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on Econometrics |
| By: | Osman Dogan (Ph.D. Program in Economics, City University of New York Graduate Center); Suleyman Taspinar (Ph.D. Program in Economics, City University of New York Graduate Center) |
| Abstract: | We consider a spatial econometric model containing a spatial lag in the dependent variable and the disturbance term with an unknown form of heteroskedasticity in innovations. We first prove that the maximum likelihood (ML) estimator for spatial autoregressive models is generally inconsistent when heteroskedasticity is not taken into account in the estimation. We show that the necessary condition for the consistency of the ML estimator of spatial autoregressive parameters depends on the structure of the spatial weight matrices. Then, we extend the robust generalized method of moment (GMM) estimation approach in Lin and Lee (2010) for the spatial model allowing for a spatial lag not only in the dependent variable but also in the disturbance term. We show the consistency of the robust GMM estimator and determine its asymptotic distribution. Finally, through a comprehensive Monte Carlo simulation, we compare finite sample properties of the robust GMM estimator with other estimators proposed in the literature. |
| Keywords: | spatial autoregressive models, unknown heteroskedasticity, robustness, GMM, asymptotics, MLE |
| JEL: | C13 C21 C31 |
| Date: | 2013–12–16 |
| URL: | http://d.repec.org/n?u=RePEc:cgc:wpaper:001&r=ecm |
| By: | Arturas Juodis |
| Abstract: | This paper considers the Panel Vector Autoregressive Models of order 1 (PVAR(1)) with possibly spatially dependent error terms. We propose a simple Method of Moments based cointegration test using the rank test of Kleibergen and Paap (2006) for fixed number of time observations. The test is shown to be robust to spatial dependence, cross-sectional and time series heteroscedasticity as well as unbalanced panels. The main novelty of our approach is that we fully exploit the "weakness" of the Anderson and Hsiao (1982) moment conditions in construction of the new test. The finite-sample performance of the proposed test statistic is investigated using the simulated data. The results show that for most scenarios the method performs well in terms of both size and power. The proposed test is applied to employment and wage equations using Spanish firm data of Alonso-Borrego and Arellano (1999) and the results show little evidence for cointegration. |
| Date: | 2013–10–03 |
| URL: | http://d.repec.org/n?u=RePEc:ame:wpaper:1308&r=ecm |
| By: | Dalia Jazmín Valencia García; Rosa E. Lillo; Juan Romo |
| Abstract: | Measuring dependence is a basic question when dealing with functional observations. The usual correlation for curves is not robust. Kendall's coefficient is a natural description of dependence between finite dimensional random variables. We extend this concept to functional observations. Given a bivariate sample of functions, a robust analysis of dependence can be carried out through the functional version of a Kendall correlation coefficient introduced in this paper. We also study its statistical properties and provide several applications to both simulated and real data, including asset portfolios in finance and microarray time series in genetics |
| Keywords: | Dependence , Functional data , Concordance , Kendall's tau |
| Date: | 2013–12 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws133228&r=ecm |
| By: | Osman Dogan (Ph.D. Program in Economics, City University of New York Graduate Center) |
| Abstract: | In this study, we investigate the necessary condition for the consistency of the maximum like- lihood estimator (MLE) of spatial models that have a spatial moving average process in the disturbance term (for short SARMA(1,1)). We show that the maximum likelihood estimator (MLE) of the spatial autoregressive and spatial moving average parameters is generally incon- sistent when heteroskedasticity is not considered in the estimation. The necessary condition for the consistency of the MLE depends on the structure of the spatial weight matrices. We also show that the inconsistency of the spatial autoregressive and spatial moving average parameters contaminates the MLE of the parameters of the exogenous variables. A Monte Carlo simulation study provides evaluation of the performance of the MLE in the presence of heteroskedastic innovations. The simulation results indicate that the MLE imposes substantial amount of bias on both autoregressive and moving average parameters. However, they also show that the MLE imposes almost no bias on the parameters of the exogenous variables in moderate sample sizes. |
| Keywords: | spatial dependence, spatial moving average, spatial autoregressive, maximum likelihood estimator, MLE, asymptotics, heteroskedasticity, SARMA(1,1) |
| JEL: | C13 C21 C31 |
| Date: | 2013–12–16 |
| URL: | http://d.repec.org/n?u=RePEc:cgc:wpaper:002&r=ecm |
| By: | Dalia Jazmín Valencia García; Rosa E. Lillo; Juan Romo |
| Abstract: | We present a notion of Spearman's coefficient for functional data that extends the classical bivariate concept to situations where the observed data are curves generated by a stochastic process. Since Spearman's coefficient for bivariate samples is based on the natural data ordering in dimension one, we need to consider a data order in the functional context where a natural order between functions does not exist. The development uses a pre-order inspired in a depth definition but considering a down-up ordering instead of a center-outwards ordering of the sample. We show some of the main characteristics of Spearman's coefficient for functions and propose an independence test with a bootstrap methodology. We illustrate the performance of the new coefficient with both simulated and real data |
| Keywords: | Spearman's coefficient; , Dependence , Functional data , Grades |
| Date: | 2013–12 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws133329&r=ecm |
| By: | Masayoshi Hayashi (Faculty of Economics, The University of Tokyo) |
| Abstract: | 　　 This study proposes decomposition and estimation methods that can be applied to analyze both regional stabilization and redistribution. The method proposed herein follows the approach taken by Shorrocks (1982), and applies it to per-capita level quantities of the relevant variables rather than the log-linear quantities used by Asdrubali et al. (1996) for regional stabilization and the normalized per-capita quantities used by Bayoumi and Masson (1995) for regional redistribution. I directly calculate the proportional contributions to the decomposition and bootstrap their confidence intervals rather than indirectly obtain them as OLS estimates from the artificial regressions by Asdrubali et al. (1996). I then apply the proposed method to Japanese prefectural accounts data so that we can compare the presented analysis with those in previous studies. Furthermore, I also apply the method to municipal budgetary data in Japan in order to demonstrate its usefulness. |
| Date: | 2013–12 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2013cf910&r=ecm |
| By: | Shutes, Karl; Adcock, Chris |
| Abstract: | This paper considers the impact of using the regularisation techniques for the analysis of the extended skew-normal distribution. The approach is estimated using a number of techniques and compared to OLS based LASSO and ridge regressions in addition to non- constrained skew-normal regression. |
| Keywords: | Skew-normal; LASSO; l1 regression |
| JEL: | C1 C13 C16 C46 |
| Date: | 2013–11–24 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:52217&r=ecm |
| By: | Martin Paldam (Department of Economics and Business, Aarhus University) |
| Abstract: | Economic research typically runs J regressions for each selected for publication – it is often selected as the ‘best’ of the regressions. The paper examines five possible meanings of the word ‘best’: SR0 is ideal selection with no bias; SR1 is polishing: selection by statistical fit; SR2 is censoring: selection by the size of estimate; SR3 selects the optimal combination of fit and size; and SR4 selects the first satisficing result. The last four SRs are steered by priors and result in bias. The MST and the FAT-PET have been developed for detection and correction of such bias. The simulations are made by data variation, while the model is the same. It appears that SR0 generates narrow funnels much at odds with observed funnels, while the other four funnels look more realistic. SR1 to SR4 give the mean a substantial bias that confirms the prior causing the bias. The FAT-PET MRA works well in finding the true value. |
| Keywords: | Meta-analysis, selection of regressions, publication bias |
| JEL: | B4 C9 |
| Date: | 2013–12–13 |
| URL: | http://d.repec.org/n?u=RePEc:aah:aarhec:2013-27&r=ecm |
| By: | Ramos, Arturo; Sanz-Gracia, Fernando; González-Val, Rafael |
| Abstract: | We study the US city size distribution using the Census places data, without size restriction, for the period (1900-2010). Also, we use the recently introduced US City Clustering Algorithm (CCA) data for 1991 and 2000. We compare the lognormal, two distributions named after Ioannides and Skouras (2013) and the double Pareto lognormal with two newly introduced distributions. The empirical results are overwhelming: One of the new distributions widely outperform any of the previously used density functions for each type of data. We also develop a theory which generates the new distributions based on the standard geometric Brownian motion for the population in the short term. We propose some extensions of the theory in order to deal with the long term empirical features. |
| Keywords: | US city size distribution, population thresholds, lower and upper tail, new statistical distributions |
| JEL: | C13 C16 R00 |
| Date: | 2013–12–13 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:52190&r=ecm |
| By: | Antonio, Katrien; Zhang, Yanwei |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:ner:leuven:urn:hdl:123456789/428972&r=ecm |
| By: | Antonio, Katrien; Zhang, Yanwei |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:ner:leuven:urn:hdl:123456789/428973&r=ecm |
| By: | Jonas E. Arias; Juan F. Rubio-Ramirez; Daniel F. Waggoner |
| Abstract: | Are optimism shocks an important source of business cycle fluctuations? Are decit-nanced tax cuts better than decit-nanced spending to increase output? These questions have been previously studied using SVARs identied with sign and zero restrictions and the answers have been positive and denite in both cases. While the identication of SVARs with sign and zero restrictions is theoretically attractive because it allows the researcher to remain agnostic with respect to the responses of the key variables of interest, we show that current implementation of these techniques does not respect the agnosticism of the theory. These algorithms impose additional sign restrictions on variables that are seemingly unrestricted that bias the results and produce misleading condence intervals. We provide an alternative and ecient algorithm that does not introduce any additional sign restriction, hence preserving the agnosticism of the theory. Without the additional restrictions, it is hard to support the claim that either optimism shocks are an important source of business cycle fluctuations or decit-nanced tax cuts work best at improving output. Our algorithm is not only correct but also faster than current ones. |
| Keywords: | SVARs; Sign and Zero Restrictions; Optimism and Fiscal Shocks |
| JEL: | C10 |
| Date: | 2013–12 |
| URL: | http://d.repec.org/n?u=RePEc:bbv:wpaper:1338&r=ecm |