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on Econometric Time Series |
| By: | Fabio Canova; Fernando J. Pérez Forero |
| Abstract: | This paper provides a method to estimate time varying coefficients structural VARs which are non-recursive and potentially overidentified. The procedure allows for linear and non-linear restrictions on the parameters, maintains the multi-move structure of standard algorithms and can be used to estimate structural models with different identification restrictions. We study the transmission of monetary policy shocks and compare the results with those obtained with traditional methods. |
| Keywords: | Non-recursive overidentified SVARs, Time-varying coefficient models, Bayesian methods, Monetary transmission mechanism |
| JEL: | C11 E51 E52 |
| Date: | 2012–05 |
| URL: | http://d.repec.org/n?u=RePEc:upf:upfgen:1321&r=ets |
| By: | Dante Amengual (CEMFI, Centro de Estudios Monetarios y Financieros); Gabriele Fiorentini (Università di Firenze and RCEA); Enrique Sentana (CEMFI, Centro de Estudios Monetarios y Financieros) |
| Abstract: | Sequential maximum likelihood and GMM estimators of distributional parameters obtained from the standardised innovations of multivariate conditionally heteroskedastic dynamic regression models evaluated at Gaussian PML estimators preserve the consistency of mean and variance parameters while allowing for realistic distributions. We assess the efficiency of those estimators, and obtain moment conditions leading to sequential estimators as efficient as their joint maximum likelihood counterparts. We also obtain standard errors for the quantiles required in VaR and CoVaR calculations, and analyse the effects on these measures of distributional misspecification. Finally, we illustrate the small sample performance of these procedures through Monte Carlo simulations. |
| Keywords: | Elliptical distributions, Efficient estimation, Systemic risk, Value at risk. |
| JEL: | C13 C32 G11 |
| Date: | 2012–02 |
| URL: | http://d.repec.org/n?u=RePEc:cmf:wpaper:wp2012_1201&r=ets |
| By: | Tommaso, Proietti; Alessandra, Luati |
| Abstract: | The purpose of this chapter is to provide a comprehensive treatment of likelihood inference for state space models. These are a class of time series models relating an observable time series to quantities called states, which are characterized by a simple temporal dependence structure, typically a first order Markov process. The states have sometimes substantial interpretation. Key estimation problems in economics concern latent variables, such as the output gap, potential output, the non-accelerating-inflation rate of unemployment, or NAIRU, core inflation, and so forth. Time-varying volatility, which is quintessential to finance, is an important feature also in macroeconomics. In the multivariate framework relevant features can be common to different series, meaning that the driving forces of a particular feature and/or the transmission mechanism are the same. The objective of this chapter is reviewing this algorithm and discussing maximum likelihood inference, starting from the linear Gaussian case and discussing the extensions to a nonlinear and non Gaussian framework. |
| Keywords: | Time series models; Unobserved components; |
| JEL: | C13 C22 |
| Date: | 2012–04–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:39600&r=ets |
| By: | John H. Cochrane |
| Abstract: | I translate familiar concepts of discrete-time time-series to contnuous-time equivalent. I cover lag operators, ARMA models, the relation between levels and differences, integration and cointegration, and the Hansen-Sargent prediction formulas. |
| JEL: | C01 C5 C58 E17 G17 |
| Date: | 2012–06 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:18181&r=ets |