nep-ecm New Economics Papers
on Econometrics
Issue of 2023‒05‒08
thirteen papers chosen by
Sune Karlsson
Örebro universitet

  1. Testing for idiosyncratic Treatment Effect Heterogeneity By Jaime Ramirez-Cuellar
  2. Extremum Monte Carlo Filters: Real-Time Signal Extraction via Simulation and Regression By Francisco Blasques; Siem Jan Koopman; Karim Moussa
  3. Inference for Aggregate Efficiency: Theory and Guidelines for Practitioners By Léopold Simar; Valentin Zelenyuk; Shirong Zhao
  4. A Note on Quasi-Maximum-Likelihood Estimation in Hidden Markov Models with Covariate-Dependent Transition Probabilities By Demian Pouzo; Zacharias Psaradakis; Martin Sola
  5. Factor modelling for clustering high-dimensional time series By Zhang, Bo; Pan, Guangming; Yao, Qiwei; Wang, Jian-Zhou
  6. Novel techniques for Bayesian inference in univariate and multivariate stochastic volatility models By Mike G. Tsionas
  7. Testing and Identifying Substitution and Complementarity Patterns By Rui Wang
  8. Symmetric positive semi-definite Fourier estimator of instantaneous variance-covariance matrix By Jir\^o Akahori; Nien-Lin Liu; Maria Elvira Mancino; Tommaso Mariotti; Yukie Yasuda
  9. Slow Expectation-Maximization Convergence in Low-Noise Dynamic Factor Models By Daan Opschoor; Dick van Dijk
  10. OFTER: An Online Pipeline for Time Series Forecasting By Nikolas Michael; Mihai Cucuringu; Sam Howison
  11. Graphical Representation of Multidimensional Poverty: Insights for Index Construction and Policy Making By Rodrigo García Arancibia; Ignacio Girela
  12. Optimal Cross-Correlation Estimates from Asynchronous Tick-by-Tick Trading Data By William H. Press
  13. Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage By Rafael Alves; Diego S. de Brito; Marcelo C. Medeiros; Ruy M. Ribeiro

  1. By: Jaime Ramirez-Cuellar
    Abstract: This paper provides asymptotically valid tests for the null hypothesis of no treatment effect heterogeneity. Importantly, I consider the presence of heterogeneity that is not explained by observed characteristics, or so-called idiosyncratic heterogeneity. When examining this heterogeneity, common statistical tests encounter a nuisance parameter problem in the average treatment effect which renders the asymptotic distribution of the test statistic dependent on that parameter. I propose an asymptotically valid test that circumvents the estimation of that parameter using the empirical characteristic function. A simulation study illustrates not only the test's validity but its higher power in rejecting a false null as compared to current tests. Furthermore, I show the method's usefulness through its application to a microfinance experiment in Bosnia and Herzegovina. In this experiment and for outcomes related to loan take-up and self-employment, the tests suggest that treatment effect heterogeneity does not seem to be completely accounted for by baseline characteristics. For those outcomes, researchers could potentially try to collect more baseline characteristics to inspect the remaining treatment effect heterogeneity, and potentially, improve treatment targeting.
    Date: 2023–04
  2. By: Francisco Blasques (Vrije Universiteit Amsterdam); Siem Jan Koopman (Vrije Universiteit Amsterdam); Karim Moussa (Vrije Universiteit Amsterdam)
    Abstract: This paper introduces a novel simulation-based filtering method for general state space models. It allows for the computation of time-varying conditional means, quantiles, and modes, but also for the prediction of latent variables in general. The method relies on generating artificial samples of data from the joint distribution implied by the model and on estimating the conditional quantities of interest via extremum estimation. We call this procedure Extremum Monte Carlo and define a corresponding class of filters for signal extraction. The method can be applied to any model from which data can be simulated and is not liable to the curse of dimensionality. Furthermore, the use of extremum estimation allows for a wide range of conditioning sets, including data with missing entries and unequal spacing. The filtering method also places the computational burden predominantly in the off-line phase, which makes it particularly suitable for real-time applications. We present illustrations for some challenging problems characterized by nonlinearity, high-dimensionality, and intractable density functions.
    Keywords: Nonlinear non-Gaussian state space models, Least squares Monte Carlo, Real-time filtering, Intractable densities, Curse of dimensionality
    Date: 2023–03–24
  3. By: Léopold Simar (Institut de Statistique, Biostatistique et Sciences Actuarielles, Université Catholique de Louvain, Voie du Roman Pays 20, B1348 Louvain-la-Neuve, Belgium); Valentin Zelenyuk (School of Economics and Centre for Efficiency and Productivity Analysis (CEPA) at The University of Queensland, Australia); Shirong Zhao (School of Finance, Dongbei University of Finance and Economics, Dalian, Liaoning 116025)
    Abstract: We expand the recently developed framework for the inference for aggregate efficiency, by extending the existing theory and providing guidelines for practitioners. In particular, we develop the central limit theorems (CLTs) for aggregate input-oriented efficiency, analogous to the output-oriented framework established by Simar and Zelenyuk (2018). To further improve the finite sample performance of the developed CLTs, we propose a simple yet easy to implement method through using the biascorrected individual efficiency estimate to improve the variance estimator. The extensive Monte-Carlo experiments confirmed the developed CLTs for aggregate inputoriented efficiency and also confirmed the better performance of our proposed method in the finite sample sizes. Finally, we use two well-known empirical data sets to illustrate the differences across the existing methods to facilitate the use by practitioners.
    Keywords: Data Envelopment Analysis, Efficiency, Non-parametric Efficiency Estimators, Free Disposal Hull, Aggregate Efficiency
    JEL: C1 C3
    Date: 2023–03
  4. By: Demian Pouzo (University of California); Zacharias Psaradakis (University of London); Martin Sola (Universidad Torcuato di Tella)
    Abstract: We consider hidden Markov models with a discrete-valued regime sequence whose transition probabilities are covariate-dependent. We show that consistent estimation of the parameters of the conditional distribution of the observable variables is possible via quasi-maximum-likelihood based on a (misspecified) mixture model without Markov dependence. Some related numerical results are also discussed.
    Keywords: Consistency; covariate-dependent transition probabilities; hidden Markov model; mixture model; quasi-maximum-likelihood; misspecified model
    Date: 2023–04
  5. By: Zhang, Bo; Pan, Guangming; Yao, Qiwei; Wang, Jian-Zhou
    Abstract: We propose a new unsupervised learning method for clustering a large number of time series based on a latent factor structure. Each cluster is characterized by its own cluster-specific factors in addition to some common factors which impact on all the time series concerned. Our setting also offers the flexibility that some time series may not belong to any clusters. The consistency with explicit convergence rates is established for the estimation of the common factors, the cluster-specific factors, and the latent clusters. Numerical illustration with both simulated data as well as a real data example is also reported. As a spin-off, the proposed new approach also advances significantly the statistical inference for the factor model of Lam and Yao. Supplementary materials for this article are available online.
    Keywords: eigenanalysis; idiosyncratic components; k-means clustering algorithm; strong and weak factors; EP/V007556/1; No.12001517 & 72091212; T&F deal
    JEL: C1
    Date: 2023–04–05
  6. By: Mike G. Tsionas (Lancaster University)
    Abstract: In this paper we exploit properties of the likelihood function of the stochastic volatility model to show that it can be approximated accurately and efficiently using a response surface methodology. The approximation is across the plausible range of parameter values and all possible data and is found to be highly accurate. The methods extend easily to multivariate models and are applied to artificial data as well as ten exchange rates and all stocks of FTSE100 using daily data. Formal comparisons with multivariate GARCH models are undertaken using a special prior for the GARCH parameters. The comparisons are based on marginal likelihood and the Bayes factors.
    Keywords: Stochastic volatility; response surface; likelihood; Monte Carlo.
    JEL: C13 C15
    Date: 2022–02
  7. By: Rui Wang
    Abstract: This paper studies semiparametric identification of substitution and complementarity patterns between two goods using a panel multinomial choice model with bundles. The model allows the two goods to be either substitutes or complements and admits heterogeneous complementarity through observed characteristics. I first provide testable implications for the complementarity relationship between goods. I then characterize the sharp identified set for the model parameters and provide sufficient conditions for point identification. The identification analysis accommodates endogenous covariates through flexible dependence structures between observed characteristics and fixed effects while placing no distributional assumptions on unobserved preference shocks. My method is shown to perform more robustly than the parametric method through Monte Carlo simulations. As an extension, I allow for unobserved heterogeneity in the complementarity, investigate scenarios involving more than two goods, and study a class of nonseparable utility functions.
    Date: 2023–04
  8. By: Jir\^o Akahori; Nien-Lin Liu; Maria Elvira Mancino; Tommaso Mariotti; Yukie Yasuda
    Abstract: In this paper we propose an estimator of spot covariance matrix which ensure symmetric positive semi-definite estimations. The proposed estimator relies on a suitable modification of the Fourier covariance estimator in Malliavin and Mancino (2009) and it is consistent for suitable choices of the weighting kernel. The accuracy and the ability of the estimator to produce positive semi-definite covariance matrices is evaluated with an extensive numerical study, in comparison with the competitors present in the literature. The results of the simulation study are confirmed under many scenarios, that consider the dimensionality of the problem, the asynchronicity of data and the presence of several specification of market microstructure noise.
    Date: 2023–04
  9. By: Daan Opschoor (Erasmus University Rotterdam); Dick van Dijk (Erasmus University Rotterdam)
    Abstract: This paper addresses the poor performance of the Expectation-Maximization (EM) algorithm in the estimation of low-noise dynamic factor models, commonly used in macroeconomic forecasting and nowcasting. We show analytically and in Monte Carlo simulations how the EM algorithm stagnates in a low-noise environment, leading to inaccurate estimates of factor loadings and latent factors. An adaptive version of EM considerably speeds up convergence, producing substantial improvements in estimation accuracy. Modestly increasing the noise level also accelerates convergence. A nowcasting exercise of euro area GDP growth shows gains up to 34% by using adaptive EM relative to the usual EM.
    Keywords: Dynamic factor models, EM algorithm, artificial noise, convergence speed, nowcasting
    JEL: C32 C51 C53 E37
    Date: 2023–04–05
  10. By: Nikolas Michael; Mihai Cucuringu; Sam Howison
    Abstract: We introduce OFTER, a time series forecasting pipeline tailored for mid-sized multivariate time series. OFTER utilizes the non-parametric models of k-nearest neighbors and Generalized Regression Neural Networks, integrated with a dimensionality reduction component. To circumvent the curse of dimensionality, we employ a weighted norm based on a modified version of the maximal correlation coefficient. The pipeline we introduce is specifically designed for online tasks, has an interpretable output, and is able to outperform several state-of-the art baselines. The computational efficacy of the algorithm, its online nature, and its ability to operate in low signal-to-noise regimes, render OFTER an ideal approach for financial multivariate time series problems, such as daily equity forecasting. Our work demonstrates that while deep learning models hold significant promise for time series forecasting, traditional methods carefully integrating mainstream tools remain very competitive alternatives with the added benefits of scalability and interpretability.
    Date: 2023–04
  11. By: Rodrigo García Arancibia (Universidad Nacional del Litoral/CONICET); Ignacio Girela (Universidad Nacional de Córdoba/CONICET)
    Abstract: By means of probabilistic graphical models, in this paper, we present a new framework for exploring relationships among indicators commonly included in the Multidimensional Poverty Index (MPI). In particular, we propose an Ising model with covariates for modeling the MPI as an undirected graph. First, we prove why Ising models are consistent with the theoretical distribution of MPI indicators. Then, a comparison between our estimates and the association measures typically used in the literature is provided. Finally, we show how undirected graphs can complement the MPI policy relevant properties, apart from discovering further insightful patterns that can be useful for policy purposes. This novel approach is illustrated with an empirical application for the global MPI indicators of Guinea and Ecuador, taking living areas and monetary poverty as covariates, respectively
    Keywords: MPI, Markov Random Fields, Ising Model, Conditional Dependency, Deprivations
    JEL: I3 C18 C35
    Date: 2023–04
  12. By: William H. Press
    Abstract: Given two time series, A and B, sampled asynchronously at different times {t_A_i} and {t_B_j}, termed "ticks", how can one best estimate the correlation coefficient \rho between changes in A and B? We derive a natural, minimum-variance estimator that does not use any interpolation or binning, then derive from it a fast (linear time) estimator that is demonstrably nearly as good. This "fast tickwise estimator" is compared in simulation to the usual method of interpolating changes to a regular grid. Even when the grid spacing is optimized for the particular parameters (not often possible in practice), the fast tickwise estimator has generally smaller estimation errors, often by a large factor. These results are directly applicable to tick-by-tick price data of financial assets.
    Date: 2023–03
  13. By: Rafael Alves; Diego S. de Brito; Marcelo C. Medeiros; Ruy M. Ribeiro
    Abstract: We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S\&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (e.g., size, value, and profitability) and use sectoral restrictions in the residual covariance matrix. This restricted model is then estimated using vector heterogeneous autoregressive (VHAR) models with the least absolute shrinkage and selection operator (LASSO). Our methodology improves forecasting precision relative to standard benchmarks and leads to better estimates of minimum variance portfolios.
    Date: 2023–03

This nep-ecm issue is ©2023 by Sune Karlsson. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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