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on Econometric Time Series |
| By: | K. Istrefi; B. Vonnak |
| Abstract: | Some authors argue that the delayed overshooting puzzle often found in the literature is an artifact of improper identification of monetary policy shocks, like Cholesky ordering. We investigate this claim by estimating the dynamic effect of monetary policy shocks on exchange rate using various identification schemes, where the data is generated by a small open economy DSGE model. We find that, on large sample, Cholesky type of restrictions perform comparably with model-consistent sign restrictions approach and do not produce the puzzle artificially. On short samples, however, Cholesky restrictions produce a more uncertain estimate for the shape of the exchange rate than sign restrictions. |
| Keywords: | Monetary Policy; Exchange Rate; DSGE; Vector Autoregressions; Cholesky Decomposition; Sign restrictions. |
| JEL: | E52 F41 C32 |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:bfr:banfra:576&r=ets |
| By: | Majid M. Al-Sadoon |
| Abstract: | The methodology of multivariate Granger non-causality testing at various horizons is extended to allow for inference on its directionality. This paper presents empirical manifestations of these subspaces and provides useful interpretations for them. It then proposes methods for estimating these subspaces and finding their dimensions utilizing simple vector autoregressions modelling that is easy to implement. The methodology is illustrated by an application to empirical monetary policy. |
| Keywords: | Granger causality, VAR model, rank testing, Okun's law, policy trade-offs |
| JEL: | C12 C13 C15 C32 C53 E3 E4 E52 |
| Date: | 2015–11 |
| URL: | http://d.repec.org/n?u=RePEc:bge:wpaper:850&r=ets |
| By: | Zhongjun Qu (Boston University); Denis Tkachenko (National University of Singapore) |
| Abstract: | This paper presents a framework for analyzing global identification in log linearized DSGE models that encompasses both determinacy and indeterminacy. First, it considers a frequency domain expression for the Kullback-Leibler distance between two DSGE models, and shows that global identification fails if and only if the minimized distance equals zero. This result has three features. (1) It can be applied across DSGE models with different structures. (2) It permits checking whether a subset of frequencies can deliver identification. (3) It delivers parameter values that yield observational equivalence if there is identification failure. Next, the paper proposes a measure for the empirical closeness between two DSGE models for a further understanding of the strength of identification. The measure gauges the feasibility of distinguishing one model from another based on a finite number of observations generated by the two models. It is shown to be equal to the highest possible power in a Gaussian model under a local asymptotic framework. The above theory is illustrated using two small scale and one medium scale DSGE models. The results document that certain parameters can be identified under indeterminacy but not determinacy, that different monetary policy rules can be (nearly) observationally equivalent, and that identification properties can differ substantially between small and medium scale models. For implementation, two procedures are developed and made available, both of which can be used to obtain and thus to cross validate the findings reported in the empirical applications. Although the paper focuses on DSGE models, the results are also applicable to other vector linear processes with well defined spectra, such as the (factor augmented) vector autoregression. |
| Keywords: | Dynamic stochastic general equilibrium models, frequency domain, global identification, multiple equilibria, spectral density |
| JEL: | C10 C30 C52 E1 E3 |
| Date: | 2015–08 |
| URL: | http://d.repec.org/n?u=RePEc:bos:wpaper:wp2015-002&r=ets |
| By: | Zhongjun Qu (Boston University) |
| Abstract: | This paper builds upon the composite likelihood concept of Lindsay (1988) to develop a framework for parameter identification, estimation, inference and forecasting in DSGE models allowing for stochastic singularity. The framework consists of the following four components. First, it provides a necessary and sufficient condition for parameter identification, where the identifying information is provided by the first and second order properties of the nonsingular submodels. Second, it provides an MCMC based procedure for parameter estimation. Third, it delivers confidence sets for the structural parameters and the impulse responses that allow for model misspecification. Fourth, it generates forecasts for all the observed endogenous variables, irrespective of the number of shocks in the model. The framework encompasses the conventional likelihood analysis as a special case when the model is nonsingular. Importantly, it enables the researcher to start with a basic model and then gradually incorporate more shocks and other features, meanwhile confronting all the models with the data to assess their implications. The methodology is illustrated using both small and medium scale DSGE models. These models have numbers of shocks ranging between one and seven. |
| Keywords: | business cycle, dynamic stochastic general equilibrium models, identification, impulse response, MCMC, stochastic singularity |
| JEL: | C13 C32 C51 E1 |
| Date: | 2015–06 |
| URL: | http://d.repec.org/n?u=RePEc:bos:wpaper:wp2015-003&r=ets |
| By: | Zhongjun Qu (Boston University); Fan Zhuo (Boston University) |
| Abstract: | Markov regime switching models are widely considered in economics and finance. Although there have been persistent interests (see e.g., Hansen, 1992, Garcia, 1998, and Cho and White, 2007), the asymptotic distributions of likelihood ratio based tests have remained unknown. This paper considers such tests and establishes their asymptotic distributions in the context of non- linear models allowing for multiple switching parameters. The analysis simultaneously addresses three difficulties: (i) some nuisance parameters are unidentified under the null hypothesis, (ii) the null hypothesis yields a local optimum, and (iii) conditional regime probabilities follow stochastic processes that can only be represented recursively. Addressing these issues permits substantial power gains in empirically relevant situations. Besides obtaining the tests' asymptotic distributions, this paper also obtains four sets of results that can be of independent interest: (1) a characterization of conditional regime probabilities and their high order derivatives with respect to the model's parameters, (2) a high order approximation to the log likelihood ratio permitting multiple switching parameters, (3) a refinement to the asymptotic distribution, and (4) a unified algorithm for simulating the critical values. For models that are linear under the null hypothesis, the elements needed for the algorithm can all be computed analytically. The above results also shed light on why some bootstrap procedures can be inconsistent and why standard information criteria, such as the Bayesian information criterion (BIC), can be sensitive to the hypothesis and the model's structure. When applied to the US quarterly real GDP growth rates, the methods suggest fairly strong evidence favoring the regime switching specification, which holds consistently over a range of sample periods. |
| Keywords: | Hypothesis testing, likelihood ratio, Markov switching, nonlinearity |
| JEL: | C12 C22 E32 |
| Date: | 2015–10 |
| URL: | http://d.repec.org/n?u=RePEc:bos:wpaper:wp2015-004&r=ets |
| By: | Peter Farkas; Laszlo Matyas |
| Abstract: | This paper introduces a nonparametric, non-asymptotic method for statistical testing based on boundary crossing events. The method is presented by showing it’s use for unit root testing. Two versions of the test are discussed. The first is designed for time series data as well as for cross sectionally independent panel data. The second is taking into account cross-sectional dependence as well. Through Monte Carlo studies we show that the proposed tests are more powerful than existing unit root tests when the error term has t-distribution and the sample size is small. The paper also discusses two empirical applications. The first one analyzes the possibility of mean reversion in the excess returns for the S&P500. Here, the unobserved mean is identified using Shiller’s CAPE ratio. Our test supports mean reversion, which can be interpreted as evidence against strong efficient market hypothesis. The second application cannot confirm the PPP hypothesis in exchange-rate data of OECD countries. |
| Date: | 2015–11–03 |
| URL: | http://d.repec.org/n?u=RePEc:ceu:econwp:2015_5&r=ets |
| By: | Cimadomo, Jacopo; D'Agostino, Antonello |
| Abstract: | In this paper, we propose a time-varying parameter VAR model with stochastic volatility which allows for estimation on data sampled at different frequencies. Our contribution is twofold. First, we extend the methodology developed by Cogley and Sargent (2005), and Primiceri (2005), to a mixed-frequency setting. In particular, our approach allows for the inclusion of two different categories of variables (high-frequency and low-frequency) into the same time varying model. Second, we use this model to study the macroeconomic effects of government spending shocks in Italy over the 1988Q4-2013Q3 period. Italy - as well as most other euro area economies - is characterised by short quarterly time series for fiscal variables, whereas annual data are generally available for a longer sample before 1999. Our results show that the proposed time-varying mixed-frequency model improves on the performance of a simple linear interpolation model in generating the true path of the missing observations. Second, our empirical analysis suggests that government spending shocks tend to have positive effects on output in Italy. The fiscal multiplier, which is maximized at the one year horizon, follows a U-shape over the sample considered: it peaks at around 1.5 at the beginning of the sample, it then stabilizes between 0.8 and 0.9 from the mid-1990s to the late 2000s, before rising again to above unity during of the recent crisis. JEL Classification: C32, E62, H30, H50 |
| Keywords: | government spending multiplier, mixed-frequency data, time variation |
| Date: | 2015–10 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20151856&r=ets |
| By: | Joshua C.C. Chan |
| Abstract: | We introduce a class of large Bayesian vector autoregressions (BVARs) that allows for non-Gaussian, heteroscedastic and serially dependent innovations. To make estimation computationally tractable, we exploit a certain Kronecker structure of the likelihood implied by this class of models. We propose a unified approach for estimating these models using Markov chain Monte Carlo (MCMC) methods. In an application that involves 20 macroeconomic variables, we find that these BVARs with more flexible covariance structures outperform the standard variant with independent, homoscedastic Gaussian innovations in both in-sample model-fit and out-of-sample forecast performance. |
| Keywords: | stochastic volatility, non-Gaussian, ARMA, forecasting |
| JEL: | C11 C51 C53 |
| Date: | 2015–11 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2015-41&r=ets |
| By: | Joshua C.C. Chan |
| Abstract: | We propose an easy technique to test for time-variation in coefficients and volatilities. Specifically, by using a noncentered parameterization for state space models, we develop a method to directly calculate the relevant Bayes factor using the Savage-Dickey density ratio—thus avoiding the computation of the marginal likelihood altogether. The proposed methodology is illustrated via two empirical applications. In the first application we test for time-variation in the volatility of inflation in the G7 countries. The second application investigates if there is substantial time-variation in the NAIRU in the US. |
| Keywords: | Bayesian model comparison, state space, inflation uncertainty, NAIRU |
| JEL: | C11 C32 E31 E52 |
| Date: | 2015–11 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2015-42&r=ets |
| By: | DIAF, Sami |
| Abstract: | This paper deals with a special class of multifractal models called the Multifractal Random Walk which has been widely used in finance because of its parsimonious framework, featuring many properties of financial data not considered in traditional linear models. Using the log-normal version, results confirm the Algerian Dinar is a multifractal process and has a rich wider variation spectrum versus the US Dollar than the Euro. |
| Keywords: | multifractal processes, stochastic volatility |
| JEL: | C5 F37 G15 |
| Date: | 2015–02 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:67619&r=ets |
| By: | Aue, Alexander; Horvath, Lajos; Pellatt, Daniel |
| Abstract: | Heteroskedasticity is a common feature of financial time series and is commonly addressed in the model building process through the use of ARCH and GARCH processes. More recently multivariate variants of these processes have been in the focus of research with attention given to methods seeking an efficient and economic estimation of a large number of model parameters. Due to the need for estimation of many parameters, however, these models may not be suitable for modeling now prevalent high-frequency volatility data. One potentially useful way to bypass these issues is to take a functional approach. In this paper, theory is developed for a new functional version of the generalized autoregressive conditionally heteroskedastic process, termed fGARCH. The main results are concerned with the structure of the fGARCH(1,1) process, providing criteria for the existence of a strictly stationary solutions both in the space of square-integrable and continuous functions. An estimation procedure is introduced and its consistency verified. A small empirical study highlights potential applications to intraday volatility estimation. |
| Keywords: | Econometrics; Financial time series; Functional data; GARCH processes; Stationary solutions |
| JEL: | C1 C13 C4 |
| Date: | 2015–08–20 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:67702&r=ets |
| By: | David E. Allen (The University of Sydney, The University of South Australia, Australia); Michael McAleer (National Tsing Hua University, Taiwan; Erasmus University Rotterdam, the Netherlands; Complutense University of Madrid, Spain); Shelton Peiris (The University of Sydney, Australia); Abhay K. Singh (Edith Cowan University, Australia) |
| Abstract: | This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non-linear models, including smooth transition regression models, logistic smooth transition regressions models, threshold autoregressive models, nonlinear autoregressive models, and additive nonlinear autoregressive models, plus Neural Network models.The results suggest that there is no dominating class of time series models, and the different currency pairs relationships with the US dollar are captured best by neural net regression models, over the ten year sample of daily exchange rate returns data, from August 2005 to August 2015. |
| Keywords: | Non linear models; time series; non-parametric; smooth-transition regression models; neural networks; GMDH shell |
| JEL: | C45 C53 F3 G15 |
| Date: | 2015–11–06 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20150125&r=ets |