocks using sign and forecast-consistency restrictions in a monetary VAR model. |
| Keywords: | Vector Autoregression (VAR); Survey Forecasts; Bayesian VAR; Inflation Risk; Forward Guidance |
| JEL: | C11 C32 E31 |
| Date: | 2018–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedkrw:rwp18-13&r=all |
| By: | Qihui Chen; Zheng Fang |
| Abstract: | This paper presents a unified second order asymptotic framework for conducting inference on parameters of the form $\phi(\theta_0)$, where $\theta_0$ is unknown but can be estimated by $\hat\theta_n$, and $\phi$ is a known map that admits null first order derivative at $\theta_0$. For a large number of examples in the literature, the second order Delta method reveals a nondegenerate weak limit for the plug-in estimator $\phi(\hat\theta_n)$. We show, however, that the `standard' bootstrap is consistent if and only if the second order derivative $\phi_{\theta_0}''=0$ under regularity conditions, i.e., the standard bootstrap is inconsistent if $\phi_{\theta_0}''\neq 0$, and provides degenerate limits unhelpful for inference otherwise. We thus identify a source of bootstrap failures distinct from that in Fang and Santos (2018) because the problem (of consistently bootstrapping a \textit{nondegenerate} limit) persists even if $\phi$ is differentiable. We show that the correction procedure in Babu (1984) can be extended to our general setup. Alternatively, a modified bootstrap is proposed when the map is \textit{in addition} second order nondifferentiable. Both are shown to provide local size control under some conditions. As an illustration, we develop a test of common conditional heteroskedastic (CH) features, a setting with both degeneracy and nondifferentiability -- the latter is because the Jacobian matrix is degenerate at zero and we allow the existence of multiple common CH features. |
| Date: | 2019–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1901.04861&r=all |
| By: | Christian Myohl |
| Abstract: | This paper studies the propagation and properties of a confidence shock in a structural vector autoregression (VAR) model with and without financial variables. The addition of a financial block does not considerably change the propagation and the contribution to the forecast error variance by the confidence shock. Nevertheless, for specific historical episodes, the inclusion of a financial block plays a role. In several recessions, the VAR with the financial block assigns a smaller role to confidence shocks for the fall in GDP. This suggests that the confidence shock may not be properly identified in a structural VAR when financial variables are omitted. Further, I identify a financial channel by which the confidence shock affects economic activity. |
| Keywords: | Confidence shocks, structural VARs, financial channel |
| JEL: | C32 E32 E44 |
| Date: | 2018–08 |
| URL: | http://d.repec.org/n?u=RePEc:ube:dpvwib:dp1821&r=all |
| By: | Francis Vitek |
| Abstract: | This paper considers the problem of jointly decomposing a set of time series variables into cyclical and trend components, subject to sets of stochastic linear restrictions among these cyclical and trend components. We derive a closed form solution to an ordinary problem featuring homogeneous penalty term difference orders and static restrictions, as well as to a generalized problem featuring heterogeneous penalty term difference orders and dynamic restrictions. We use our Generalized Multivariate Linear Filter to jointly estimate potential output, the natural rate of unemployment and the natural rate of interest, conditional on selected equilibrium conditions from a calibrated New Keynesian model. |
| Date: | 2018–12–10 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:18/275&r=all |
| By: | Chudik, Alexander (Federal Reserve Bank of Dallas); Pesaran, M. Hashem (University of Southern California); Mohaddes, Kamiar (University of Cambridge) |
| Abstract: | The paper contributes to the growing Global VAR (GVAR) literature by showing how global and national shocks can be identified within a GVAR framework. The usefulness of the proposed approach is illustrated in an application to the analysis of the interactions between public debt and real output growth in a multi-country setting, and the results are compared to those obtained from standard single-country VAR analysis. We find that on average (across countries) global shocks explain about one-third of the long-horizon forecast error variance of output growth, and about one-fifth of the long-run variance of the rate of change of debt-to-GDP. Evidence on the degree of cross-sectional dependence in these variables and their innovations is exploited to identify the global shocks, and priors are used to identify the national shocks within a Bayesian framework. It is found that posterior median debt elasticity with respect to output is much larger when the rise in output is due to a fiscal policy shock, as compared to when the rise in output is due to a positive technology shock. The cross-country average of the median debt elasticity is 1.58 when the rise in output is due to a fiscal expansion as compared to 0.75 when the rise in output follows from a favorable output shock. |
| Keywords: | Factor-augmented VARs; Global VARs; identification of global and country specific shocks; Bayesian analysis; public debt; output growth; debt elasticity |
| JEL: | C30 E62 H6 |
| Date: | 2018–12–26 |
| URL: | http://d.repec.org/n?u=RePEc:fip:feddgw:351&r=all |
| By: | Riccardo (Jack) Lucchetti (DISES, Facoltà di Economia "Giorgio Fuà "); Ioannis A. Venetis (Department of Economics, University of Patras) |
| Abstract: | This package deals with the estimation of dynamic factor models (DFM); for the moment, three factor extraction techniques are available, but we plan to add more in future versions. Further additions will include parameter restrictions. |
| Keywords: | Dynamic factor models, EM algorithm, Kalman filter, Principal components |
| JEL: | C32 C38 C87 |
| Date: | 2019–01 |
| URL: | http://d.repec.org/n?u=RePEc:anc:wgretl:7&r=all |