Joint Econometrics and Statistics Seminar Series Lent term 2013

Han Ai (Chinese Academy of Sciences)

Title: Autoregressive Conditional Models for Interval-Valued Time Series Data

Abstract: An interval-valued observation in a time period contains more information than a point-valued observation in the same time period. Examples of interval data include the maximum and minimum temperatures in a day, the maximum and minimum GDP growth rates in a year, the maximum and minimum asset prices in a trading day, the bid and ask prices in a trading period, the long term and short term interests, and the 90%-tile and 10%-tile incomes of a cohort in a year, etc. Interval forecasts may be of direct interest in practice, as it contains information on the range of variation and the level or trend of economic processes. Moreover, the informational advantage of interval data can be exploited for more efficient econometric estimation and inference. We propose a new class of autoregressive conditional interval (ACI) models for interval-valued time series data. A minimum distance estimation method is proposed to estimate the parameters of an ACI model, and the consistency, asymptotic normality and asymptotic efficiency of the proposed estimator are established. It is shown that a two-stage minimum distance estimator is asymptotically most efficient among a class of minimum distance estimators, and it achieves the Cramer-Rao lower bound when the left and right bounds of the interval innovation process follow a bivariate normal distribution. Simulation studies show that the two-stage minimum distance estimator outperforms conditional least squares estimators based on the ranges and/or midpoints of the interval sample, as well as the conditional quasi- maximumlikelihood estimator based on the bivariate left and right bound information of the interval sample. In an empirical study on asset pricing, we document that when return interval data is used, some bond market factors, particularly the default risk factor, are significant in explaining excess stock returns, even after the stock market factors are controlled in regressions.
This differs from the previous findings (e.g., Fama and French (1993)) in the literature.

Raffaella Giacomini (University College London)

Title: Forecasting with judgment

Abstract: The paper seeks to answer the following questions: how to define judgement? How to incorporate it into existing model-based forecasts in a rigorous way? How to know whether and when incorporating judgement gives more accurate forecasts? We broadly define judgement as a set of moment conditions involving a subset of the variables in a benchmark model, but specialize the discussion to two empirically relevant types of judgement: 1) mean and/or variance forecasts based on survey data; 2) (nonlinear) restrictions based on economic theory - such as Euler equations or Taylor rules - imposed on forecasts from atheoretical models. We propose incorporating judgement into existing forecasts from a benchmark model using exponential tilting. We provide theoretical results that help establish whether and when incorporating judgement improves forecast accuracy, and illustrate the usefulness of the method for anchoring yield curve forecasts.

Christian Brownlees (Unversitat Pompeu Fabra, Barcelona)

NETS: Network Estimation for Time Series

Howell Tong (LSE)

Title: On conditionally heteroscedastic AR models with thresholds.

Christian Francq (Université Lille 3)

Risk-parameter estimation in volatility models

Petyo Bonev (University of Mannheim)

Title: Nonparametric Duration IV Methods.

Abstract: Dynamic selection and endogeneous noncompliance hamper the evaluation of treatmenteffects when the outcome of interest is a duration variable. Existing methods either restrict their analysis to settings where only one of those two problems exists, or adopt parametric or semi-parametric structure. In this paper we develop two completely nonparametric Instrumental Variable approaches for duration data which enable us to identify treatment effects in the presence of both dynamic selection and endogeneous noncompliance. We suggest corresponding estimators. Our approaches are revealed to have as special cases numerous existing models. We suggest simple procedures to test for endogeneity. We apply our estimator to a French policy reform to estimate the effect of a change in the unemployment insurance system on the duration of unemployment.