Indirect Inference for Locally Stationary Models

We propose the use of indirect inference estimation to conduct inference in complex locally stationary models. We develop a local indirect inference algorithm and establish the asymptotic properties of the proposed estimator. Due to the nonparametric nature of locally stationary models, the resulting indirect inference estimator exhibits nonparametric rates of convergence. We validate our methodology with simulation studies in the confines of a locally stationary moving average model and a new locally stationary multiplicative stochastic volatility model. Using this indirect inference methodology and the new locally stationary volatility model, we obtain evidence of non-linear, time-varying volatility trends for monthly returns on several Fama-French portfolios

Semiparametric Estimation of Locally Stationary Diffusion Models

This paper proposes a class of locally stationary diffusion processes. The modelhas a time varying but locally linear drift and a volatility coefficient that is allowed tovary over time and space. We propose estimators of all the unknown quantitiesbased on long span data. Our estimation method makes use of the localstationarity. We establish asymptotic theory for the proposed estimators as thetime span increases. We apply this method to the real financial data to illustrate thevalidity of our model. Finally, we present a simulation study to provide the finitesampleperformance of the proposed estimators.diffusion processes, local stationarity, term structure dynamics, density matching, option pricing.

What Impulse Response Do Instrumental Variables Identify?

Macro shocks are often composites, yet overlooked in the impulse response analysis. When an instrumental variable (IV) is used to identify a composite shock, it violates the common IV exclusion restriction. We show that the Local Projection-IV estimand is represented as a weighted average of component-wise impulse responses but with possibly negative weights, which occur when the IV and shock components have opposite correlations. We further develop alternative (set-) identification strategies for the LP-IV based on sign restrictions or additional granular information. Our applications confirm the composite nature of monetary policy shocks and reveal a non-defense spending multiplier exceeding one

Semiparametric estimation of locally stationary diffusion models

This paper proposes a class of locally stationary diffusion processes. The model has a time varying but locally linear drift and a volatility coefficient that is allowed to vary over time and space. We propose estimators of all the unknown quantities based on long span data. Our estimation method makes use of the local stationarity. We establish asymptotic theory for the proposed estimators as the time span increases. We apply this method to the real financial data to illustrate the validity of our model. Finally, we present a simulation study to provide the finitesample performance of the proposed estimators

Structural-break models under mis-specification: Implications for forecasting

This paper revisits the least squares estimator of the linear regression with a structural break. We view the model as an approximation to the true data generating process whose exact nature is unknown but perhaps changing over time either continuously or with some jumps. This view is widely held in the forecasting literature and under this view, the time series dependence property of all the observed variables is unstable as well. We establish that the rate of convergence of the estimator to a properly defined limit is at most the cube root of T , where T is the sample size, which is much slower than the standard super consistent rate. We also provide an asymptotic distribution of the estimator and that of the Gaussian quasi likelihood ratio statistic for a certain class of true data generating processes. We relate our finding to current forecast combination methods and propose a new averaging scheme. Our method compares favourably with various contemporary forecasting methods in forecasting a number of macroeconomic series.OAIID:oai:osos.snu.ac.kr:snu2015-01/102/0000009966/2ADJUST_YN:YEMP_ID:A079761DEPT_CD:212CITE_RATE:1.6DEPT_NM:경제학부SCOPUS_YN:YCONFIRM:

Estimation of a nonparametric model for bond prices from cross-section and time series information

We develop a novel estimation methodology for an additive nonparametric panel model that is suitable for capturing the pricing of coupon-paying government bonds followed over many time periods. We use our model to estimate the discount function and yield curve of nominally riskless government bonds. The novelty of our approach is the combination of two different techniques: cross-sectional nonparametric methods and kernel estimation for time varying dynamics in the time series context. The resulting estimator is used for predicting individual bond prices given the full schedule of their future payments. In addition, it is able to capture the yield curve shapes and dynamics commonly observed in the fixed income markets. We establish the consistency, the rate of convergence, and the asymptotic normality of the proposed estimator. A Monte Carlo exercise illustrates the good performance of the method under different scenarios. We apply our methodology to the daily CRSP bond market dataset, and compare ours with the popular Diebold and Li (2006) method.</p