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Econometrics, Volume 3, Issue 1 (March 2015) , Pages 1-186

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Open AccessArticle
A Joint Chow Test for Structural Instability
Econometrics 2015, 3(1), 156-186; https://doi.org/10.3390/econometrics3010156 - 12 Mar 2015
Cited by 5 | Viewed by 3343
Abstract
The classical Chow test for structural instability requires strictly exogenous regressors and a break-point specified in advance. In this paper, we consider two generalisations, the one-step recursive Chow test (based on the sequence of studentised recursive residuals) and its supremum counterpart, which relaxes [...] Read more.
The classical Chow test for structural instability requires strictly exogenous regressors and a break-point specified in advance. In this paper, we consider two generalisations, the one-step recursive Chow test (based on the sequence of studentised recursive residuals) and its supremum counterpart, which relaxes these requirements. We use results on the strong consistency of regression estimators to show that the one-step test is appropriate for stationary, unit root or explosive processes modelled in the autoregressive distributed lags (ADL) framework. We then use the results in extreme value theory to develop a new supremum version of the test, suitable for formal testing of structural instability with an unknown break-point. The test assumes the normality of errors and is intended to be used in situations where this can be either assumed nor established empirically. Simulations show that the supremum test has desirable power properties, in particular against level shifts late in the sample and against outliers. An application to U.K. GDP data is given. Full article
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Open AccessArticle
Two-Step Lasso Estimation of the Spatial Weights Matrix
Econometrics 2015, 3(1), 128-155; https://doi.org/10.3390/econometrics3010128 - 09 Mar 2015
Cited by 13 | Viewed by 3039
Abstract
The vast majority of spatial econometric research relies on the assumption that the spatial network structure is known a priori. This study considers a two-step estimation strategy for estimating the n(n-1) interaction effects in a spatial autoregressive panel model where the spatial dimension [...] Read more.
The vast majority of spatial econometric research relies on the assumption that the spatial network structure is known a priori. This study considers a two-step estimation strategy for estimating the n(n-1) interaction effects in a spatial autoregressive panel model where the spatial dimension is potentially large. The identifying assumption is approximate sparsity of the spatial weights matrix. The proposed estimation methodology exploits the Lasso estimator and mimics two-stage least squares (2SLS) to account for endogeneity of the spatial lag. The developed two-step estimator is of more general interest. It may be used in applications where the number of endogenous regressors and the number of instrumental variables is larger than the number of observations. We derive convergence rates for the two-step Lasso estimator. Our Monte Carlo simulation results show that the two-step estimator is consistent and successfully recovers the spatial network structure for reasonable sample size, T. Full article
(This article belongs to the Special Issue Spatial Econometrics)
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Open AccessArticle
Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with a Moving Average Disturbance Term
Econometrics 2015, 3(1), 101-127; https://doi.org/10.3390/econometrics3010101 - 26 Feb 2015
Cited by 1 | Viewed by 2380
Abstract
In this study, I investigate the necessary condition for the consistency of the maximum likelihood estimator (MLE) of spatial models with a spatial moving average process in the disturbance term. I show that the MLE of spatial autoregressive and spatial moving average parameters [...] Read more.
In this study, I investigate the necessary condition for the consistency of the maximum likelihood estimator (MLE) of spatial models with a spatial moving average process in the disturbance term. I show that the MLE of spatial autoregressive and spatial moving average parameters is generally inconsistent when heteroskedasticity is not considered in the estimation. I also show that the MLE of parameters of exogenous variables is inconsistent and determine its asymptotic bias. I provide simulation results to evaluate the performance of the MLE. The simulation results indicate that the MLE imposes a substantial amount of bias on both autoregressive and moving average parameters. Full article
(This article belongs to the Special Issue Spatial Econometrics)
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Open AccessArticle
Entropy Maximization as a Basis for Information Recovery in Dynamic Economic Behavioral Systems
Econometrics 2015, 3(1), 91-100; https://doi.org/10.3390/econometrics3010091 - 16 Feb 2015
Cited by 5 | Viewed by 2761
Abstract
As a basis for information recovery in open dynamic microeconomic systems, we emphasize the connection between adaptive intelligent behavior, causal entropy maximization and self-organized equilibrium seeking behavior. This entropy-based causal adaptive behavior framework permits the use of information-theoretic methods as a solution basis [...] Read more.
As a basis for information recovery in open dynamic microeconomic systems, we emphasize the connection between adaptive intelligent behavior, causal entropy maximization and self-organized equilibrium seeking behavior. This entropy-based causal adaptive behavior framework permits the use of information-theoretic methods as a solution basis for the resulting pure and stochastic inverse economic-econometric problems. We cast the information recovery problem in the form of a binary network and suggest information-theoretic methods to recover estimates of the unknown binary behavioral parameters without explicitly sampling the configuration-arrangement of the sample space. Full article
Open AccessArticle
Finding Starting-Values for the Estimation of Vector STAR Models
Econometrics 2015, 3(1), 65-90; https://doi.org/10.3390/econometrics3010065 - 29 Jan 2015
Cited by 7 | Viewed by 2377
Abstract
This paper focuses on finding starting-values for the estimation of Vector STAR models. Based on a Monte Carlo study, different procedures are evaluated. Their performance is assessed with respect to model fit and computational effort. I employ (i) grid search algorithms and (ii) [...] Read more.
This paper focuses on finding starting-values for the estimation of Vector STAR models. Based on a Monte Carlo study, different procedures are evaluated. Their performance is assessed with respect to model fit and computational effort. I employ (i) grid search algorithms and (ii) heuristic optimization procedures, namely differential evolution, threshold accepting, and simulated annealing. In the equation-by-equation starting-value search approach the procedures achieve equally good results. Unless the errors are cross-correlated, equation-by-equation search followed by a derivative-based algorithm can handle such an optimization problem sufficiently well. This result holds also for higher-dimensional Vector STAR models with a slight edge for heuristic methods. For more complex Vector STAR models which require a multivariate search approach, simulated annealing and differential evolution outperform threshold accepting and the grid search. Full article
(This article belongs to the Special Issue Non-Linear Regression Modeling)
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Open AccessArticle
On the Interpretation of Instrumental Variables in the Presence of Specification Errors
Econometrics 2015, 3(1), 55-64; https://doi.org/10.3390/econometrics3010055 - 29 Jan 2015
Cited by 8 | Viewed by 2759
Abstract
The method of instrumental variables (IV) and the generalized method of moments (GMM), and their applications to the estimation of errors-in-variables and simultaneous equations models in econometrics, require data on a sufficient number of instrumental variables that are both exogenous and relevant. We [...] Read more.
The method of instrumental variables (IV) and the generalized method of moments (GMM), and their applications to the estimation of errors-in-variables and simultaneous equations models in econometrics, require data on a sufficient number of instrumental variables that are both exogenous and relevant. We argue that, in general, such instruments (weak or strong) cannot exist. Full article
Open AccessArticle
Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity
Econometrics 2015, 3(1), 2-54; https://doi.org/10.3390/econometrics3010002 - 16 Jan 2015
Cited by 1 | Viewed by 2560
Abstract
We introduce and investigate some properties of a class of nonlinear time series models based on the moving sample quantiles in the autoregressive data generating process. We derive a test fit to detect this type of nonlinearity. Using the daily realized volatility data [...] Read more.
We introduce and investigate some properties of a class of nonlinear time series models based on the moving sample quantiles in the autoregressive data generating process. We derive a test fit to detect this type of nonlinearity. Using the daily realized volatility data of Standard & Poor’s 500 (S&P 500) and several other indices, we obtained good performance using these models in an out-of-sample forecasting exercise compared with the forecasts obtained based on the usual linear heterogeneous autoregressive and other models of realized volatility. Full article
(This article belongs to the Special Issue Non-Linear Regression Modeling)
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Open AccessEditorial
Acknowledgement to Reviewers of Econometrics in 2014
Econometrics 2015, 3(1), 1; https://doi.org/10.3390/econometrics3010001 - 09 Jan 2015
Cited by 1 | Viewed by 1738
Abstract
The editors of Econometrics would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2014:[...] Full article
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