Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Finance (73)
- Asian Studies (34)
- International and Area Studies (34)
- Business (32)
- Economic Theory (28)
-
- Medicine and Health Sciences (24)
- Physical Sciences and Mathematics (21)
- Statistics and Probability (18)
- Finance and Financial Management (17)
- Behavioral Economics (12)
- Macroeconomics (11)
- Real Estate (8)
- Health Economics (7)
- Applied Statistics (5)
- Industrial Organization (4)
- Public Affairs, Public Policy and Public Administration (4)
- Environmental Sciences (3)
- International Economics (3)
- Labor Economics (3)
- Portfolio and Security Analysis (3)
- Public Health (3)
- Statistical Models (3)
- Computer Sciences (2)
- Education (2)
- Growth and Development (2)
- Medical Sciences (2)
- Multivariate Analysis (2)
- Keyword
-
- Specification test (28)
- Bootstrap (23)
- Fixed effects (22)
- Panel data (18)
- High-frequency data (15)
-
- Dynamic panel (14)
- Endogeneity (14)
- Heterogeneity (14)
- Realized volatility (14)
- Empirical likelihood (13)
- Markov chain Monte Carlo (13)
- Autoregression (12)
- Cointegration (12)
- Interactive fixed effects (12)
- Nonstationarity (12)
- Semimartingale (12)
- Spatial dependence (12)
- Structural change (12)
- Bias (11)
- Consistency (11)
- Heteroskedasticity (11)
- Nonparametric regression (11)
- Box-Cox transformation (10)
- Latent variable models (10)
- Local to unity (10)
- Long memory (10)
- Maximum likelihood (10)
- Penalized least squares (10)
- Bias reduction (9)
- Decision theory (9)
- Publication Year
Articles 91 - 120 of 771
Full-Text Articles in Econometrics
Bubble Testing Under Polynomial Trends, Xiaohu Wang, Jun Yu
Bubble Testing Under Polynomial Trends, Xiaohu Wang, Jun Yu
Research Collection School Of Economics
This paper develops the asymptotic theory of the least squares estimator of the autoregressive (AR) coefficient in an AR(1) regression with intercept when data is generated from a polynomial trend model in different forms. It is shown that the commonly used right-tailed unit root tests tend to favor the explosive alternative. A new procedure, which implements the right-tailed unit root tests in an AR(2) regression, is proposed. It is shown that when the data generating process has a polynomial trend, the test statistics based on the new procedure cannot find evidence of explosiveness. Whereas, when the data generating process is …
School Attendance Information Or Conditional Cash Transfer? Evidence From A Randomized Field Experiment In Rural Bangladesh, Tomoki Fujii, Christine Ho, Rohan Ray, Abu S. Shonchoy
School Attendance Information Or Conditional Cash Transfer? Evidence From A Randomized Field Experiment In Rural Bangladesh, Tomoki Fujii, Christine Ho, Rohan Ray, Abu S. Shonchoy
Research Collection School Of Economics
Low school attendance remains an important challenge in resource-poor settings with cash and information constraints. We compare conditional cash transfer (CCT) treatments with framing variations (gain and loss) against attendance information treatment as interventions to address these constraints in a unified framework. Our randomized evaluation shows CCT treatments increase attendance by 11 percentage points, about half of which is attributable to attendance information. These treatments improve girls’ academic aspirations and reduce early marriage. Daily CCT set at a quarter of local child wage maximizes attendance impact. We highlight the importance of low-cost information technology to boost attendance sustainably and cost-effectively.
Volatility Puzzle: Long Memory Or Antipersistency, Shuping Shi, Jun Yu
Volatility Puzzle: Long Memory Or Antipersistency, Shuping Shi, Jun Yu
Research Collection School Of Economics
The log realized volatility (RV) is often modeled as an autoregressive fractionally integrated moving average model ARFIMA(1, d, 0). Two conflicting empirical results have been found in the literature. One stream shows that log RV has a long memory (i.e., the fractional parameter d > 0). The other stream suggests that the autoregressive coefficient α is near unity with antipersistent errors (i.e., d
Finite Sample Comparison Of Alternative Estimators For Fractional Gaussian Noise, Shuping Shi, Jun Yu, Chen Zhang
Finite Sample Comparison Of Alternative Estimators For Fractional Gaussian Noise, Shuping Shi, Jun Yu, Chen Zhang
Research Collection School Of Economics
The fractional Brownian motion (fBm) process is a continuous-time Gaussian process with its increment being the fractional Gaussian noise (fGn). It has enjoyed widespread empirical applications across many fields, from science to economics and finance. The dynamics of fBm and fGn are governed by a fractional parameter H ∈ (0, 1). This paper first derives an analytical expression for the spectral density of fGn and investigates the accuracy of various approximation methods for the spectral density. Next, we conduct an extensive Monte Carlo study comparing the finite sample performance and computational cost of alternative estimation methods for H under the …
On The Optimal Forecast With The Fractional Brownian Motion, Xiaohu Wang, Chen Zhang, Jun Yu
On The Optimal Forecast With The Fractional Brownian Motion, Xiaohu Wang, Chen Zhang, Jun Yu
Research Collection School Of Economics
This paper examines the performance of alternative forecasting formulae with the fractional Brownian motion based on a discrete and finite sample. One formula gives the optimal forecast when a continuous record over the infinite past is available. Another formula gives the optimal forecast when a continuous record over the finite past is available. Alternative discretiza-tion schemes are proposed to approximate these formulae. These alternative discretization schemes are then compared with the conditional expectation of the target variable on the vector of the discrete and finite sample. It is shown that the conditional expectation delivers more accurate forecasts than the discretization-based …
Robust Testing For Explosive Behavior With Strongly Dependent Errors, Yiu Lim Lui, Peter C. B. Phillips, Jun Yu
Robust Testing For Explosive Behavior With Strongly Dependent Errors, Yiu Lim Lui, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
A heteroskedasticity-autocorrelation robust (HAR) test statistic is proposed to test for the presence of explosive roots in financial or real asset prices when the equation errors are strongly dependent. Limit theory for the test statistic is developed and extended to heteroskedastic models. The new test has stable size properties unlike conventional test statistics that typically lead to size distortion and inconsistency in the presence of strongly dependent equation errors. The new procedure can be used to consistently time-stamp the origination and termination of an explosive episode under similar conditions of long memory errors. Simulations are conducted to assess the finite …
Low-Rank Panel Quantile Regression: Estimation And Inference, Yiren Wang, Yichong Zhang, Yichong Zhang
Low-Rank Panel Quantile Regression: Estimation And Inference, Yiren Wang, Yichong Zhang, Yichong Zhang
Research Collection School Of Economics
In this paper, we propose a class of low-rank panel quantile regression models which allow for unobserved slope heterogeneity over both individuals and time. We estimate the heterogeneous intercept and slope matrices via nuclear norm regularization followed by sample splitting, row- and column-wise quantile regressions and debiasing. We show that the estimators of the factors and factor loadings associated with the intercept and slope matrices are asymptotically normally distributed. In addition, we develop two specification tests: one for the null hypothesis that the slope coefficient is a constant over time and/or individuals under the case that true rank of slope …
Bayesian Methods In Economics And Finance: Editor's Introduction, Jun Yu
Bayesian Methods In Economics And Finance: Editor's Introduction, Jun Yu
Research Collection School Of Economics
Modern days, Bayesian methods have gained prominence in theoretical work and applications in economics and finance due to the rapid development of computational technologies and their ability to learn. The special issue intends to examine central aspects in Bayesian analysis and applications, including prior choices, model selection with massive data and latent variables, hypothesis testing, Bayesian learning. In total, this special issue contains ten papers, all subject to the Journal of Econometrics (JOE)’s normal refereeing process. Most of these papers came from a conference held at the ESSEC Singapore campus on 10 December 2018.
Posterior-Based Wald-Type Statistic For Hypothesis Testing, Xiaobin Liu, Yong Li, Jun Yu, Tao Zeng
Posterior-Based Wald-Type Statistic For Hypothesis Testing, Xiaobin Liu, Yong Li, Jun Yu, Tao Zeng
Research Collection School Of Economics
A new Wald-type statistic is proposed for hypothesis testing based on Bayesian posterior distributions under the correct model specification. The new statistic can be explained as a posterior version of the Wald statistic and has several nice properties. First, it is well-defined under improper prior distributions. Second, it avoids Jeffreys–Lindley–Bartlett’s paradox. Third, under the null hypothesis and repeated sampling, it follows a distribution asymptotically, offering an asymptotically pivotal test. Fourth, it only requires inverting the posterior covariance for parameters of interest. Fifth and perhaps most importantly, when a random sample from the posterior distribution (such as MCMC output) is available, …
Efficient Bilateral Trade Via Two-Stage Mechanisms: Comparison Between One-Sided And Two-Sided Asymmetric Information Environments, Takashi Kunimoto, Cuiling Zhang
Efficient Bilateral Trade Via Two-Stage Mechanisms: Comparison Between One-Sided And Two-Sided Asymmetric Information Environments, Takashi Kunimoto, Cuiling Zhang
Research Collection School Of Economics
This paper first considers a bilateral-trade model with one-sided asymmetric information in which one agent (seller) initially owns an indivisible object and is fully informed of its value, while the other agent (buyer) intends to obtain the object whose value is unknown to himself. As no mechanisms can generally result in efficient, voluntary bilateral trades (Jehiel and Pauzner, 2006), we aim to overturn this impossibility result by employing two-stage mechanisms (Mezzetti, 2004) in which first, the outcome (e.g., allocation of the goods) is determined, then the agents observe their own outcome-decision payoffs, and finally, transfers are made. We show that …
A Consistent Specification Test For Dynamic Quantile Models, Peter Horvath, Jia Li, Zhipeng Liao, Andrew J. Patton
A Consistent Specification Test For Dynamic Quantile Models, Peter Horvath, Jia Li, Zhipeng Liao, Andrew J. Patton
Research Collection School Of Economics
Correct specification of a conditional quantile model implies that a particular conditional moment is equal to zero. We nonparametrically estimate the conditional moment function via series regression and test whether it is identically zero using uniform functional inference. Our approach is theoretically justified via a strong Gaussian approximation for statistics of growing dimensions in a general time series setting. We propose a novel bootstrap method in this nonstandard context and show that it significantly outperforms the benchmark asymptotic approximation in finite samples, especially for tail quantiles such as Value-at-Risk (VaR). We use the proposed new test to study the VaR …
Weak Identification Of Long Memory With Implications For Inference, Jia Li, Peter C. B. Phillips, Shuping Shi, Jun Yu
Weak Identification Of Long Memory With Implications For Inference, Jia Li, Peter C. B. Phillips, Shuping Shi, Jun Yu
Research Collection School Of Economics
This paper explores weak identification issues arising in commonly used models of economic and financial time series. Two highly popular configurations are shown to be asymptotically observationally equivalent: one with long memory and weak autoregressive dynamics, the other with antipersistent shocks and a near-unit autoregressive root. We develop a data-driven semiparametric and identification-robust approach to inference that reveals such ambiguities and documents the prevalence of weak identification in many realized volatility and trading volume series. The identification-robust empirical evidence generally favors long memory dynamics in volatility and volume, a conclusion that is corroborated using social-media news flow data.
Win: How Public Entrepreneurship Can Transform The Developing World, Tomoki Fujii
Win: How Public Entrepreneurship Can Transform The Developing World, Tomoki Fujii
Research Collection School Of Economics
This book provides a story about the Infrastructure Development Company Limited (IDCOL) written from the perspective of its first full-time Chief Executive Officer. For those who have never heard of IDCOL, it was created in 1997 by the Government of Bangladesh as a nonbank financial institution to fill the financial gap for developing medium- to large-scale infrastructure. IDCOL had a very modest start with a nominal paid-up capital of less than US$2,000, but its capital, equity, and reserves increased to US$110 million by 2020. During this massive expansion, IDCOL met various challenges. This book gives an account of these challenges …
Variation And Efficiency Of High-Frequency Betas, Congshan Zhang, Jia Li, Viktor Todorov, George Tauchen
Variation And Efficiency Of High-Frequency Betas, Congshan Zhang, Jia Li, Viktor Todorov, George Tauchen
Research Collection School Of Economics
This paper studies the efficient estimation of betas from high-frequency return data on a fixed time interval. Under an assumption of equal diffusive and jump betas, we derive the semiparametric efficiency bound for estimating the common beta and develop an adaptive estimator that attains the efficiency bound. We further propose a Hausman type test for deciding whether the common beta assumption is true from the high-frequency data. In our empirical analysis we provide examples of stocks and time periods for which a common market beta assumption appears true and ones for which this is not the case. We further quantify …
The Grid Bootstrap For Continuous Time Models, Yiu Lim Lui, Weilin Xiao, Jun Yu
The Grid Bootstrap For Continuous Time Models, Yiu Lim Lui, Weilin Xiao, Jun Yu
Research Collection School Of Economics
This article proposes the new grid bootstrap to construct confidence intervals (CI) for the persistence parameter in a class of continuous-time models. It is different from the standard grid bootstrap of Hansen in dealing with the initial condition. The asymptotic validity of the CI is discussed under the in-fill scheme. The modified grid bootstrap leads to uniform inferences on the persistence parameter. Its improvement over in-fill asymptotics is achieved by expanding the coefficient-based statistic around its in-fill asymptotic distribution that is non-pivotal and depends on the initial condition. Monte Carlo studies show that the modified grid bootstrap performs better than …
Occupation Density Estimation For Noisy High-Frequency Data, Congshan Zhang, Jia Li, Tim Bollerslev
Occupation Density Estimation For Noisy High-Frequency Data, Congshan Zhang, Jia Li, Tim Bollerslev
Research Collection School Of Economics
This paper studies the nonparametric estimation of occupation densities for semimartingale processes observed with noise. As leading examples we consider the stochastic volatility of a latent efficient price process, the volatility of the latent noise that separates the efficient price from the actually observed price, and nonlinear transformations of these processes. Our estimation methods are decidedly nonparametric and consist of two steps: the estimation of the spot price and noise volatility processes based on pre-averaging techniques and in-fill asymptotic arguments, followed by a kernel-type estimation of the occupation densities. Our spot volatility estimates attain the optimal rate of convergence, and …
A Posterior-Based Wald-Type Statistic For Hypothesis Testing, Yong Li, Xiaobin Liu, Tao Zeng, Jun Yu
A Posterior-Based Wald-Type Statistic For Hypothesis Testing, Yong Li, Xiaobin Liu, Tao Zeng, Jun Yu
Research Collection School Of Economics
A new Wald-type statistic is proposed for hypothesis testing based on Bayesian posterior distributions under the correct model specification. The new statistic can be explained as a posterior version of the Wald statistic and has several nice properties. First, it is well-defined under improper prior distributions. Second, it avoids Jeffreys–Lindley–Bartlett’s paradox. Third, under the null hypothesis and repeated sampling, it follows a χ2" role="presentation" style="box-sizing: border-box; margin: 0px; padding: 0px; display: inline-block; line-height: normal; font-size: 16.2px; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; position: relative;">χ2 …
Conditional Superior Predictive Ability, Jia Li, Zhipeng Liao, Rogier Quaedvlieg
Conditional Superior Predictive Ability, Jia Li, Zhipeng Liao, Rogier Quaedvlieg
Research Collection School Of Economics
This article proposes a test for the conditional superior predictive ability (CSPA) of a family of forecasting methods with respect to a benchmark. The test is functional in nature: under the null hypothesis, the benchmark’s conditional expected loss is no more than those of the competitors, uniformly across all conditioning states. By inverting the CSPA tests for a set of benchmarks, we obtain confidence sets for the uniformly most superior method. The econometric inference pertains to testing conditional moment inequalities for time series data with general serial dependence, and we justify its asymptotic validity using a uniform non-parametric inference method …
Risk Price Variation: The Missing Half Of Empirical Asset Pricing, Andrew John Patton, Brian M. Weller
Risk Price Variation: The Missing Half Of Empirical Asset Pricing, Andrew John Patton, Brian M. Weller
Research Collection School Of Economics
Equal compensation across assets for the same risk exposures is a bedrock of asset pricing theory and empirics. Yet real-world frictions can violate this equality and create apparently high Sharpe ratio opportunities. We develop new methods for asset pricing with cross-sectional heterogeneity in compensation for risk. We extend k-means clustering to group assets by risk prices and introduce a formal test for whether differences in risk premiums across market segments are too large to occur by chance. We find significant evidence of cross-sectional variation in risk prices for almost all combinations of test assets, factor models, and time periods considered.
Nonignorable Missing Data, Single Index Propensity Score And Profile Synthetic Distribution Function, Xuerong Chen, Denis H. Y. Leung, Jing Qin
Nonignorable Missing Data, Single Index Propensity Score And Profile Synthetic Distribution Function, Xuerong Chen, Denis H. Y. Leung, Jing Qin
Research Collection School Of Economics
In missing data problems, missing not at random is difficult to handle since the response probability or propensity score is confounded with the outcome data model in the likelihood. Existing works often assume the propensity score is known up to a finite dimensional parameter. We relax this assumption and consider an unspecified single index model for the propensity score. A pseudo-likelihood based on the complete data is constructed by profiling out a synthetic distribution function that involves the unknown propensity score. The pseudo-likelihood gives asymptotically normal estimates. Simulations show the method compares favorably with existing methods.
A Panel Clustering Approach To Analyzing Bubble Behavior, Yanbo Liu, Peter C. B. Phillips, Jun Yu
A Panel Clustering Approach To Analyzing Bubble Behavior, Yanbo Liu, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
This study provides new mechanisms for identifying and estimating explosive bubbles in mixed-root panel autoregressions with a latent group structure. A post-clustering approach is employed that combines a recursive k-means clustering al-gorithm with panel-data test statistics for testing the presence of explosive roots in time series trajectories. Uniform consistency of the k-means clustering algorithm is established, showing that the post-clustering estimate is asymptotically equivalent to the oracle counterpart that uses the true group identities. Based on the estimated group membership, right-tailed self-normalized t-tests and coefficient-based J-tests, each with pivotal limit distributions, are introduced to detect the explosive roots. The usual …
Uniform Nonparametric Inference For Spatially Dependent Panel Data: The Xtnpsreg Command, Jia Li, Zhipeng Liao, Wenyu Zhou
Uniform Nonparametric Inference For Spatially Dependent Panel Data: The Xtnpsreg Command, Jia Li, Zhipeng Liao, Wenyu Zhou
Research Collection School Of Economics
In this article, we introduce a command, xtnpsreg, that implements a uniform nonparametric inference procedure for possibly unbalanced panel datasets with general forms of spatio-temporal dependence. We demonstrate how to apply this command in several use cases, including (i) the nonparametric estimation of conditional mean function and its marginal response; (ii) the construction of uniform confidence bands for these nonparametric functional parameters; (iii) specification tests for parametric model restrictions; and (iv) the estimation and uniform inference for functional coefficients in semi-nonparametric models.
Learning Before Testing: A Selective Nonparametric Test For Conditional Moment Restrictions, Jia Li, Zhipeng Liao, Wenyu Zhou
Learning Before Testing: A Selective Nonparametric Test For Conditional Moment Restrictions, Jia Li, Zhipeng Liao, Wenyu Zhou
Research Collection School Of Economics
This paper develops a new test for conditional moment restrictions via nonparametric series regression, with approximating series terms selected by Lasso. Machine-learning the main features of the unknown conditional expectation function beforehand enables the test to seek power in a targeted fashion. The data-driven selection, however, also tends to distort the test’s size nontrivially, because it restricts the (growing-dimensional) score vector in the series regression on a random polytope, and hence, effectively alters the score’s asymptotic normality. A novel critical value is proposed to account for this truncation effect. We establish the size and local power properties of the proposed …
Forecasting Equity Index Volatility By Measuring The Linkage Among Component Stocks, Yue Qiu, Tian Xie, Jun Yu, Qiankun Zhou
Forecasting Equity Index Volatility By Measuring The Linkage Among Component Stocks, Yue Qiu, Tian Xie, Jun Yu, Qiankun Zhou
Research Collection School Of Economics
The linkage among the realized volatilities of component stocks is important when modeling and forecasting the relevant index volatility. In this article, the linkage is measured via an extended Common Correlated Effects (CCEs) approach under a panel heterogeneous autoregression model where unobserved common factors in errors are assumed. Consistency of the CCE estimator is obtained. The common factors are extracted using the principal component analysis. Empirical studies show that realized volatility models exploiting the linkage effects lead to significantly better out-of-sample forecast performance, for example, an up to 32% increase in the pseudo R2. We also conduct various forecasting exercises …
Pitfalls In Bootstrapping Spurious Regression, Peter C. B. Phillips
Pitfalls In Bootstrapping Spurious Regression, Peter C. B. Phillips
Research Collection School Of Economics
The bootstrap is shown to be inconsistent in spurious regression. The failure of the bootstrap is spectacular in that the bootstrap effectively turns a spurious regression into a cointegrating regression. In particular, the serial correlation coefficient of the residuals in the bootstrap regression does not converge to unity, so the bootstrap is not even first order consistent. The block bootstrap serial correlation coefficient does converge to unity and is therefore first order consistent, but has a slower rate of convergence and a different limit distribution from that of the sample data serial correlation coefficient. The analysis covers spurious regressions involving …
A Practical Guide To Harnessing The Har Volatility Model, Adam Clements, Daniel P. A. Preve
A Practical Guide To Harnessing The Har Volatility Model, Adam Clements, Daniel P. A. Preve
Research Collection School Of Economics
The standard heterogeneous autoregressive (HAR) model is perhaps the most popular benchmark model for forecasting return volatility. It is often estimated using raw realized variance (RV) and ordinary least squares (OLS). However, given the stylized facts of RV and well-known properties of OLS, this combination should be far from ideal. The aim of this paper is to investigate how the predictive accuracy of the HAR model depends on the choice of estimator, transformation, or combination scheme made by the market practitioner. In an out-of-sample study, covering the S&P 500 index and 26 frequently traded NYSE stocks, it is found that …
Economic Forecasting In An Epidemic: A Break From The Past?, Hwee Kwan Chow, Keen Meng Choy
Economic Forecasting In An Epidemic: A Break From The Past?, Hwee Kwan Chow, Keen Meng Choy
Research Collection School Of Economics
This paper aims to investigate whether the predictive ability and behaviour of professional forecasters are different during the Covid-19 epidemic as compared with the global financial crisis of 2008 and normal times. To this end, we utilise a survey of professional forecasters in Singapore collated by the central bank to analyse the forecasting record for GDP growth and CPI inflation. We first examine the point forecasts to document the extent of forecast failure in the pandemic crisis and test for behavioural explanations of the possible sources of forecast errors such as leader following and herding behaviour. Using percentile-based summary measures …
Fixed-K Inference For Volatility, Tim Bollerslev, Jia Li, Zhipeng Liao
Fixed-K Inference For Volatility, Tim Bollerslev, Jia Li, Zhipeng Liao
Research Collection School Of Economics
We present a new theory for the conduct of nonparametric inference about the latent spot volatility of a semimartingale asset price process. In contrast to existing theories based on the asymptotic notion of an increasing number of observations in local estimation blocks, our theory treats the estimation block size k as fixed. While the resulting spot volatility estimator is no longer consistent, the new theory permits the construction of asymptotically valid and easy-to-calculate pointwise confidence intervals for the volatility at any given point in time. Extending the theory to a high-dimensional inference setting with a growing number of estimation blocks …
Spatial Dynamic Models With Short Panels: Evaluating The Impact Of Home Purchase Restrictions On Housing Prices, Naqun Huang, Zhenlin Yang
Spatial Dynamic Models With Short Panels: Evaluating The Impact Of Home Purchase Restrictions On Housing Prices, Naqun Huang, Zhenlin Yang
Research Collection School Of Economics
Since the 2007 housing crisis in the United States, many countries have begun implementing various macroprudential policies to curb the ongoing rise in housing prices. As there is no clear consensus in the literature on the efficacy of these interventions, understanding their short-term impacts is crucial in informing future policy designs. Adapting a new econometric technique, we examine the short-term impact of home purchase restrictions in Singapore, accounting for the short panel nature of the data and the existence of dynamic, spatial, spatiotemporal, and unit-specific effects. Using quarterly housing data over 2012Q4-2014Q2, we find that public housing prices decrease by …
Rationalizable Implementation In Finite Mechanisms, Y-C Chen, Takashi Kunimoto, Y Sun, S. Xiong
Rationalizable Implementation In Finite Mechanisms, Y-C Chen, Takashi Kunimoto, Y Sun, S. Xiong
Research Collection School Of Economics
We prove that the Maskin monotonicity condition (proposed by Bergemann et al. (2011)) fully characterizes exact rationalizable implementation in an environment with lotteries and transfers. Different from previous papers, our approach possesses many appealing features simultaneously, e.g., finite mechanisms with no integer game or modulo game are used; no transfers are made in any rationalizable profile; the message space is small; the implementation is robust to information perturbations in the sense of Oury and Tercieux (2012).