2nd SERG Workshop: Program. September 23, 2015

Transcription

1 2nd SERG Workshop: Program September 23,

2 1 Workshop Summary Breakfast, Coffee Break/Morning Tea, and Coffee Break/Afternoon tea will be held in the Common Room (room 328) of the Merewether building. All sessions will be held in room 498 of the Merewether Building. Table 1: Schedule for 30th of September Time Event 08:30-09:00 Breakfast 09:00-09:15 Welcome Remarks 09:15-10:30 Keynote Speaker: Mervyn Silvapulle, "Goodness-of-fit Tests for GARCH Models" 10:30-11:00 Coffee Break/Morning Tea 11:00-12:30 Session 1 12:30-14:00 Lunch 14:00-15:30 Session 2 15:30-16:00 Coffee Break/Afternoon tea 16:00-17:30 Session 3 18:00-21:30 Dinner at Al Aseel Table 2: Schedule for 1st of October Time Event 08:30-09:00 Breakfast 09:00-10:30 Session 4 10:30-11:00 Coffee Break/Morning Tea 11:00-12:30 Session 5 12:30-14:00 Lunch 14:00-15:30 Session 6 15:30-15:45 Farewell Remarks 2

3 2 Detailed List of Sessions Session 1: Wednesday, September 30, 2015, 11:00 to 12:30. Change Detection in Granger Causality By Stan Hurn (Queensland University of Technology), Peter Phillips (Yale University, University of Auckland, University of Southampton & Singapore Management University), and Shuping Shi (Macquarie University). Discussant: Valentyn Panchenko, University of New South Wales. Estimation of a Scale-Free Network Formation Model By Anton Kolotilin (University of New South Wales), and Valentyn Panchenko (University of New South Wales). Discussant: Shuping Shi, Macquarie University. Session 2: Wednesday, September 30, 2015, 14:00 to 15:30. A New Measure of Vector Dependence, with an Application to Financial Contagion By Ivan Medovikov (Brock University), and Artem Prokhorov (University of Sydney). Discussant: Youngki Shin, University of Technology Sydney. Structural Change in Sparsity By Sokbae Lee (Seoul National University), Yuan Liao (University of Maryland), Myung Hwan Seo (London School of Economics), and Youngki Shin (University of Technology of Sydney). Discussant: Artem Prokhorov, University of Sydney. 3

4 Session 3: Wednesday, September 30, 2015, 16:00 to 17:30. An Improved Bootstrap Test for Restricted Stochastic Dominance By Thomas Lok ((University of Sydney), and Rami Tabri (University of Sydney). Discussant: Jay Lee, University of New South Wales. Asymptotic Refinements of a Misspecification-Robust Bootstrap for GEL Estimators By Jay Lee (University of New South Wales). Discussant: Rami Tabri, University of Sydney. Session 4: Thursday, October 1, 2015, 09:00 to 10:30. Diagnostic Checks for Multivariate Parametric Intensity By Simon Kwok (University of Sydney). Discussant: Susumu Imai, University of Technology Sydney and Queen s University. Identification and Estimation of Differentiated Products Models using Market Size and Cost Data By David Byrne (University of Melbourne), Susumu Imai (University of Technology Sydney and Queen s University), Neelam Jain (City University London), Vasilis Sarafidis (Monash University), and Masayuki Hirukawa (Setsunan University) Discussant: Simon Kwok, University of Sydney. 4

5 Session 5: Thursday, October 1, 2015, 11:00 to 12:30. Efficient estimation of Approximate factor Models of Large Dimensions By Rachida Ouysse (University of New South Wales). Discussant: Andrey Vasnev, University of Sydney. Generalized Variance: A Robust Estimator of Stock Price Volatility By M. Sutton (University of Sydney), Andrey Vasnev (University of Sydney), and Richard Gerlach (University of Sydney) Discussant: Rachida Ouysse (University of New South Wales). Session 6: Thursday, October 1, 2015, 14:00 to 15:30. Distribution Forecasting in Nonlinear Models with Stochastic Volatility By Peter Exterkate (University of Sydney). Discussant: Minh-Ngoc Tran, University of Sydney. Exact ABC using Importance Sampling By Minh-Ngoc Tran (University of Sydney), and Robert Kohn (University of New South Wales) Discussant: Peter Exterkate, University of Sydney. 5

6 3 Abstracts 3.1 Keynote Session Goodness-of-fit Tests for GARCH Models By Mervyn Silvapulle (Monash University) and Indeewara Perera. Abstract: Regression models with heteroscedastic errors, including the generalized autoregressive conditional heteroscedasticity [GARCH] models, are widely used in several areas particularly in finance and economics. In this paper, we consider the scenario where a parametric form is assumed for the entire model and the statistical inference requires the estimated conditional distribution of the dependent variable, not just the parameters of the variance function. For example, the objective may be to use the estimated model for density-forecasting and/or estimating risk measures such as a quantile of the dependent variable. As a consequence, answers to substantive questions critically depend on the assumed parametric form of the model. This paper develops a Kolomogorov- Smirnov type method for testing the goodness-of-fit of the model against the alternative that does not specify any parametric form for the GARCH. The limiting distribution of the test statistic turns out to be complicated and it depends on unknown nuisance parameters. Therefore, a bootstrap procedure is proposed to implement the tests and is shown to be asymptotically valid. The tests are shown to have non-trivial asymptotic local power. A challenge encountered in developing a bootstrap method is that the computer cannot generate data from the stationary population model at the estimated parameter value. The main contribution of this paper is to show that the proposed bootstrap method is valid. The proposed tests performed well in a simulation study. The tests are also illustrated using a data example. 3.2 Session 1 Change Detection in Granger Causality By Stan Hurn (Queensland University of Technology), Peter Phillips (Yale University, University of Auckland, University of Southampton & Singapore Management University), and Shuping Shi 6

7 (Macquarie University). Abstract: Causal relationships in econometrics are typically based on the concept of predictability and are established in terms of tests for Granger causality. These causal relationships are susceptible to change, especially during times of financial turbulence, making the real-time detection of instability an important practical issue. This paper develops a test for detecting changes in causal relationships based on a recursive rolling window, which is analogous to the procedure used in recent work on financial bubble detection. The limiting distribution of the test takes a simple form under the null hypothesis and is easy to implement in conditions of homoskedasticity, conditional heteroskedasticity and unconditional heteroskedasticity. Simulation experiments compare the efficacy of the proposed test with two other commonly used tests, the forward recursive and the rolling window tests. The results indicate that both the rolling and the recursive rolling approaches offer good finite sample performance in situations where there are one or two changes in the causal relationship over the sample period. The testing strategies are illustrated in an empirical application that explores the causal impact of the slope of the yield curve on output and inflation in the U.S. over the period Estimation of a Scale-Free Network Formation Model By Anton Kolotilin (University of New South Wales), and Valentyn Panchenko (University of New South Wales). Abstract: Growing evidence suggests that many social and economic networks are scale free in that their degree distribution P (d) is approximately proportional to d γ. The most widespread explanation for this phenomenon is a random network formation process with preferential attachment. For a general version of such a process, we develop a class of GMM estimators. We show formally that these GMM estimators give consis- tent estimates of model parameters. Simulations suggest that the GMM estimates are asymptotically normally distributed. The commonly used NLLS estimator gives highly biased and inconsistent estimates; Hill (1975) estimator performs even worse. 7

8 3.3 Session 2 A New Measure of Vector Dependence, with an Application to Financial Contagion By Ivan Medovikov (Brock University), and Artem Prokhorov (University of Sydney). Abstract: We propose a new nonparametric measure of association between an arbitrary number of random vectors. The measure is based on the empirical copula process for the multivariate marginals, corresponding to the vectors, and is insensitive to the dependence between the withinvector components. It monotonically changes in the [0, 1] interval, covering the entire range of dependence between exact vector independence and a version of vector co-monotonicity. We study the properties of the new measure under several well-known copulas and provide a non-parametric estimator of the measure, along with its asymptotic theory, under fairly general assumptions. To illustrate the applicability of the new measure, we use it to assess the degree of interdependence between the financial markets in the Americas, Europe and Asia, surrounding the financial crisis of We find strong evidence of previously unknown contagion patterns, with selected regions exhibiting little dependence before and after the crisis and a lot of dependence during the crisis period. Structural Change in Sparsity By Sokbae Lee (Seoul National University), Yuan Liao (University of Maryland), Myung Hwan Seo (London School of Economics), and Youngki Shin (University of Technology of Sydney). Abstract: In the high-dimensional sparse modeling literature, it has been crucially assumed that the sparsity structure of the model is homogeneous over the entire population. That is, the identities of important regressors are invariant across the population and across the individuals in the collected sample. In practice, however, the sparsity structure may not always be invariant in the population, due to heterogeneity across different sub-populations. We consider a general, possibly non-smooth M-estimation framework, allowing a possible structural change regarding the identities of important regressors in the population. Our penalized M-estimator not only selects covariates but also discriminates between a model with homogeneous sparsity and a model with a 8

9 structural change in sparsity. As a result, it is not necessary to know or pretest whether the structural change is present, or where it occurs. We derive asymptotic bounds on the estimation loss of the penalized M-estimators, and achieve the oracle properties. We also show that when there is a structural change, the estimator of the threshold parameter is super-consistent. If the signal is relatively strong, the rates of convergence can be further improved and asymptotic distributional properties of the estimators including the threshold estimator can be established using an adaptive penalization. The proposed methods are then applied to quantile regression and logistic regression models and are illustrated via Monte Carlo experiments. 3.4 Session 3 An Improved Bootstrap Test for Restricted Stochastic Dominance By Thomas Lok ((University of Sydney), and Rami Tabri (University of Sydney). Abstract:This paper proposes a uniformly asymptotically valid method of testing for restricted stochastic dominance based on the bootstrap test of Linton et al. (2010). The method reformulates their bootstrap test statistics using a constrained estimator of the contact set that imposes the restrictions of the null hypothesis. As our simulation results show, this characteristic of our test makes it noticeably less conservative than the test of Linton et al. (2010) and improves its power against alternatives that have some non-violated inequalities. Asymptotic Refinements of a Misspecification-Robust Bootstrap for GEL Estimators By Jay Lee (University of New South Wales). Abstract: I propose a nonparametric iid bootstrap procedure for the empirical likelihood, the exponential tilting, and the exponentially tilted empirical likelihood estimators that achieves asymptotic refinements for t tests and confidence intervals, and Wald tests and confidence regions based on such estimators. Furthermore, the proposed bootstrap is robust to model misspecification, i.e., it achieves asymptotic refinements regardless of whether the assumed moment condition model is correctly specified or not. This result is new, because asymptotic refinements of the bootstrap 9

10 based on these estimators have not been established in the literature even under correct model specification. Monte Carlo experiments are conducted in dynamic panel data setting to support the theoretical finding. As an application, bootstrap confidence intervals for the returns to schooling of Hellerstein and Imbens (1999) are calculated. The result suggests that the returns to schooling may be higher. 3.5 Session 4 Diagnostic Checks for Multivariate Parametric Intensity By Simon Kwok (University of Sydney). Abstract: The properties of the popular portmanteau tests are well known for standard time series models with.xed and discrete time intervals such as vector ARMA and GARCH models and their variants. However, there are not many theoretical results nor any empirical performance analyses on the asymptotic distribution of these test statistics applied to an estimated multivariate parametric continuous time intensity model with parameter uncertainty. This paper aims at.lling this gap in the literature, by deriving a large-sample distribution theory for generalized residual autocorrelations and proposing statistical test procedures that check for model adequacy. The test procedures are theoretically justi.ed for a wide class of multivariate parametric recurrent-event intensity models. Identification and Estimation of Differentiated Products Models using Market Size and Cost Data By David Byrne (University of Melbourne), Susumu Imai (University of Technology Sydney and Queen s University), Neelam Jain (City University London), Vasilis Sarafidis (Monash University), and Masayuki Hirukawa (Setsunan University) Abstract: We propose a new methodology for estimating the demand and cost functions of differentiated products models when demand and cost data are available. The method deals with the endogeneity of prices to demand shocks and the endogeneity of outputs to cost shocks, by using variation in market size that does not need to be exogenous, and cost data. We establish nonpara- 10

11 metric identification, consistency and asymptotic normality of our estimator. Using Monte-Carlo experiments, we show our method works well in contexts where instruments are correlated with demand and cost shocks, and where commonly-used instrumental variables estimators are biased and numerically unstable. 3.6 Session 5 Efficient estimation of Approximate factor Models of Large Dimensions By Rachida Ouysse (University of New South Wales). Abstract: Efficient estimation of factor models is attracting considerable attention be- cause recent empirical evidence suggests the estimates are adversely affected by the inability to account for the cross sectional dynamics. Let X t be a N- dimensional vector of stationary variables observed at time t = 1..., T such that X t = ΛF t + ɛ t, where the common factors F t and their loadings Λ are un- observed. A factor structure is approximate when the idiosyncratic errors ɛ t are weakly correlated across the variables. Principal components analysis (PCA) provides consistent estimation of the factor structure and efficiency can be achieved using robust econometric tools such as generalized PCA and quasi maximum likelihood. However when N > T, the sample covariance matrix is singular and accounting for cross-sectional dynamics is challenging without imposing a structure on these dynamics. Instead we use the approximate structure assumption of bounded 1 N N N i=1 j=1 E(ɛ itɛ jt ), as a constraint in the PCA framework. Our penalized PCA can be interpreted as a shrinkage regression where the off diagonal elements of the covariance matrix are shrunk towards zero as N grows large. We show that our estimators are consistent and more efficient that PCA. Furthermore, simulation experiments show that our approach compares well with other alternatives that make use of a known covariance structure. Generalized Variance: A Robust Estimator of Stock Price Volatility By M. Sutton (University of Sydney), Andrey Vasnev (University of Sydney), and Richard Gerlach (University of Sydney) 11

12 Abstract: This paper proposes an ex-post volatility estimator, called generalized variance, that uses high frequency data to provide measurements robust to the idiosyncratic noise of stock markets caused by market microstructures. The new volatility estimator is analyzed theoretically, examined in a simulation study and evaluated empirically against the two currently dominant measures of daily volatility: realized volatility and realized range. The main finding is that generalized variance is robust to the presence of microstructures while delivering accuracy superior to realized volatility and realized range in several circumstances. The empirical study features Australian stocks from the ASX Session 6 Distribution Forecasting in Nonlinear Models with Stochastic Volatility By Peter Exterkate (University of Sydney). Abstract: Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This makes it a powerful forecasting tool, which is applicable in many different contexts. However, it is usually applied only to independent and identically distributed observations. This paper introduces a variant of kernel ridge regression for time series with stochastic volatility. The conditional mean and volatility are both modelled as nonlinear functions of observed variables. We set up the estimation problem in a Bayesian manner and derive a Gibbs sampler to obtain draws from the predictive distribution. A simulation study and an application to forecasting the distribution of returns on the S&P500 index are presented, and we find that our method outperforms most popular GARCH variants in terms of one-day-ahead predictive ability. Notably, most of this improvement comes from a more adequate approximation to the tails of the distribution. Exact ABC using Importance Sampling By Minh-Ngoc Tran (University of Sydney), and Robert Kohn (University of New South Wales) Abstract: Approximate Bayesian Computation (ABC) is a powerful method for carrying out 12

13 Bayesian inference when the likelihood is computationally intractable. However, a draw- back of ABC is that it is an approximate method that induces a systematic error because it is necessary to set a tolerance level to make the computation tractable. The issue of how to optimally set this tolerance level has been the subject of extensive research. This paper proposes an ABC algorithm based on importance sampling that estimates expectations with respect to the exact posterior distribution given the observed summary statistics. This overcomes the need to select the tolerance level. By exact we mean that there is no systematic error and the Monte Carlo error can be made arbitrarily small by increasing the number of importance samples. We provide a formal justification for the method and study its convergence properties. The method is illustrated in two applications and the empirical results suggest that the proposed ABC based estimators consistently converge to the true values as the number of importance samples increases. Our proposed approach can be applied more generally to any importance sampling problem where an unbiased estimate of the likelihood is required. 13