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on Econometric Time Series |
| By: | Ole E. Barndorff-Nielsen; Peter Reinhard Hansen; Asger Lunde; Neil Shephard (School of Economics and Management, University of Aarhus, Denmark) |
| Abstract: | We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement noise of certain types and can also handle non-synchronous trading. It is the first estimator which has these three properties which are all essential for empirical work in this area. We derive the large sample asymptotics of this estimator and assess its accuracy using a Monte Carlo study. We implement the estimator on some US equity data, comparing our results to previous work which has used returns measured over 5 or 10 minutes intervals. We show the new estimator is substantially more precise. |
| Keywords: | HAC estimator, Long run variance estimator, Market frictions, Quadratic variation, Realised variance |
| JEL: | C13 C32 |
| Date: | 2008–12–11 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2008-63&r=ets |
| By: | Christian Kascha (Norges Bank (Central Bank of Norway)); Francesco Ravazzolo (Norges Bank (Central Bank of Norway)) |
| Abstract: | In this paper, we empirically evaluate competing approaches for combining inflation density forecasts in terms of Kullback-Leibler divergence. In particular, we apply a similar suite of models to four different data sets and aim at identifying combination methods that perform well throughout different series and variations of the model suite. We pool individual densities using linear and logarithmic combination methods. The suite consists of linear forecasting models with moving estimation windows to account for structural change. We find that combining densities is a much better strategy than selecting a particular model ex-ante. While combinations do not always perform better than the best individual model, combinations always yield accurate forecasts and, as we show analytically, provide insurance against selecting inappropriate models. Combining with equal weights often outperforms other weighting schemes. Also, logarithmic combinations can be advantageous, in particular if symmetric densities are preferred. |
| Keywords: | Forecast Combination, Logarithmic Combinations, Density Forecasts, Inflation Forecasting |
| JEL: | C53 E37 |
| Date: | 2008–12–12 |
| URL: | http://d.repec.org/n?u=RePEc:bno:worpap:2008_22&r=ets |
| By: | Andersson, Fredrik N. G. (Department of Economics, Lund University) |
| Abstract: | Economic theory commonly distinguishes between different time horizons such as the short run and the long run, each with its own relationships and its own dynamics. Engle (1974) proposed a bandspectrum regression to estimate such models. This paper proposes a new estimator for non-stationary panel data models, a bandspectrum cointegration estimator. The bandspectrum cointegration estimator uses first differenced data to avoid spurious results. Such estimates are, however, less efficient than estimates from a model with non-stationary data. Still, simulation results in the paper show that the bandspectrum cointegration estimator is more efficient than common time domain estimators, for example VECM and OLS levels estimators, if the data generating process contains more than one time horizon. The BSCE furthermore identifies all horizons in the data generating process and estimates an individual parameter vector for each, a property that neither time domain estimator possesses. |
| Keywords: | Cointegration; Bandspectrum Regression; Simulations; Wavelets; Frequency domain |
| JEL: | C14 C15 C23 |
| Date: | 2008–12–02 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:lunewp:2008_018&r=ets |
| By: | Gengenbach Christian; Urbain Jean-Pierre; Westerlund Joakim (METEOR) |
| Abstract: | This paper considers a cointegrated panel data model with common factors. Starting from the triangular representation of the model as used by Bai et al. (2008) a Granger type representation theorem is derived. The conditional error correction representation is obtained, which is used as a basis for developing two new tests for the null hypothesis of noerror correction. The asymptotic distributions of the tests are shown to be free of nuisanceparameters, depending only on the number of non-stationary variables. However, the tests are not cross-sectionally independent, which makes pooling difficult. Nevertheless, the averages of the tests converge in distribution. This makes pooling possible in spite of the cross-sectional dependence. We investigate the nite sample performance of the proposed tests in a Monte Carlo experiment and compare them to the tests proposed by Westerlund (2007). We also present two empirical applications of the new tests. |
| Keywords: | econometrics; |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:umamet:2008051&r=ets |
| By: | D. Aristei (Department of Economics, Finance and Statistics, University of Perugia); Luca Pieroni (Department of Economics, Finance and Statistics, University of Perugia and Depratment of Economics, University of the West of England, Bristol) |
| Abstract: | Private demand systems provide a practical application for analyzing identification issues in cointegration analysis. The paper conducts Montecarlo simulation experiments of cointegrated demand systems by assuming non-separability of government consumption. This framework enables further to test the robustness of models under alternative empirical specifications in which the homogeneity restriction is assumed to hold. The results highlight that separability of utility function with respect to government spending and the over-inclusion of lagged dependent variables introduce important bias in identifying the long run demand system, while the model specification with homogeneity restriction perform better when the theoretical hypothesis is contained in the data.Length: 34 pages |
| Keywords: | Non-separable structural models, Demand systems, Homogeneity. |
| JEL: | C52 C32 D12 H40 |
| Date: | 2008–11 |
| URL: | http://d.repec.org/n?u=RePEc:uwe:wpaper:0809&r=ets |
| By: | Enzo Weber |
| Abstract: | In the literature of identifcation through autoregressive conditional heteroscedasticity, Weber (2008) developed the structural constant conditional correlation (SCCC) model. Besides determining linear simultaneous in uences between several variables, this model considers interaction in the structural innovations. Even though this allows for common fundamental driving forces, these cannot explain time variation in correlations of observed variables, which still have to rely on causal transmission eects. In this context, the present paper extends the analysis to structural dynamic conditional correlation (SDCC). The additional fexibility is shown to make an important contribution in the estimation of empirical real-data examples. |
| Keywords: | Simultaneity, Identifcation, EGARCH, DCC |
| JEL: | C32 G10 |
| Date: | 2008–12 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2008-069&r=ets |
| By: | Sophie Bereau; Antonia Lopez Villavicencio; Valerie Mignon |
| Abstract: | We study the nonlinear dynamics of the real exchange rate towards its behavioral equilibrium value (BEER) using a Panel Smooth Transition Regression model framework.We show that the real exchange rate convergence process in the long run is characterized by nonlinearities for emerging economies, whereas industrialized countries exhibit a linear pattern. Moreover, there exists an asymmetric behavior of the real exchange rate when facing an over- or an undervaluation of the domestic currency. Finally, our results suggest that the real exchange rate is unable to unwind alone global imbalances. |
| Keywords: | Equilibrium exchange rate; BEER model; panel smooth transition regression; panel vector error correction model |
| JEL: | F31 C23 |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:cii:cepidt:2008-23&r=ets |
| By: | Rossi, Eduardo; Spazzini, Filippo |
| Abstract: | Multivariate GARCH models are in principle able to accommodate the features of the dynamic conditional correlations processes, although with the drawback, when the number of financial returns series considered increases, that the parameterizations entail too many parameters.In general, the interaction between model parametrization of the second conditional moment and the conditional density of asset returns adopted in the estimation determines the fitting of such models to the observed dynamics of the data. This paper aims to evaluate the interactions between conditional second moment specifications and probability distributions adopted in the likelihood computation, in forecasting volatilities and covolatilities. We measure the relative performances of alternative conditional second moment and probability distributions specifications by means of Monte Carlo simulations, using both statistical and financial forecasting loss functions. |
| Keywords: | Multivariate GARCH models; Model uncertainty; Quasi-maximum likelihood; Monte Carlo methods |
| JEL: | C32 C52 C01 |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:12260&r=ets |