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Articles 661 - 690 of 828

Full-Text Articles in Econometrics

A Simple Approach To The Parametric Estimation Of Potentially Nonstationary Diffusions, Federico Bandi, Peter C. B. Phillips Apr 2007

A Simple Approach To The Parametric Estimation Of Potentially Nonstationary Diffusions, Federico Bandi, Peter C. B. Phillips

Research Collection School Of Economics

A simple and robust approach is proposed for the parametric estimation of scalar homogeneous stochastic differential equations. We specify a parametric class of diffusions and estimate the parameters of interest by minimizing criteria based on the integrated squared difference between kernel estimates of the drift and diffusion functions and their parametric counterparts. The procedure does not require simulations or approximations to the true transition density and has the simplicity of standard nonlinear least-squares methods in discrete time. A complete asymptotic theory for the parametric estimates is developed. The limit theory relies on infill and long span asymptotics and is robust …

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Simulation-Based Estimation Of Contingent-Claims Prices, Jun Yu Apr 2007

Simulation-Based Estimation Of Contingent-Claims Prices, Jun Yu

Research Collection School Of Economics

A new methodology is proposed to estimate theoretical prices of financial contingent-claims whose values are dependent on some other underlying financial assets. In the literature the preferred choice of estimator is usually maximum likelihood (ML). ML has strong asymptotic justification but is not necessarily the best method in finite samples. The present paper proposes instead a simulation-based method that improves the finite sample performance of the ML estimator while maintaining its good asymptotic properties. The methods are implemented and evaluated here in the Black-Scholes option pricing model and in the Vasicek bond pricing model, but have wider applicability. Monte Carlo …

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Long Run Variance Estimation And Robust Regression Testing Using Sharp Origin Kernels With No Truncation, Peter C. B. Phillips, Yixiao Sun, Sainan Jin Mar 2007

Long Run Variance Estimation And Robust Regression Testing Using Sharp Origin Kernels With No Truncation, Peter C. B. Phillips, Yixiao Sun, Sainan Jin

Research Collection School Of Economics

A new family of kernels is suggested for use in long run variance (LRV) estimation and robust regression testing. The kernels are constructed by taking powers of the Bartlett kernel and are intended to be used with no truncation (or bandwidth) parameter. As the power parameter ([rho]) increases, the kernels become very sharp at the origin and increasingly downweight values away from the origin, thereby achieving effects similar to a bandwidth parameter. Sharp origin kernels can be used in regression testing in much the same way as conventional kernels with no truncation, as suggested in the work of Kiefer and …

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Bias In Dynamic Panel Estimation With Fixed Effects, Incidental Trends And Cross Section Dependence, Peter C. B. Phillips, Donggyu Sul Mar 2007

Bias In Dynamic Panel Estimation With Fixed Effects, Incidental Trends And Cross Section Dependence, Peter C. B. Phillips, Donggyu Sul

Research Collection School Of Economics

Explicit asymptotic bias formulae are given for dynamic panel regression estimators as the cross section sample size N --> ∞. The results extend earlier work by Nickell [1981. Biases in dynamic models with fixed effects. Econometrica 49, 1417-1426] and later authors in several directions that are relevant for practical work, including models with unit roots, deterministic trends, predetermined and exogenous regressors, and errors that may be cross sectionally dependent. The asymptotic bias is found to be so large when incidental linear trends are fitted and the time series sample size is small that it changes the sign of the autoregressive …

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Monotonicity Conditions And Inequality Imputation For Sample-Selection And Non-Response Problems, Myoung-Jae Lee Feb 2007

Monotonicity Conditions And Inequality Imputation For Sample-Selection And Non-Response Problems, Myoung-Jae Lee

Research Collection School Of Economics

Under a sample selection or non-response problem, where a response variable y is observed only when a condition δ = 1 is met, the identified mean E(y|δ = 1) is not equal to the desired mean E(y). But the monotonicity condition E(y|δ = 1) ≤ E(y|δ = 0) yields an informative bound E(y|δ = 1) ≤ E(y), which is enough for certain inferences. For example, in a majority voting with δ being the vote-turnout, it is enough to know if E(y) > 0.5 or not, for which E(y|δ = 1) > 0.5 is sufficient under the monotonicity. The main question is then …

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Moving Window Unit Root Test: Locating Real Estate Price Bubbles In Seoul Apartment Market, Shuping Shi Jan 2007

Moving Window Unit Root Test: Locating Real Estate Price Bubbles In Seoul Apartment Market, Shuping Shi

Dissertations and Theses Collection (Open Access)

Bubbles are characterized by rapid expansion followed by a contraction. Evans (1991) shows that stationarity tests suggested by Hamilton and Whiteman (1985) and Diba and Grossman (1988) are incapable of detecting periodically collapsing bubbles. Phillips, Wu, and Yu (2006) advanced the forward recursive unit root test which improves the power significantly in the presence of periodically collapsing bubbles. In this paper, we consider rolling window unit root test with a pre-selected optimum window. A combining use of conventional unit root test and forward recursive unit root test is suggested from the results of power comparison. Furthermore, we apply those three …

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Regional Trade Agreements Revisited, Hui Chin Tan Jan 2007

Regional Trade Agreements Revisited, Hui Chin Tan

Dissertations and Theses Collection (Open Access)

The gravity model is a workhorse for econometric studies of the impact of regional trade agreements (RTAs). Despite its initial lack of theoretical basis, the model has been successfully derived from various trade theories. The latest theoretical derivation by Anderson and van Wincoop (2003) reveals that prior gravity studies have made the critical error of omitting the multilateral resistance variable, which results in biased estimates. Other recent studies have highlighted empirical issues with the commonly used procedure of log-linearizing the gravity model and estimating the parameters using Ordinary Least Squares (OLS) regression. Silva and Tenreyro (2006) point out that this …

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Open Vs Sealed Bid Auctions: Testing For Revenue Equivalence Under Singapore's Vehicle Quota System, Roberto S. Mariano, Winston T. H. Koh, Yiu Kuen Tse Jan 2007

Open Vs Sealed Bid Auctions: Testing For Revenue Equivalence Under Singapore's Vehicle Quota System, Roberto S. Mariano, Winston T. H. Koh, Yiu Kuen Tse

Research Collection School Of Economics

Using data from the auction of vehicle quota licenses in Singapore, we study if revenue equivalence holds when the auction format was switched from a sealed-bid format (May 1990 to June 2001) to an open bidding format since July 2001. Our econometric analysis indicates the change in auction format led to a change in bidding behavior. On average, the quota license premium under the open bidding format is about US$1000 (about 7.5% of the Category E license price in June 2001) lower, compared to the forecast level that would have prevailed if there had been no change in the auction …

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Indirect Inference For Dynamic Panel Models, Christian Gourieroux, Peter C. B. Phillips, Jun Yu Jan 2007

Indirect Inference For Dynamic Panel Models, Christian Gourieroux, Peter C. B. Phillips, Jun Yu

Research Collection School Of Economics

Maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size and large cross section sample size asymptotics. This paper proposes a general, computationally inexpensive method of bias reduction that is based on indirect inference, shows unbiasedness and analyzes efficiency. Monte Carlo studies show that our procedure achieves substantial bias reductions with only mild increases in variance, thereby substantially reducing root mean square errors. The method is compared with certain consistent estimators and is shown to have superior finite sample properties to the generalized method of …

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Limit Theory For Moderate Deviations From A Unit Root Under Weak Dependence, Peter C. B. Phillips, Tassos Magadalinos Jan 2007

Limit Theory For Moderate Deviations From A Unit Root Under Weak Dependence, Peter C. B. Phillips, Tassos Magadalinos

Research Collection School Of Economics

An asymptotic theory is given for autoregressive time series with weakly dependent innovations and a root of the form rho_{n} = 1+c/n^{alpha}, involving moderate deviations from unity when alpha in (0,1) and c in R are constant parameters. The limit theory combines a functional law to a diffusion on D[0,infinity) and a central limit theorem. For c > 0, the limit theory of the first order serial correlation coefficient is Cauchy and is invariant to both the distribution and the dependence structure of the innovations. To our knowledge, this is the first invariance principle of its kind for explosive processes. The …

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Temporal Aggregation And Risk-Return Relation, Jin Xing, Leping Wang, Jun Yu Jan 2007

Temporal Aggregation And Risk-Return Relation, Jin Xing, Leping Wang, Jun Yu

Research Collection School Of Economics

The function form of a linear intertemporal relation between risk and return is suggested by Merton's [1973. Econometrica 41, 867–887] analytical work for instantaneous returns, whereas empirical studies have examined the nature of this relation using temporally aggregated data, i.e., daily, monthly, quarterly, or even yearly returns. Our paper carefully examines the temporal aggregation effect on the validity of the linear specification of the risk–return relation at discrete horizons, and on its implications on the reliability of the resulting inference about the risk–return relation based on different observation intervals. Surprisingly, we show that, based on the standard Heston's [1993. Review …

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Temporal Aggregation And Risk-Return Relation, Xing Jin, Leping Wang, Jun Yu Jan 2007

Temporal Aggregation And Risk-Return Relation, Xing Jin, Leping Wang, Jun Yu

Research Collection School Of Economics

The function form of a linear intertemporal relation between risk and return is suggested by Merton's [1973. Econometrica 41, 867–887] analytical work for instantaneous returns, whereas empirical studies have examined the nature of this relation using temporally aggregated data, i.e., daily, monthly, quarterly, or even yearly returns. Our paper carefully examines the temporal aggregation effect on the validity of the linear specification of the risk–return relation at discrete horizons, and on its implications on the reliability of the resulting inference about the risk–return relation based on different observation intervals. Surprisingly, we show that, based on the standard Heston's [1993. Review …

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Modeling Transaction Data Of Trade Direction And Estimation Of Probability Of Informed Trading, Anthony S. Tay, Christopher Ting, Yiu Kuen Tse, Mitch Warachka Jan 2007

Modeling Transaction Data Of Trade Direction And Estimation Of Probability Of Informed Trading, Anthony S. Tay, Christopher Ting, Yiu Kuen Tse, Mitch Warachka

Research Collection School Of Economics

This paper implements the Asymmetric AutoregressiveConditional Duration (AACD) model of Bauwens and Giot (2003) to analyzeirregularly spaced transaction data of trade direction, namely buy versus sellorders. We examine the influence of lagged transaction duration, lagged volumeand lagged trade direction on transaction duration and direction. Our resultsare applied to estimate the probability of informed trading (PIN) based on theEasley, Hvidkjaer and O’Hara (2002) framework. Unlike the Easley-Hvidkjaer-O’Hara model, which uses the daily aggregate number of buy and sellorders, the AACD model makes full use of transaction data and allows forinteractions between buy and sell orders.

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Statistics With Estimated Parameters, Zhenlin Yang, Yiu Kuen Tse, Zhidong Bai Jan 2007

Statistics With Estimated Parameters, Zhenlin Yang, Yiu Kuen Tse, Zhidong Bai

Research Collection School Of Economics

This paper studies a general problem of making inferences for functions of two sets of parameters where, when the first set is given, there exists a statistic with a known distribution. We study the distribution of this statistic when the first set of parameters is unknown and is replaced by an estimator. We show that under mild conditions the variance of the statistic is inflated when the unconstrained maximum likelihood estimator (MLE) is used, but deflated when the constrained MLE is used. The results are shown to be useful in hypothesis testing and confidence-interval construction in providing simpler and improved …

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A Two-Stage Realized Volatility Approach To Estimation Of Diffusion Processes With Discrete Data, Peter C. B. Phillips, Jun Yu Dec 2006

A Two-Stage Realized Volatility Approach To Estimation Of Diffusion Processes With Discrete Data, Peter C. B. Phillips, Jun Yu

Research Collection School Of Economics

This paper motivates and introduces a two-stage method of estimating diffusion processes based on discretely sampled observations. In the first stage we make use of the feasible central limit theory for realized volatility, as developed in [Jacod, J., 1994] and [Barndorff-Nielsen, O., Shephard, N., 2002], to provide a regression model for estimating the parameters in the diffusion function. In the second stage, the in-fill likelihood function is derived by means of the Girsanov theorem and then used to estimate the parameters in the drift function. Consistency and asymptotic distribution theory for these estimates are established in various contexts. The finite …

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Indirect Inference For Dynamic Panel Models, Christian Gourieroux, Peter C. B. Phillips, Jun Yu Dec 2006

Indirect Inference For Dynamic Panel Models, Christian Gourieroux, Peter C. B. Phillips, Jun Yu

Research Collection School Of Economics

It is well-known that maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size (T) and large cross section sample size (N) asymptotics. The estimation bias is particularly relevant in practical applications when T is small and the autoregressive parameter is close to unity. The present paper proposes a general, computationally inexpensive method of bias reduction that is based on indirect inference (Gouriéroux et al., 1993), shows unbiasedness and analyzes efficiency. The method is implemented in a simple linear dynamic panel model, but has wider …

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Household Heterogeneity And Optimal Inter-Temporal Pricing For A Durable-Good Monopoly, Winston T. H. Koh Dec 2006

Household Heterogeneity And Optimal Inter-Temporal Pricing For A Durable-Good Monopoly, Winston T. H. Koh

Research Collection School Of Economics

In this paper, I extend the analysis in Koh (2006) to examine the optimality of inter-temporal price discrimination for a durable-good monopoly in a model where infinitely-lived households consume both durable goods and a stream of non-durable goods subject to different inter-temporal budget constraints. I also consider the multi-dimensional setting where households differ in both inter-temporal budget constraints and the utilities they derive from the consumption of the durable good.

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Mixing Frequencies: Stock Returns As A Predictor Of Real Output Growth, Anthony S. Tay Dec 2006

Mixing Frequencies: Stock Returns As A Predictor Of Real Output Growth, Anthony S. Tay

Research Collection School Of Economics

We investigate two methods for using daily stock returns to forecast, and update forecasts of, quarterly real output growth. Both methods aggregate daily returns in some manner to form a single stock market variable. We consider (i) augmenting the quarterly AR(1) model for real output growth with daily returns using a nonparametric Mixed Data Sampling (MIDAS) setting, and (ii) augmenting the quarterly AR(1) model with the most recent r -day returns as an additional predictor. We find that our mixed frequency models perform well in forecasting real output growth.

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Simulation-Based Estimation Of Contingent-Claims Prices, Peter C. B. Phillips, Jun Yu Dec 2006

Simulation-Based Estimation Of Contingent-Claims Prices, Peter C. B. Phillips, Jun Yu

Research Collection School Of Economics

A new methodology is proposed to estimate theortical prices of financial contingent-claims whose values are dependent on some other underlying financial assets. In the literature the preferred choice of estimator is usually maximum likelihood (ML). ML has strong asymptotic justification but is not necessarily the best method in finite samples. The present paper proposes instead a simulation-based method that improves the finite sample performance of the ML estimator while maintaining its good asymptotic properties. The methods are implemented and evaluated here in the Black-Scholes option pricing model and in the Vasicek bond pricing model, but have wider applicability. Monte Carlo …

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Maximum Likelihood And Gaussian Estimation Of Continuous Time Models In Finance, Peter C. B. Phillips, Jun Yu Dec 2006

Maximum Likelihood And Gaussian Estimation Of Continuous Time Models In Finance, Peter C. B. Phillips, Jun Yu

Research Collection School Of Economics

This paper overviews maximum likelihood and Gaussian methods of estimating continuous time models used in finance. Since the exact likelihood can be constructed only in special cases, much attention has been devoted to the development of methods designed to approximate the likelihood. These approaches range from crude Euler-type approximations and higher order stochastic Taylor series expansions to more complex polynomial-based expansions and infill approximations to the likelihood based on a continuous time data record. The methods are discussed, their properties are outlined and their relative finite sample performance compared in a simulation experiment with the nonlinear CIR diffusion model, which …

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Multivariate Stochastic Volatility, Manabu Asai, Michael Mcaleer, Jun Yu Dec 2006

Multivariate Stochastic Volatility, Manabu Asai, Michael Mcaleer, Jun Yu

Research Collection School Of Economics

The literature on multivariate stochastic volatility (MSV) models has developed significantly over the last few years. This paper reviews the substantial literature on specification, estimation and evaluation of MSV models. A wide range of MSV models is presented according to various categories, namely (i) asymmetric models; (ii) factor models; (iii) time-varying correlation models; and (iv) alternative MSV specifications, including models based on the matrix exponential transformation, Cholesky decomposition, Wishart autoregressive process, and the empirical range. Alternative methods of estimation, including quasi-maximum likelihood, simulated maximum likelihood, Monte Carlo likelihood, and Markov chain Monte Carlo methods, are discussed and compared. Various methods …

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Lottery Rather Than Waiting-Line Auction, Winston T. H. Koh, Zhenlin Yang, Lijing Zhu Oct 2006

Lottery Rather Than Waiting-Line Auction, Winston T. H. Koh, Zhenlin Yang, Lijing Zhu

Research Collection School Of Economics

This paper investigates the allocative efficiency of two non-price allocation mechanisms – the lottery (random allocation) and the waiting-line auction (queue system) – for the cases where consumers possess identical time costs (the homogeneous case), and where time costs are correlated with time valuations (the heterogeneous case). We show that the relative efficiency of the two mechanisms depends critically on a scarcity factor (measured by the ratio of the number of objects available for allocation over the number of participants) and on the shape of the distribution of valuations. We show that the lottery dominates the waiting-line auction for a …

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On Joint Modelling And Testing For Local And Global Spatial Externalities, Zhenlin Yang Oct 2006

On Joint Modelling And Testing For Local And Global Spatial Externalities, Zhenlin Yang

Research Collection School Of Economics

This paper concerns the joint modeling, estimation and testing for local and global spatial externalities. Spatial externalities have become in recent years a standard notion of economic research activities in relation to social interactions, spatial spillovers and dependence, etc., and have received an increasing attention by econometricians and applied researchers. While conceptually the principle underlying the spatial dependence is straightforward, the precise way in which this dependence should be included in a regression model is complex. Following the taxonomy of Anselin (2003, International Regional Science Review 26, 153-166), a general model is proposed, which takes into account jointly local and …

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Set Inference For Semiparametric Discrete Games, Kyoo-Il Kim Sep 2006

Set Inference For Semiparametric Discrete Games, Kyoo-Il Kim

Research Collection School Of Economics

We consider estimation and inference of parameters in discrete games allowing for multiple equilibria, without using an equilibrium selection rule. We do a set inference while a game model can contain infinite dimensional parameters. Examples can include signaling games with discrete types where the type distribution is nonparametrically specified and entry-exit games with partially linear payoffs functions. A consistent set estimator and a confidence interval of a function of parameters are provided in this paper. We note that achieving a consistent point estimation often requires an information reduction. Due to this less use of information, we may end up a …

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Semiparametric Estimation Of Signaling Games, Kyoo-Il Kim Sep 2006

Semiparametric Estimation Of Signaling Games, Kyoo-Il Kim

Research Collection School Of Economics

This paper studies an econometric modeling of a signaling game with two players where one player has one of two types. In particular, we develop an estimation strategy that identifies the payoffs structure and the distribution of types from data of observed actions. We can achieve uniqueness of equilibrium using a refinement, which enables us to identify the parameters of interest. In the game, we consider non-strategic public signals about the types. Because the mixing distribution of these signals is nonparametrically specified, we propose to estimate the model using a sieve conditional MLE. We achieve the consistency and the asymptotic …

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Uniform Convergence Rate Of The Snp Density Estimator And Testing For Similarity Of Two Unknown Densities, Kyoo-Il Kim Sep 2006

Uniform Convergence Rate Of The Snp Density Estimator And Testing For Similarity Of Two Unknown Densities, Kyoo-Il Kim

Research Collection School Of Economics

This paper studies the uniform convergence rate of the turncated SNP (semi-nonparametric) density estimator. Using the uniform convergence rate result we obtain, we propose a test statistic testing the equivalence of two unknown densities where two densities are estimated using the SNP estimator and supports of densities are possibly unbounded.

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Multivariate Stochastic Volatility Models: Bayesian Estimation And Model Comparison, Jun Yu, Renate Meyer Sep 2006

Multivariate Stochastic Volatility Models: Bayesian Estimation And Model Comparison, Jun Yu, Renate Meyer

Research Collection School Of Economics

In this paper we show that fully likelihood-based estimation and comparison of multivariate stochastic volatility (SV) models can be easily performed via a freely available Bayesian software called WinBUGS. Moreover, we introduce to the literature several new specifications that are natural extensions to certain existing models, one of which allows for time-varying correlation coefficients. Ideas are illustrated by fitting, to a bivariate time series data of weekly exchange rates, nine multivariate SV models, including the specifications with Granger causality in volatility, time-varying correlations, heavy-tailed error distributions, additive factor structure, and multiplicative factor structure. Empirical results suggest that the best specifications …

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Higher Order Bias Correcting Moment Equation For M-Estimation And Its Higher Order Efficiency, Kyoo-Il Kim Sep 2006

Higher Order Bias Correcting Moment Equation For M-Estimation And Its Higher Order Efficiency, Kyoo-Il Kim

Research Collection School Of Economics

This paper studies an alternative bias correction for the M-estimator, which is obtained by correcting the moment equation in the spirit of Firth (1993). In particular, this paper compares the stochastic expansions of the analytically bias-corrected estimator and the alternative estimator and finds that the third-order stochastic expansions of these two estimators are identical. This implies that at least in terms of the third order stochastic expansion, we cannot improve on the simple one-step bias correction by using the bias correction of moment equations. Though the result in this paper is for a fixed number of parameters, our intuition may …

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Spectral Density Estimation And Robust Hypothesis Testing Using Steep Origin Kernels Without Truncation, Peter C.B Philips, Yixiao Sun, Sainan Jin Aug 2006

Spectral Density Estimation And Robust Hypothesis Testing Using Steep Origin Kernels Without Truncation, Peter C.B Philips, Yixiao Sun, Sainan Jin

Research Collection School Of Economics

A new class of kernels for long-run variance and spectral density estimation is developed by exponentiating traditional quadratic kernels. Depending on whether the exponent parameter is allowed to grow with the sample size, we establish different asymptotic approximations to the sampling distribution of the proposed estimators. When the exponent is passed to infinity with the sample size, the new estimator is consistent and shown to be asymptotically normal. When the exponent is fixed, the new estimator is inconsistent and has a nonstandard limiting distribution. It is shown via Monte Carlo experiments that, when the chosen exponent is small in practical …

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A Modified Family Of Power Transformations, Zhenlin Yang Jul 2006

A Modified Family Of Power Transformations, Zhenlin Yang

Research Collection School Of Economics

A modified family of power transformation, called the dual power transformation, is proposed. The new transformation is shown to possess properties similar to those of the well-known Box-Cox power transformation, but overcomes the long-standing truncation problem of the latter. It generates a rich family of distributions that is seen to be very useful in modeling and analysis of durations and event-times.

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