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Full-Text Articles in Social and Behavioral Sciences
Testing For Structural Changes In Factor Models Via A Nonparametric Regression, Liangjun Su, Xia Wang
Testing For Structural Changes In Factor Models Via A Nonparametric Regression, Liangjun Su, Xia Wang
Research Collection School Of Economics
We propose a model-free test for structural changes in factor models. The basic idea is to regress the data on commonly estimated factors by local smoothing and compare the fitted values of time-varying factor loadings with those of time-invariant factor loadings estimated via principal component analysis. By construction, the test is designed to be powerful against both smooth structural changes and sudden structural breaks with a possibly unknown number of breaks and unknown break dates in the factor loadings. No restrictions on the form of alternatives or trimming of boundary regions near the beginning or end of the sample period …
Estimation Of Large Dimensional Factor Models With An Unknown Number Of Breaks, Shujie Ma, Liangjun Su
Estimation Of Large Dimensional Factor Models With An Unknown Number Of Breaks, Shujie Ma, Liangjun Su
Research Collection School Of Economics
In this paper we study the estimation of a large dimensional factor model when the factor loadingsexhibit an unknown number of changes over time. We propose a novel three-step procedure to detect the breaks if any and then identify their locations. In the first step, we divide the whole time span into subintervals and fit a conventional factor model on each interval. In the second step, we apply the adaptive fused group Lasso to identify intervals containing a break. In the third step, we devise a grid search method to estimate the location of the break on each identified interval. …
On Time-Varying Factor Models: Estimation And Testing, Liangjun Su, Xia Wang
On Time-Varying Factor Models: Estimation And Testing, Liangjun Su, Xia Wang
Research Collection School Of Economics
Conventional factor models assume that factor loadings are fixed over a long horizon of time, which appears overly restrictive and unrealistic in applications. In this paper, we introduce a time-varying factor model where factor loadings are allowed to change smoothly over time. We propose a local version of the principal component method to estimate the latent factors and time-varying factor loadings simultaneously. We establish the limiting distributions of the estimated factors and factor loadings in the standard large N and large T framework. We also propose a BIC-type information criterion to determine the number of factors, which can be used …
Shrinkage Estimation Of Common Breaks In Panel Data Models Via Adaptive Group Fused Lasso, Junhui Qian, Liangjun Su
Shrinkage Estimation Of Common Breaks In Panel Data Models Via Adaptive Group Fused Lasso, Junhui Qian, Liangjun Su
Research Collection School Of Economics
In this paper we consider estimation and inference of common breaks in panel data models via adaptive group fused Lasso. We consider two approaches—penalized least squares (PLS) for first-differenced models without endogenous regressors, and penalized GMM (PGMM) for first-differenced models with endogeneity. We show that with probability tending to one, both methods can correctly determine the unknown number of breaks and estimate the common break dates consistently. We establish the asymptotic distributions of the Lasso estimators of the regression coefficients and their post Lasso versions. We also propose and validate a data-driven method to determine the tuning parameter used in …
Estimation Of Large Dimensional Factor Models With An Unknown Number Of Breaks, Shujie Ma, Liangjun Su
Estimation Of Large Dimensional Factor Models With An Unknown Number Of Breaks, Shujie Ma, Liangjun Su
Research Collection School Of Economics
In this paper we study the estimation of a large dimensional factor model when the factor loadings exhibit an unknown number of changes over time. We propose a novel three-step procedure to detect the breaks if any and then identify their locations. In the first step, we divide the whole time span into subintervals and fit a conventional factor model on each interval. In the second step, we apply the adaptive fused group Lasso to identify intervals containing a break. In the third step, we devise a grid search method to estimate the location of the break on each identified …
On Time-Varying Factor Models: Estimation And Testing, Liangjun Su, Xia Wang
On Time-Varying Factor Models: Estimation And Testing, Liangjun Su, Xia Wang
Research Collection School Of Economics
Conventional factor models assume that factor loadings are fixed over a long horizon of time, which appears overly restrictive and unrealistic in applications. In this paper, we introduce a time-varying factor model where factor loadings are allowed to change smoothly over time. We propose a local version of the principal component method to estimate the latent factors and time-varying factor loadings simultaneously. We establish the limiting distributions of the estimated factors and factor loadings in the standard large N and large T framework. We also propose a BIC-type information criterion to determine the number of factors, which can be used …
On Bias In The Estimation Of Structural Break Points, Liang Jiang, Xiaohu Wang, Jun Yu
On Bias In The Estimation Of Structural Break Points, Liang Jiang, Xiaohu Wang, Jun Yu
Research Collection School Of Economics
Based on the Girsanov theorem, this paper obtains the exact Önite sample distribution of the maximum likelihood estimator of structural break points in a continuous time model. The exact Önite sample theory suggests that, in empirically realistic situations, there is a strong Önite sample bias in the estimator of structural break points. This property is shared by least squares estimator of both the absolute structural break point and the fractional structural break point in discrete time models. A simulation-based method based on the indirect estimation approach is proposed to reduce the bias both in continuous time and discrete time models. …
Structural Change Estimation In Time Series Regressions With Endogenous Variables, Junhui Qian, Liangjun Su
Structural Change Estimation In Time Series Regressions With Endogenous Variables, Junhui Qian, Liangjun Su
Research Collection School Of Economics
We propose to apply the group fused Lasso to estimate time series models with endogenous regressors and an unknown number of breaks. It can correctly determine the number of breaks and estimate the break dates asymptotically. Simulations and applications are given.
Shrinkage Estimation Of Regression Models With Multiple Structural Changes, Junhui Qian, Liangjun Su
Shrinkage Estimation Of Regression Models With Multiple Structural Changes, Junhui Qian, Liangjun Su
Research Collection School Of Economics
In this paper we consider the problem of determining the number of structural changes in multiple linear regression models via group fused Lasso (least absolute shrinkage and selection operator). We show that with probability tending to one our method can correctly determine the unknown number of breaks and the estimated break dates are sufficiently close to the true break dates. We obtain estimates of the regression coefficients via post Lasso and establish the asymptotic distributions of the estimates of both break ratios and regression coefficients. We also propose and validate a data-driven method to determine the tuning parameter. Monte Carlo …
Pricing For Goodwill: A Threshold Quantile Regression Approach, Heng Ju, Liangjun Su, Pai Xu
Pricing For Goodwill: A Threshold Quantile Regression Approach, Heng Ju, Liangjun Su, Pai Xu
Research Collection School Of Economics
In the absence of other effective trust systems, an agent's reputation status becomes a critical factor in online transactions. A higher reputation category may give sellers an advantage in competition on online trading platforms. It is also possible that such reputation benefits provide sufficient incentives for sellers to adjust their pricing behavior. We here propose a simple economic model in which an online seller maximizes the sum of the profit from current sales and the possible future gain from a targeted higher reputation level. We show that the model can predict a jump in optimal pricing behavior. We adopt a …
Testing Structural Change In Conditional Distributions Via Quantile Regressions, Liangjun Su, Zhijie Xiao
Testing Structural Change In Conditional Distributions Via Quantile Regressions, Liangjun Su, Zhijie Xiao
Research Collection School Of Economics
We propose tests for structural change in conditional distributions via quantile regressions. To avoid misspecification on the conditioning relationship, we construct the tests based on the residuals from local polynomial quantile regressions. In particular, the tests are based upon the cumulative sums of generalized residuals from quantile regressions and have power against local alternatives at rate n−1/2. We derive the limiting distributions for our tests under the null hypothesis of no structural change and a sequence of local alternatives. The proposed tests apply to a wide range of dynamic models, including time series regressions with m.d.s. errors, as well as …
Testing Structural Change In Time-Series Nonparametric Regression Models, Liangjun Su, Zhijie Xiao
Testing Structural Change In Time-Series Nonparametric Regression Models, Liangjun Su, Zhijie Xiao
Research Collection School Of Economics
We propose a CUSUM type of test for structural change in dynamic nonparametric regression models. It is based upon the cumulative sums of weighted residuals from a single nonparametric regression and complements the conventional parameter instability tests in parametric models. We derive the limiting distributions of the test under both the null hypothesis and sequences of local alternatives. A boot-strap procedure is also proposed and its validity is justified. Finally, simulation experiments are conducted to investigate the finite sample properties of our test.
Green Revolutions And Miracle Economies: Agricultural Innovation, Trade And Growth, Brishti Guha
Green Revolutions And Miracle Economies: Agricultural Innovation, Trade And Growth, Brishti Guha
Research Collection School Of Economics
The purpose of this paper is to develop a simple model of an economy in which growth is driven by a combination of exogenous technical change in agriculture and a rising world demand for labor-intensive manufactured exports. We explore the relative roles of an exogenous agricultural productivity shock and rising export demand in a model with two traded industrial goods and a non-traded agricultural good, food. When the non-traded sector uses a specific factor, we show that technical change in agriculture may be the key to factor migration into industry, in particular driving intersectoral labor migration. A key assumption is …
Green Revolutions And Miracle Economies: Agricultural Innovation, Trade And Growth, Brishti Guha
Green Revolutions And Miracle Economies: Agricultural Innovation, Trade And Growth, Brishti Guha
Research Collection School Of Economics
The purpose of this paper is to develop a simple model of an economy in which growth is driven by a combination of exogenous technical change in agriculture as well as by a rising world demand for labor-intensive manufactured exports. We explore the relative roles of agricultural innovation and rising export demand in a model with two traded industrial goods and a non-traded agricultural good, food. When the non-traded sector uses a specific factor, we show that technical change in agriculture may be the key to sustained factor accumulation in industry, in particular driving intersectoral labor migration. A key assumption …
Structural Change And Lead-Lag Relationship Between The Nikkei Spot Index And Futures Price: A Genetic Programming Approach, Donald Lien, Yiu Kuen Tse, X. B. Chang
Structural Change And Lead-Lag Relationship Between The Nikkei Spot Index And Futures Price: A Genetic Programming Approach, Donald Lien, Yiu Kuen Tse, X. B. Chang
Research Collection School of Economics
In this paper we adopt a nonparametric genetic programming approach to identify the structural changes in the Nikkei spot index and futures price. Due to the dominance of the “normal” period in sample size, the lead-lag relationship identified in the spot-futures system based on conventional methods such as test for Granger causality pertains to the normal period and may not be applicable in the “extreme” period. Using genetic programming we identify the lead-lag relationship based on the chronological ordering of the structural changes in the spot and futures markets. Our results show that in recent periods, major market changes originated …