GARCH (1,1) models are widely used for modelling processes with time varying volatility. These include financial time series, which can be particularly heavy tailed. In this paper, we propose a log-transform-based least squares estimator (LSE) for the GARCH (1,1) model. The asymptotic properties...

This paper proposes a Cramer-von Mises (CM) test statistic to check the adequacy of weak ARMA models. Without posing a martingale difference assumption on the error terms, the asymptotic null distribution of the CM test is obtained by using the Hillbert space approach. Moreover, this CM test is...

In this paper, we study parametric nonlinear regression under the Harris recurrent Markov chain framework. We first consider the nonlinear least squares estimators of the parameters in the homoskedastic case, and establish asymptotic theory for the proposed estimators. Our results show that the...

This note defines the asymmetric count data, first order moving average model and gives some of its basic properties. A brief account of conditional least squares estimation of unknown parameters is also given.

This study implements Mathematica to estimate a system of national accounts. The estimation methods applied are portrayed in Danilov and Magnus (2008), including the Bayesian estimation, restricted and unrestricted least-squares estimation and best linear unbiased estimation. Operationalizing...

We investigate the time-varying ARCH (tvARCH) process. It is shown that it can be used to describe the slow decay of the sample autocorrelations of the squared returns often observed in financial time series, which warrants the further study of parameter estimation methods for the model. Since...

Purpose – The aim of this study is to develop an applicable and detailed model for customer lifetime value (CLV) and to highlight the most important indicators relevant for a specific industry – namely the banking sector. Design/methodology/approach – This study compares the results of the...

This paper proposes using the Gaussian approximation, also known as quantile coupling, to estimate a quantile model. The quantile coupling allows one to apply the standard Gaussian-based estimation and inference to the transformed data set. The resulting estimator is asymptotically normal with a...

The growth curve model is a useful tool for studying the growth problems, repeated measurements and longitudinal data. A key point using the growth curve model to fit data is determining the degree of polynomial profile form, choosing suitable explanatory variables, shrinking some regression...

In this paper we estimate the parameters in the stochastic SIS epidemic model by using pseudo-maximum likelihood estimation (pseudo-MLE) and least squares estimation. We obtain the point estimators and <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$100 (1-\alpha )\%$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mrow> <mn>100</mn> <mo stretchy="false">(</mo> <mn>1</mn> <mo>-</mo> <mi mathvariant="italic">α</mi> <mo stretchy="false">)</mo> <mo>%</mo> </mrow> </math> </EquationSource> </InlineEquation> confidence intervals as well as <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$$100...</equationsource></inlineequation></equationsource></equationsource></inlineequation>

This article presents discussions on the optimal and robust designs for trigonometric regression models under different optimality criteria. First, we investigate the classical Q-optimal designs for estimating the response function in a full trigonometric regression model with a given order. The...

A rigorous methodology is proposed to study cell division data consisting in several observed genealogical trees of possibly different shapes. The procedure takes into account missing observations, data from different trees, as well as the dependence structure within genealogical trees. Its main...

A least squares estimation approach for the estimation of a GARCH (1,1) modelis developed. The asymptotic properties of the estimator are studied given mild regularity conditions, which require only that the error term has a conditionalmomen t of some order. We establish the consistency,...

In this paper we discuss the calibration issues of power models built on mean-reverting processes combined with long memory. The unknown parameters of fractional mean-reversion processes are estimated by a hybrid estimation method, which is built upon the marriage of the quadratic variation and...

Methods for the parameter estimation for a spatio-temporal marked point process model, the so-called growth-interaction model, are investigated. Least squares estimation methods for this model found in the literature are only concerned with fitting the mark distribution observed in the data....

This paper studies the least squares estimator (LSE) of the multiple-regime threshold autoregressive (TAR) model and establishes its asymptotic theory. It is shown that the LSE is strongly consistent. When the autoregressive function is discontinuous over each threshold, the estimated thresholds...

The purpose of this paper is to introduce and examine two alternative, although similar, approaches to the Moving Blocks and subsampling Bootstraps to bootstrapping the estimator of the parameters for time series regression models. More specifically, the first bootstrap is based on resampling...

The purpose of this paper is to introduce and examine two alternative, although similar, approaches to the Moving Blocks and subsampling Bootstraps to bootstrapping the estimator of the parameters for time series regression models. More specifically, the first bootstrap is based on resampling...

It is well-know that estimation by reduced rank regression is given by the solution to a generalized eigenvalue problem. This paper presents a new proof to establish this result and provides additional insight into the structure of the estimation problem. The proof is a direct algebraic proof...

The behavior of averaged periodograms and cross-periodograms of a broad class of nonstationary processes is studied. The processes include nonstationary ones that are fractional of any order, as well as asymptotically stationary fractional ones. The cross-periodogram can involve two...

The behaviour of averaged periodograms and cross-periodograms of a broad class of nonstationary processes is studied. The processes include nonstationary ones that are fractional of any order, as well as asymptotically stationary fractional ones. The cross-periodogram can involve two...

The behaviour of averaged periodograms and cross-periodograms of a broad class of nonstationary processes is studied. The processes include nonstationary ones that are fractional of any order, as well as asymptotically stationary fractional ones. The cross-periodogram can involve two...

A common assumption for data analysis in functional magnetic resonance imaging is that the response signal can be modelled as the convolution of a haemodynamic response (HDR) kernel with a stimulus reference function. Early approaches modelled spatially constant HDR kernels, but more recently...

This paper is a rejoinder to Kliman's (2008-9) reply to a paper published in the >i>Journal of Post Keynesian Economics>/i> by Díaz and Osuna (2005-6). We show that Kliman's reasoning about spurious correlation rests on the popular confusion between correlation and causation.

The Makeham distribution has been used to describe human mortality and establish actuarial tables. The hazard function is defined by μ(t)=A+BCt, we use the least squares type estimation to estimate the parameters of Makeham distribution in this paper. Seven cases are considered, when A, B, C...

The paper studies the forecasting of a future size distribution of plants. As a model we use an open Markov chain model for macro data. Estimation is by reparametrization instead of by inequality restrictions using single equation least squares. The estimator is studied in a small Monte Carlo...

A least squares estimation approach for the estimation of a GARCH (1,1) model is developed. The asymptotic properties of the estimator are studied given mild regularity conditions, which require only that the error term has a conditional moment of some order. We establish the consistency,...

While there is an extensive literature on identifying the asymptotic properties of adaptive learning algorithms, little is explicitly mentioned on how to actually implement these algorithms on the computer to analyze the quantitative effects of learning in dynamic macroeconomic models. The aim...