Nonlinear Regression with Dependent Observations

2011

The asymptotic properties of the least squares estimator of the cusp in some nonlinear nonregular regression models is investigated via the study of the weak convergence of the least squares process generalizing earlier results in Prakasa Rao (Statist. Prob. Lett. 3 (1985), 15-18).

Defence and Peace Economics, 2009

± We would like to thank Nicola Spagnolo for his valuable suggestions. The usual disclaimer applies.

Review of Economics, 2013

In this study, we examine the relationship between foreign direct investment (FDI) and terrorist incidents that took place in Turkey during the period 1991:12 to 2003:12. By doing so we contribute to the literature by allowing for a possible nonlinear relationship between terrorism and FDI. The data used to measure the intensity of terrorism were collected from a major newspaper of Turkey, and therefore is limited to the direct signals given to the market. Empirical evidence from both linear and non-linear models confirms that terrorism has a large negative impact on foreign direct investment. As far as the results of the nonlinear model estimation are concerned, the impact of terrorism on FDI is estimated to be more severe during periods of high terrorism where the intensity of terrorism passes a certain threshold level. This threshold level can be interpreted as a warning ‘signal’ that FDI may decrease severely and thereby can be used by policy makers to design effective policy me...

The Journal of Finance, 1993

An important issue in applications of multifactor models of asset returns is the appropriate number of factors. Most extant tests for the number of factors are valid only for strict factor models, in which diversifiable returns are uncorrelated across assets. In this paper we develop a test statistic to determine the number of factors in an approximate factor model of asset returns, which does not require that diversifiable components of returns be uncorrelated across assets. We find evidence for one to six pervasive factors in the cross-section of New York Stock Exchange and American Stock Exchange stock returns.

Empirical Economics, 2013

We replicate Shaw (J Labor Econ 14(4): 1996) who found that individual wage growth is higher for individuals with greater preference for risk taking. Expanding her dataset with more American observations and data for Germany, Spain, and Italy, we find evidence that risk attitudes are relevant but support is mixed at best for the original specifications. Wage growth • Risk • Post-school investment JEL Classification J24 • J30 Electronic supplementary material The online version of this article

Journal of Applied Economics, 2003

The low power of available econometric tests is an important problem in applied research on unit roots and related issues. Based on the principle of methodological triangulation, the problem should be analyzed from different points of view in order to increase the validity of the results. Following this approach a strategy to test the order of integration in time series is presented using a sequence of eleven consolidated tests. In this way it is possible to determine the persistence of shocks, to specify the best strategy for trend-cycle decomposition and to obtain additional information useful for public policies. As an application of the methodology, the integration properties in the main 14 Argentine macroeconomic variables are studied. A classification of them in four homogenous groups according to their order of integration is obtained.

Journal of Applied Econometrics, 1994

2021

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

Quarterly Journal of the Royal Meteorological Society, 2012

The problem of variational data assimilation for a nonlinear evolution model is formulated as an optimal control problem to find the initial condition function (analysis). The data contain errors (observation and background errors); hence there is an error in the analysis. For mildly nonlinear dynamics the analysis error covariance can be approximated by the inverse Hessian of the cost functional in the auxiliary data assimilation problem, and for stronger nonlinearity by the 'effective' inverse Hessian. However, it has been noticed that the analysis error covariance is not the posterior covariance from the Bayesian perspective. While these two are equivalent in the linear case, the difference may become significant in practical terms with the nonlinearity level rising. For the proper Bayesian posterior covariance a new approximation via the Hessian is derived and its 'effective' counterpart is introduced. An approach for computing the mentioned estimates in the matrixfree environment using the Lanczos method with preconditioning is suggested. Numerical examples which validate the developed theory are presented for the model governed by Burgers equation with a nonlinear viscous term.

Environmetrics, 2014

Comparisons of trends across climatic data sets are complicated by the presence of serial correlation and possible step-changes in the mean. We build on heteroskedasticity and autocorrelation (HAC) robust methods, specifically the Vogelsang-Franses (VF) nonparametric variance estimator, to allow for a step-change in the mean at a known or unknown date. The VF method provides a powerful multivariate trend estimator robust to unknown serial correlation up to but not including unit roots. We show that the critical values change when the mean shift occurs at a known or unknown date. We derive an asymptotic approximation that can be used to simulate critical values, and we outline a simple bootstrap procedure that generates valid critical values and p-values. Our application builds on the literature comparing simulated and observed trends in the tropical lower-and midtroposphere since 1958. The method identifies a shift in observations relative to models in 1977, coinciding with the Pacific Climate Shift. Allowing for a level shift causes apparently significant observed trends to become statistically insignificant. Model over-estimation of warming is significant whether or not we account for a level shift, although null rejections are much stronger when the level shift is included.

Econometric Theory, 1985

In this paper we develop nonparametric estimators of the joint time series data generating process (DGP) of (xt, yt) at different t-values, of conditional DGP, of the conditional mean of xt given the past values of x and y, and, more generally, the conditional mean of (xt, yt) given their past values (vector autoregression). We establish, among other results, the central limit theorems for these estimators under far weaker mixing conditions than those used in Robinson [23], where only the xt series is considered. Uniform consistency and rate results for the consistencies of various estimators are also obtained. The results of the paper are useful in light of the fact that often the functional form of the dynamic regression is not known and also the assumption of the Gaussian process is not true.

Economic Notes, 2013

Physical scarcity is hardly sufficient to explain commodity price swings. However, despite of clues of commodity market inefficiency in the last decade, excess volatility in commodity markets emerges only under strong assumptions. When we allow for non-stationarity in commodity prices and time variation in commodity-specific risk premia, evidence of commodity market inefficiency becomes significantly weaker. Moreover, there is some evidence of commodity-specific regime changes in commodity markets, with negligible or even positive correlation between efficiency and market liquidity.

Journal of Approximation Theory, 2020

Ce document a été généré automatiquement le 18 avril 2019. La dynamique de la qualification dans l'ajustement marchand ? Le cas d'une fi...

Econometric Theory, 2012

In this paper we propose a new nonparametric test for conditional heteroskedasticity based on a measure of nonparametric goodness-of-fit (R2) that is obtained from the local polynomial regression of the residuals from a parametric regression on some covariates. We show that after being appropriately standardized, the nonparametric R2 is asymptotically normally distributed under the null hypothesis and a sequence of Pitman local alternatives. We also prove the consistency of the test and propose a bootstrap method to obtain the bootstrap p-values. We conduct a small set of simulations and compare our test with some popular parametric and nonparametric tests in the literature.

Communications in Statistics - Theory and Methods, 2016

In this paper, we propose a semi-parametric mode regression for a NONLINEAR model. We use an expectation-maximization algorithm in order to estimate the regression coefficients of modal non linear regression. We also establish asymptotic properties for the proposed estimator under assumptions of the error density. We investigate the performance through a simulation study.