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Nonparametric identification and estimation of stochastic block models from many small networks 从众多小型网络中对随机块模型进行非参数识别和估计
IF 9.9 3区 经济学
Journal of Econometrics Pub Date : 2024-06-01 DOI: 10.1016/j.jeconom.2024.105805
Koen Jochmans
{"title":"Nonparametric identification and estimation of stochastic block models from many small networks","authors":"Koen Jochmans","doi":"10.1016/j.jeconom.2024.105805","DOIUrl":"https://doi.org/10.1016/j.jeconom.2024.105805","url":null,"abstract":"<div><p>This paper concerns the analysis of network data when unobserved node-specific heterogeneity is present. We postulate a weighted version of the classic stochastic block model, where nodes belong to one of a finite number of latent communities and the placement of edges between them and any weight assigned to these depend on the communities to which the nodes belong. A simple rank condition is presented under which we establish that the number of latent communities, their distribution, and the conditional distribution of edges and weights given community membership are all nonparametrically identified from knowledge of the joint (marginal) distribution of edges and weights in graphs of a fixed size. The identification argument is constructive and we present a computationally-attractive nonparametric estimator based on it. Limit theory is derived under asymptotics where we observe a growing number of independent networks of a fixed size. The results of a series of numerical experiments are reported on.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"242 2","pages":"Article 105805"},"PeriodicalIF":9.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0304407624001519/pdfft?md5=96261be7bcaa048f4b2943e49f33c18e&pid=1-s2.0-S0304407624001519-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141541626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Identification and estimation of dynamic structural models with unobserved choices 带有未观测选择的动态结构模型的识别和估计
IF 9.9 3区 经济学
Journal of Econometrics Pub Date : 2024-06-01 DOI: 10.1016/j.jeconom.2024.105806
Yingyao Hu , Yi Xin
{"title":"Identification and estimation of dynamic structural models with unobserved choices","authors":"Yingyao Hu ,&nbsp;Yi Xin","doi":"10.1016/j.jeconom.2024.105806","DOIUrl":"10.1016/j.jeconom.2024.105806","url":null,"abstract":"<div><p>This paper develops identification and estimation methods for dynamic discrete choice models when agents’ actions are unobserved by econometricians. We provide conditions under which choice probabilities and latent state transition rules are nonparametrically identified with a continuous state variable in a single-agent dynamic discrete choice model. Our identification strategy from the baseline model can extend to models with serially correlated unobserved heterogeneity, cases in which choices are partially unavailable, and dynamic discrete games. We propose a sieve maximum likelihood estimator for primitives in agents’ utility functions and state transition rules. Monte Carlo simulation results support the validity of the proposed approach.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"242 2","pages":"Article 105806"},"PeriodicalIF":9.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141623392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Correlated Random Coefficient panel model with time-varying endogeneity 具有时变内生性的相关随机系数面板模型
IF 9.9 3区 经济学
Journal of Econometrics Pub Date : 2024-06-01 DOI: 10.1016/j.jeconom.2024.105804
Louise Laage
{"title":"A Correlated Random Coefficient panel model with time-varying endogeneity","authors":"Louise Laage","doi":"10.1016/j.jeconom.2024.105804","DOIUrl":"10.1016/j.jeconom.2024.105804","url":null,"abstract":"<div><p>This paper studies a class of linear panel models with random coefficients. We do not restrict the joint distribution of the time-invariant unobserved heterogeneity and the covariates. We investigate identification of the average partial effect (APE) when fixed-effect techniques cannot be used to control for the correlation between the regressors and the time-varying disturbances. Relying on control variables, we develop a constructive two-step identification argument. The first step identifies nonparametrically the conditional expectation of the disturbances given the regressors and the control variables, and the second step uses “between-group” variation, correcting for endogeneity, to identify the APE. We propose a natural semiparametric estimator of the APE, show its <span><math><msqrt><mrow><mi>n</mi></mrow></msqrt></math></span> asymptotic normality and compute its asymptotic variance. The estimator is computationally easy to implement, and Monte Carlo simulations show favorable finite sample properties. As an empirical illustration, we estimate the average elasticity of intertemporal substitution in a labor supply model with random coefficients.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"242 2","pages":"Article 105804"},"PeriodicalIF":9.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Semiparametrically optimal cointegration test 半参数最优协整检验
IF 9.9 3区 经济学
Journal of Econometrics Pub Date : 2024-06-01 DOI: 10.1016/j.jeconom.2024.105816
Bo Zhou
{"title":"Semiparametrically optimal cointegration test","authors":"Bo Zhou","doi":"10.1016/j.jeconom.2024.105816","DOIUrl":"10.1016/j.jeconom.2024.105816","url":null,"abstract":"<div><p>This paper aims to address the issue of semiparametric efficiency for cointegration rank testing in finite-order vector autoregressive models, where the innovation distribution is considered an infinite-dimensional nuisance parameter. Our asymptotic analysis relies on Le Cam’s theory of limit experiment, which in this context is of the Locally Asymptotically Brownian Functional (LABF) type likelihood ratios. By exploiting the structural representation of LABF, an Ornstein–Uhlenbeck experiment, we develop the asymptotic power envelopes of asymptotically invariant tests for both cases with and without time trends. We propose feasible tests based on a nonparametrically estimated density and demonstrate that their power can achieve the semiparametric power envelopes, making them semiparametrically optimal. We validate the theoretical results through large-sample simulations and illustrate satisfactory size control and excellent power performance of our tests under small samples. In both cases with and without time trends, we show that a remarkable amount of additional power can be obtained from non-Gaussian distributions.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"242 2","pages":"Article 105816"},"PeriodicalIF":9.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Change-point analysis of time series with evolutionary spectra 利用演化谱对时间序列进行变化点分析
IF 9.9 3区 经济学
Journal of Econometrics Pub Date : 2024-06-01 DOI: 10.1016/j.jeconom.2024.105811
Alessandro Casini , Pierre Perron
{"title":"Change-point analysis of time series with evolutionary spectra","authors":"Alessandro Casini ,&nbsp;Pierre Perron","doi":"10.1016/j.jeconom.2024.105811","DOIUrl":"10.1016/j.jeconom.2024.105811","url":null,"abstract":"<div><p>This paper develops change-point methods for the spectrum of a locally stationary time series. We focus on series with a bounded spectral density that change smoothly under the null hypothesis but exhibits change-points or becomes less smooth under the alternative. We address two local problems. The first is the detection of discontinuities (or breaks) in the spectrum at unknown dates and frequencies. The second involves abrupt yet continuous changes in the spectrum over a short time period at an unknown frequency without signifying a break. Both problems can be cast into changes in the degree of smoothness of the spectral density over time. We consider estimation and minimax-optimal testing. We determine the optimal rate for the minimax distinguishable boundary, i.e., the minimum break magnitude such that we are able to uniformly control type I and type II errors. We propose a novel procedure for the estimation of the change-points based on a wild sequential top-down algorithm and show its consistency under shrinking shifts and possibly growing number of change-points.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"242 2","pages":"Article 105811"},"PeriodicalIF":9.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141571081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
On LASSO for high dimensional predictive regression 关于高维预测回归的 LASSO
IF 9.9 3区 经济学
Journal of Econometrics Pub Date : 2024-06-01 DOI: 10.1016/j.jeconom.2024.105809
Ziwei Mei, Zhentao Shi
{"title":"On LASSO for high dimensional predictive regression","authors":"Ziwei Mei,&nbsp;Zhentao Shi","doi":"10.1016/j.jeconom.2024.105809","DOIUrl":"https://doi.org/10.1016/j.jeconom.2024.105809","url":null,"abstract":"<div><p>This paper examines LASSO, a widely-used <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-penalized regression method, in high dimensional linear predictive regressions, particularly when the number of potential predictors exceeds the sample size and numerous unit root regressors are present. The consistency of LASSO is contingent upon two key components: the deviation bound of the cross product of the regressors and the error term, and the restricted eigenvalue of the Gram matrix. We present new probabilistic bounds for these components, suggesting that LASSO’s rates of convergence are different from those typically observed in cross-sectional cases. When applied to a mixture of stationary, nonstationary, and cointegrated predictors, LASSO maintains its asymptotic guarantee if predictors are scale-standardized. Leveraging machine learning and macroeconomic domain expertise, LASSO demonstrates strong performance in forecasting the unemployment rate, as evidenced by its application to the FRED-MD database.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"242 2","pages":"Article 105809"},"PeriodicalIF":9.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141593217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Vector autoregressions with dynamic factor coefficients and conditionally heteroskedastic errors 具有动态因子系数和条件异方差误差的向量自回归
IF 6.3 3区 经济学
Journal of Econometrics Pub Date : 2024-05-16 DOI: 10.1016/j.jeconom.2024.105750
Paolo Gorgi, Siem Jan Koopman, Julia Schaumburg
{"title":"Vector autoregressions with dynamic factor coefficients and conditionally heteroskedastic errors","authors":"Paolo Gorgi, Siem Jan Koopman, Julia Schaumburg","doi":"10.1016/j.jeconom.2024.105750","DOIUrl":"https://doi.org/10.1016/j.jeconom.2024.105750","url":null,"abstract":"","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"44 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141062150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A simple specification test for models with many conditional moment inequalities 对具有多个条件矩不等式的模型进行简单的规格检验
IF 6.3 3区 经济学
Journal of Econometrics Pub Date : 2024-05-01 DOI: 10.1016/j.jeconom.2024.105788
Mathieu Marcoux , Thomas M. Russell , Yuanyuan Wan
{"title":"A simple specification test for models with many conditional moment inequalities","authors":"Mathieu Marcoux ,&nbsp;Thomas M. Russell ,&nbsp;Yuanyuan Wan","doi":"10.1016/j.jeconom.2024.105788","DOIUrl":"https://doi.org/10.1016/j.jeconom.2024.105788","url":null,"abstract":"<div><p>This paper proposes a simple specification test for partially identified models with a large or possibly uncountably infinite number of conditional moment (in)equalities. The approach is valid under weak assumptions, allowing for both weak identification and non-differentiable moment conditions. Computational simplifications are obtained by reusing certain expensive-to-compute components of the test statistic when constructing the critical values. Because of the weak assumptions, the procedure faces a new set of interesting theoretical issues which we show can be addressed by an unconventional sample-splitting procedure that runs multiple tests of the same null hypothesis. The resulting specification test controls size uniformly over a large class of data generating processes, has power tending to 1 for fixed alternatives, and has power against certain local alternatives which we characterize. Finally, the testing procedure is demonstrated in three simulation exercises.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"242 1","pages":"Article 105788"},"PeriodicalIF":6.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0304407624001349/pdfft?md5=684a607d51596956bac4bcdef522a2c3&pid=1-s2.0-S0304407624001349-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141250952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Better the devil you know: Improved forecasts from imperfect models 宁可信其有，不可信其无利用不完善的模型改进预测
IF 6.3 3区 经济学
Journal of Econometrics Pub Date : 2024-05-01 DOI: 10.1016/j.jeconom.2024.105767
Dong Hwan Oh , Andrew J. Patton
{"title":"Better the devil you know: Improved forecasts from imperfect models","authors":"Dong Hwan Oh ,&nbsp;Andrew J. Patton","doi":"10.1016/j.jeconom.2024.105767","DOIUrl":"https://doi.org/10.1016/j.jeconom.2024.105767","url":null,"abstract":"<div><p>Many important economic decisions are based on a parametric forecasting model that is known to be good but imperfect. We propose methods to improve out-of-sample forecasts from a misspecified model by estimating its parameters using a form of local <em>M</em> estimation (thereby nesting local OLS and local MLE), drawing on information from a state variable that is correlated with the misspecification of the model. We theoretically consider the forecast environments in which our approach is likely to offer improvements over standard methods, and we find significant forecast improvements from applying the proposed method across four distinct empirical analyses including volatility forecasting, risk management, and yield curve forecasting.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"242 1","pages":"Article 105767"},"PeriodicalIF":6.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141083567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Modeling long cycles 长周期建模
IF 6.3 3区 经济学
Journal of Econometrics Pub Date : 2024-05-01 DOI: 10.1016/j.jeconom.2024.105751
Da Natasha Kang , Vadim Marmer
{"title":"Modeling long cycles","authors":"Da Natasha Kang ,&nbsp;Vadim Marmer","doi":"10.1016/j.jeconom.2024.105751","DOIUrl":"10.1016/j.jeconom.2024.105751","url":null,"abstract":"<div><p>Recurrent boom-and-bust cycles are a salient feature of economic and financial history. Cycles found in the data are stochastic, often highly persistent, and span substantial fractions of the sample size. We refer to such cycles as “long”. In this paper, we develop a novel approach to modeling cyclical behavior specifically designed to capture long cycles. We show that existing inferential procedures may produce misleading results in the presence of long cycles and propose a new econometric procedure for the inference on the cycle length. Our procedure is asymptotically valid regardless of the cycle length. We apply our methodology to a set of macroeconomic and financial variables for the U.S. We find evidence of long stochastic cycles in the standard business cycle variables, as well as in credit and house prices. However, we rule out the presence of stochastic cycles in asset market data. Moreover, according to our result, financial cycles, as characterized by credit and house prices, tend to be twice as long as business cycles.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"242 1","pages":"Article 105751"},"PeriodicalIF":6.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141188779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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