This paper extends the existing literature on linear quadratic adjustment cost (LQAC) models under rational expectations to the inferential issues arising when: (i) agents optimise with respect to a vector of endogenous variables; (ii) the behavioural equations stemming from the agent's...

It is well-known that outliers exist in the type of multivariate data used by financial practitioners for portfolio construction and risk management. Typically, outliers are addressed prior to model fitting by applying some combination of trimming and/or Winsorization to each individual...

This chapter presents a unified set of estimation methods for fitting a rich array of models describing dynamic relationships within a longitudinal data setting. The discussion surveys approaches for characterizing the micro dynamics of continuous dependent variables both over time and across...

This paper develops optimal estimation of a potentially non-differentiable functional Γβ of a regular parameter β, when Γ satisfies certain conditions. Primary examples are min or max functionals that frequently appear in the analysis of partially identified models. This paper investigates...

We propose here a novel method of factor profiling (FP) for ultra high dimensional variable selection. The new method assumes that the correlation structure of the high dimensional data can be well represented by a set of low-dimensional latent factors (Fan et al., 2008). The latent factors can...

We propose a novel varying coefficient model, called principal varying coefficient model (PVCM), by characterizing the varying coefficients through linear combinations of a few principal functions. Compared with the conventional varying coefficient model (VCM; Chen and Tsay, 1993; Hastie and...

This paper presents a short survey on limit theorems for certain functionals of semimartingales, which are observed at high frequency. Our aim is to explain the main ideas of the theory to a broader audience. We introduce the concept of stable convergence, which is crucial for our purpose. We...

Three models are presented: AR (autoregressive), MA (moving average) and ARMA (autoregressive moving average) are common models used in time series forecasting. These three models are the various definition of each element of the General Linear Model: Y = a + b + c. For the study of linear...

In order to discuss nonlinear, it is necessary to know linear regressive as a priori. Without simple regression as the starting point, it would be difficult to understand nonlinear regression. In words, in order to understand the curve and the behavior of a curve, it is necessary to known a...

We employ a wavelet approach and conduct a time-frequency analysis of dynamic correlations between pairs of key traded assets (gold, oil, and stocks) covering the period from 1987 to 2012. The analysis is performed on both intra-day and daily data. We show that heterogeneity in correlations...