Econometric Time Series
http://lists.repec.org/mailman/listinfo/nep-ets
Econometric Time Series
2018-05-21
A Time-Space Dynamic Panel Data Model with Spatial Moving Average Errors
http://d.repec.org/n?u=RePEc:pra:mprapa:86371&r=ets
This paper focuses on the estimation and predictive performance of several estimators for the time-space dynamic panel data model with Spatial Moving Average Random Effects (SMA-RE) structure of the disturbances. A dynamic spatial Generalized Moments (GM) estimator is proposed which combines the approaches proposed by Baltagi, Fingleton and Pirotte (2014) and Fingleton (2008). The main idea is to mix non-spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a forecasting approach is proposed and a linear predictor is derived. Using Monte Carlo simulations, we compare the short-run and long-run effects and evaluate the predictive efficiencies of optimal and various suboptimal predictors using the Root Mean Square Error (RMSE) criterion. Last, our approach is illustrated by an application in geographical economics which studies the employment levels across 255 NUTS regions of the EU over the period 2001-2012, with the last two years reserved for prediction.
Baltagi, Badi H.
Fingleton, Bernard
Pirotte, Alain
Panel data; Spatial lag; Error components; Time-space; Dynamic;OLS; Within; GM; Spatial autocorrelation; Direct and indirect effects; Moving average; Prediction; Simulations, Rook contiguity, Interregional trade.
2018-04-18
Structural Breaks in Time Series
http://d.repec.org/n?u=RePEc:arx:papers:1805.03807&r=ets
This chapter covers methodological issues related to estimation, testing and computation for models involving structural changes. Our aim is to review developments as they relate to econometric applications based on linear models. Substantial advances have been made to cover models at a level of generality that allow a host of interesting practical applications. These include models with general stationary regressors and errors that can exhibit temporal dependence and heteroskedasticity, models with trending variables and possible unit roots and cointegrated models, among others. Advances have been made pertaining to computational aspects of constructing estimates, their limit distributions, tests for structural changes, and methods to determine the number of changes present. A variety of topics are covered. The first part summarizes and updates developments described in an earlier review, Perron (2006), with the exposition following heavily that of Perron (2008). Additions are included for recent developments: testing for common breaks, models with endogenous regressors (emphasizing that simply using least-squares is preferable over instrumental variables methods), quantile regressions, methods based on Lasso, panel data models, testing for changes in forecast accuracy, factors models and methods of inference based on a continuous records asymptotic framework. Our focus is on the so-called off-line methods whereby one wants to retrospectively test for breaks in a given sample of data and form confidence intervals about the break dates. The aim is to provide the readers with an overview of methods that are of direct usefulness in practice as opposed to issues that are mostly of theoretical interest.
Alessandro Casini
Pierre Perron
2018-05
How far can we forecast? Statistical tests of the predictive content
http://d.repec.org/n?u=RePEc:zbw:bubdps:072018&r=ets
Forecasts are useless whenever the forecast error variance fails to be smaller than the unconditional variance of the target variable. This paper develops tests for the null hypothesis that forecasts become uninformative beyond some limiting forecast horizon h. Following Diebold and Mariano (DM, 1995) we propose a test based on the comparison of the mean-squared error of the forecast and the sample variance. We show that the resulting test does not possess a limiting normal distribution and suggest two simple modifications of the DM-type test with different limiting null distributions. Furthermore, a forecast encompassing test is developed that tends to better control the size of the test. In our empirical analysis, we apply our tests to macroeconomic forecasts from the survey of Consensus Economics. Our results suggest that forecasts of macroeconomic key variables are barely informative beyond 2-4 quarters ahead.
Breitung, Jörg
Knüppel, Malte
Hypothesis Testing,Predictive Accuracy,Informativeness
2018
A mixture autoregressive model based on Student's $t$-distribution
http://d.repec.org/n?u=RePEc:arx:papers:1805.04010&r=ets
A new mixture autoregressive model based on Student's $t$-distribution is proposed. A key feature of our model is that the conditional $t$-distributions of the component models are based on autoregressions that have multivariate $t$-distributions as their (low-dimensional) stationary distributions. That autoregressions with such stationary distributions exist is not immediate. Our formulation implies that the conditional mean of each component model is a linear function of past observations and the conditional variance is also time varying. Compared to previous mixture autoregressive models our model may therefore be useful in applications where the data exhibits rather strong conditional heteroskedasticity. Our formulation also has the theoretical advantage that conditions for stationarity and ergodicity are always met and these properties are much more straightforward to establish than is common in nonlinear autoregressive models. An empirical example employing a realized kernel series based on S&P 500 high-frequency data shows that the proposed model performs well in volatility forecasting.
Mika Meitz
Daniel Preve
Pentti Saikkonen
2018-05
Spatiotemporal ARCH Models
http://d.repec.org/n?u=RePEc:toh:dssraa:82&r=ets
This study proposes spatiotemporal extensions of time series autoregressive conditional heteroskedasticity (ARCH) models. We call spatiotemporally extended ARCH models as spatiotemporal ARCH (ST-ARCH) models. ST-ARCH models specify conditional variances given simultaneous observations and past observations, which constitutes a good contrast with time series ARCH models that specify conditional variances given past own observations. We have proposed two types of ST-ARCH models based on cross-sectional correlations between error terms. A spatial weight matrix based on Fama-French 3 factor models are used to quantify the closeness between stock prices. We estimate the parameters in ST-ARCH models by a two-step procedure of the quasi maximum likelihood estimation method. We demonstrate the empirical properties of the models by simulation studies and real data analysis of stock price data in the Japanese market.
Takaki Sato
Yasumasa Matsuda
2018-05
Nonstationary cointegration in the fractionally cointegrated VAR model
http://d.repec.org/n?u=RePEc:kud:kuiedp:1804&r=ets
We consider the fractional cointegrated vector autoregressive (CVAR) model of Johansen and Nielsen (2012a) and make two distinct contributions. First, in their consistency proof, Johansen and Nielsen (2012a) imposed moment conditions on the errors that depend on the parameter space, such that when the parameter space is larger, stronger moment conditions are required. We show that these moment conditions can be relaxed, and for consistency we require just eight moments regardless of the parameter space. Second, Johansen and Nielsen (2012a) assumed that the cointegrating vectors are stationary, and we extend the analysis to include the possibility that the cointegrating vectors are nonstationary. Both contributions require new analysis and results for the asymptotic properties of the likelihood function of the fractional CVAR model, which we provide. Finally, our analysis follows recent research and applies a parameter space large enough that the usual (non-fractional) CVAR model constitutes an interior point and hence can be tested against the fractional model using a 2-test.
Søren Johansen
Morten Ørregaard Nielsen
Cointegration, fractional integration, likelihood inference, vector autoregressive model
2018-05-17
Cointegration and adjustment in the infinite order CVAR representation of some partially observed CVAR(1) models
http://d.repec.org/n?u=RePEc:kud:kuiedp:1805&r=ets
A multivariate CVAR(1) model for some observed variables and some unobserved variables is analysed using its infinite order CVAR representation of the observations. Cointegration and adjustment coefficients in the infinite order CVAR are found as functions of the parameters in the CVAR(1) model. Conditions for weak exogeneity of the cointegrating vectors in the approximating finite order CVAR are derived. The results are illustrated by a few simple examples of relevance for modelling causal graphs.
Søren Johansen
Adjustment coefficients, cointegrating coefficients, CVAR, causal models
2018-05-17
Specification tests for non-Gaussian maximum likelihood estimators
http://d.repec.org/n?u=RePEc:rim:rimwps:18-22&r=ets
We propose generalised DWH specification tests which simultaneously compare three or more likelihood-based estimators of conditional mean and variance parameters in multivariate conditionally heteroskedastic dynamic regression models. Our tests are useful for Garch models and in many empirically relevant macro and finance applications involving Vars and multivariate regressions. To design powerful and reliable tests, we determine the rank deficiencies of the differences between the estimators' asymptotic covariance matrices under the null of correct specification, and take into account that some parameters remain consistently estimated under the alternative of distributional misspecification. Finally, we provide finite sample results through Monte Carlo simulations.
Gabriele Fiorentini
Enrique Sentana
Durbin-Wu-Hausman Tests, Partial Adaptivity, Semiparametric Estimators, Singular Covariance Matrices
2018-05
Optimal Model Averaging of Mixed-Data Kernel-Weighted Spline Regressions
http://d.repec.org/n?u=RePEc:mcm:deptwp:2018-10&r=ets
Model averaging has a rich history dating from its use for combining forecasts from time-series models (Bates & Granger 1969) and presents a compelling alternative to model selection methods. We propose a frequentist model average procedure defined over categorical regression splines (Ma, Racine & Yang 2015) that allows for non-nested and heteroskedastic candidate models. Theoretical underpinnings are provided, finite-sample performance is evaluated, and an empirical illustration reveals that the method is capable of outperforming a range of popular model selection criteria in applied settings. An R package is available for practitioners (Racine 2017).
Jeffrey S. Racine
Qi Li
Li Zheng
2018-05
Are bootstrapped cointegration test findings unreliable?
http://d.repec.org/n?u=RePEc:zbw:fubsbe:20188&r=ets
Applied time series research often faces the challenge that (a) potentially relevant variables are unobservable, (b) it is fundamentally uncertain which covariates are relevant. Thus cointegration is often analyzed in partial systems, ignoring potential (stationary) covariates. By simulating hypothesized larger systems Benati (2015) found that a nominally significant cointegration outcome using a bootstrapped rank test (Cavaliere, Rahbek, and Taylor, 2012) in the bivariate sub-system might be due to test size distortions. In this note we review this issue systematically. Apart from revisiting the partial-system results we also investigate alternative bootstrap test approaches in the larger system. Throughout we follow the given application of a long-run Phillips curve (euro-area inflation and unemployment). The methods that include the covariates do not reject the null of no cointegration, but by simulation we find that they display very low power, such that the (bivariate) partial-system approach is still preferred. The size distortions of all approaches are only mild when a standard HP-filtered output gap measure is used among the covariates. The bivariate trace test p-value of 0.027 (heteroskedasticity-consistent wild bootstrap) therefore still suggests rejection of non-cointegration at the 5% but not at the 1% significance level. The earlier findings of considerable test size distortions can be replicated when instead an output gap measure with different longer-run developments is used. This detrimental effect of large borderline-stationary roots reflects an earlier insight from the literature (Cavaliere, Rahbek, and Taylor, 2015).
Schreiber, Sven
bootstrap,cointegration rank test,empirical size
2018
Discussion of “asymptotic theory of outlier detection algorithms for linear time series regression models” by Johansen and Nielsen
http://d.repec.org/n?u=RePEc:ehl:lserod:66724&r=ets
Atkinson, Anthony C.
Cerioli, Andrea
Riani, Marco
Fan plot; forward search; Mahalanobis distance; monitoring; robustness
2016-06-01