Journal of Econometrics最新文献

{"title":"Vector autoregressions with dynamic factor coefficients and conditionally heteroskedastic errors","authors":"Paolo Gorgi ,&nbsp;Siem Jan Koopman ,&nbsp;Julia Schaumburg","doi":"10.1016/j.jeconom.2024.105750","DOIUrl":"10.1016/j.jeconom.2024.105750","url":null,"abstract":"<div><div>We introduce a new and general methodology for analyzing vector autoregressive models with time-varying coefficient matrices and conditionally heteroskedastic disturbances. The proposed approach is transparent and simple to implement. It allows the derivation of well-defined impulse response functions that rely on the overall stability of the system. We present the finite sample properties of the model in a simulation study. In an empirical illustration we investigate the possibly time-varying relationships between U.S. industrial production, inflation, and bond spread. We empirically identify a time-varying linkage between economic and financial variables which are effectively described by a common dynamic factor. The impulse response analysis identifies substantial differences in the effects of financial shocks on output and inflation during crisis and non-crisis periods. The results also illustrate how the widely-used approach of fixing the VAR coefficients in the derivation of the impulse responses leads to a sizeable underestimation of the impact of a financial shock on output and inflation during some of the crises in our sample.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 2","pages":"Article 105750"},"PeriodicalIF":9.9,"publicationDate":"2024-09-01","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Target PCA: Transfer learning large dimensional panel data","authors":"Junting Duan ,&nbsp;Markus Pelger ,&nbsp;Ruoxuan Xiong","doi":"10.1016/j.jeconom.2023.105521","DOIUrl":"10.1016/j.jeconom.2023.105521","url":null,"abstract":"<div><div><span>This paper develops a novel method to estimate a latent factor model for a large target panel with missing observations by optimally using the information from auxiliary<span> panel data sets. We refer to our estimator as target-PCA. Transfer learning from auxiliary panel data allows us to deal with a large fraction of missing observations and weak signals in the target panel. We show that our estimator is more efficient and can consistently estimate weak factors, which are not identifiable with conventional methods. We provide the asymptotic inferential theory for target-PCA under very general assumptions on the approximate factor model and missing patterns. In an empirical study of imputing data in a mixed-frequency </span></span>macroeconomic panel, we demonstrate that target-PCA significantly outperforms all benchmark methods.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 2","pages":"Article 105521"},"PeriodicalIF":9.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135661555","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}

{"title":"Some fixed-b results for regressions with high frequency data over long spans","authors":"Taeyoon Hwang ,&nbsp;Timothy J. Vogelsang","doi":"10.1016/j.jeconom.2024.105773","DOIUrl":"10.1016/j.jeconom.2024.105773","url":null,"abstract":"<div><div>This paper develops fixed-<span><math><mi>b</mi></math></span><span> asymptotic results for heteroskedasticity autocorrelation robust (HAR) Wald tests for high frequency data using the continuous time framework of Chang et al. (2023) (CLP). It is shown that the fixed-</span><span><math><mi>b</mi></math></span> limit of HAR Wald tests for high frequency stationary regressions is the same as the standard fixed-<span><math><mi>b</mi></math></span> limit in Kiefer and Vogelsang (2005). For the case of cointegrating regression the form of the fixed-<span><math><mi>b</mi></math></span> limits are different from the stationary case and may or may not be pivotal but also have the same fixed-<span><math><mi>b</mi></math></span> limits that have been obtained for tests based on ordinary least squares (OLS) (Bunzel, 2006) and integrated modified OLS (Vogelsang and Wagner, 2014). A simulation study shows that fixed-<span><math><mi>b</mi></math></span> critical values provide rejection probabilities closer to nominal levels than traditional chi-square critical values when using data-dependent bandwidths. The Andrews (1991) data-dependent method works reasonably well for a wider range of persistence parameters than those considered by CLP. In contrast, the Newey and West (1994) data-dependent method is sensitive to the choice of pre-tuning parameters. The data-dependent method of Sun et al. (2008) give results similar to the Andrews (1991) method with slightly less over-rejection problems when used with fixed-<span><math><mi>b</mi></math></span> critical values. Our results for bandwidth choice reinforce the importance of high frequency compatibility of bandwidths as emphasized by CLP. Regardless of the bandwidth method used in practice, it is clear that fixed-<span><math><mi>b</mi></math></span> critical values can and should be used for high frequency data whenever HAR tests are based on kernel estimators of long run variances. Our results complement the analysis of Pellatt and Sun (2023) who focused on HAR tests based on orthonormal series estimators of long run variance estimator.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 2","pages":"Article 105773"},"PeriodicalIF":9.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141413458","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}

{"title":"Scenario-based quantile connectedness of the U.S. interbank liquidity risk network","authors":"Tomohiro Ando ,&nbsp;Jushan Bai ,&nbsp;Lina Lu ,&nbsp;Cindy M. Vojtech","doi":"10.1016/j.jeconom.2024.105786","DOIUrl":"10.1016/j.jeconom.2024.105786","url":null,"abstract":"<div><div><span><span>We characterize the U.S.<span> interbank liquidity risk network based on a supervisory dataset, using a scenario-based quantile network connectedness approach. In terms of methodology, we consider a quantile vector autoregressive model with unobserved heterogeneity and propose a </span></span>Bayesian nuclear norm estimation method. A common factor structure is employed to deal with unobserved heterogeneity that may exhibit endogeneity within the network. Then we develop a scenario-based quantile network connectedness framework by accommodating various economic scenarios, through a scenario-based moving average expression of the model where forecast error variance decomposition under a future pre-specified scenario is derived. The methodology is used to study the quantile-dependent liquidity risk network among large U.S. bank holding companies. The estimated quantile liquidity risk network connectedness measures could be useful for bank supervision and </span>financial stability<span><span> monitoring by providing leading indicators of the system-wide liquidity risk connectedness not only at the median but also at the </span>tails or even under a pre-specified scenario. The measures also help identify systemically important banks and vulnerable banks in the liquidity risk transmission of the U.S. banking system.</span></div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 2","pages":"Article 105786"},"PeriodicalIF":9.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723206","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}

{"title":"Tuning-parameter-free propensity score matching approach for causal inference under shape restriction","authors":"Yukun Liu ,&nbsp;Jing Qin","doi":"10.1016/j.jeconom.2024.105829","DOIUrl":"10.1016/j.jeconom.2024.105829","url":null,"abstract":"<div><p>Propensity score matching (PSM) is a pseudo-experimental method that uses statistical techniques to construct an artificial control group by matching each treated unit with one or more untreated units of similar characteristics. To date, the problem of determining the optimal number of matches per unit, which plays an important role in PSM, has not been adequately addressed. We propose a tuning-parameter-free PSM approach to causal inference based on the nonparametric maximum-likelihood estimation of the propensity score under the monotonicity constraint. The estimated propensity score is piecewise constant, and therefore automatically groups data. Hence, our proposal is free of tuning parameters. The proposed causal effect estimator is asymptotically semiparametric efficient when the covariate is univariate or the outcome and the propensity score depend on the covariate through the same index <span><math><mrow><msup><mrow><mi>X</mi></mrow><mrow><mo>⊤</mo></mrow></msup><mi>β</mi></mrow></math></span>. We conclude that matching methods based on the propensity score alone cannot, in general, be efficient.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 1","pages":"Article 105829"},"PeriodicalIF":9.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945609","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}

{"title":"Fixed-b asymptotics for panel models with two-way clustering","authors":"Kaicheng Chen,&nbsp;Timothy J. Vogelsang","doi":"10.1016/j.jeconom.2024.105831","DOIUrl":"10.1016/j.jeconom.2024.105831","url":null,"abstract":"<div><p>This paper studies a cluster robust variance estimator proposed by Chiang, Hansen and Sasaki (2024) for linear panels. First, we show algebraically that this variance estimator (CHS estimator, hereafter) is a linear combination of three common variance estimators: the one-way unit cluster estimator, the “HAC of averages” estimator, and the “average of HACs” estimator. Based on this finding, we obtain a fixed-<span><math><mi>b</mi></math></span> asymptotic result for the CHS estimator and corresponding test statistics as the cross-section and time sample sizes jointly go to infinity. Furthermore, we propose two simple bias-corrected versions of the variance estimator and derive the fixed-<span><math><mi>b</mi></math></span> limits. In a simulation study, we find that the two bias-corrected variance estimators along with fixed-<span><math><mi>b</mi></math></span> critical values provide improvements in finite sample coverage probabilities. We illustrate the impact of bias-correction and use of the fixed-<span><math><mi>b</mi></math></span> critical values on inference in an empirical example on the relationship between industry profitability and market concentration.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 1","pages":"Article 105831"},"PeriodicalIF":9.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142050189","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}

{"title":"Testing for sparse idiosyncratic components in factor-augmented regression models","authors":"Jad Beyhum ,&nbsp;Jonas Striaukas","doi":"10.1016/j.jeconom.2024.105845","DOIUrl":"10.1016/j.jeconom.2024.105845","url":null,"abstract":"<div><p>We propose a novel bootstrap test of a dense model, namely factor regression, against a sparse plus dense alternative model augmented with sparse idiosyncratic components. The asymptotic properties of the test are established under time series dependence and polynomial tails. We outline a data-driven rule to select the tuning parameter and prove its theoretical validity. In simulation experiments, our procedure exhibits high power against sparse alternatives and low power against dense deviations from the null. Moreover, we apply our test to various datasets in macroeconomics and finance and often reject the null. This suggests the presence of sparsity — on top of a dense component — in commonly studied economic applications. The R package ‘<span>FAS</span>’ implements our approach.</p></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 1","pages":"Article 105845"},"PeriodicalIF":9.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142097629","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}

{"title":"On uniform confidence intervals for the tail index and the extreme quantile","authors":"Yuya Sasaki ,&nbsp;Yulong Wang","doi":"10.1016/j.jeconom.2024.105865","DOIUrl":"10.1016/j.jeconom.2024.105865","url":null,"abstract":"<div><div>This paper presents two results concerning uniform confidence intervals for the tail index and the extreme quantile. First, we show that there exists a lower bound of the length for confidence intervals that satisfy the correct uniform coverage over a nonparametric family of tail distributions. Second, in light of the impossibility result, we construct honest confidence intervals that are uniformly valid by incorporating the worst-case bias in the nonparametric family. The proposed method is applied to simulated data and real data of financial time series.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 1","pages":"Article 105865"},"PeriodicalIF":9.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327623","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}

{"title":"High-dimensional model-assisted inference for treatment effects with multi-valued treatments","authors":"Wenfu Xu ,&nbsp;Zhiqiang Tan","doi":"10.1016/j.jeconom.2024.105852","DOIUrl":"10.1016/j.jeconom.2024.105852","url":null,"abstract":"<div><div>Consider estimation of average treatment effects with multi-valued treatments using augmented inverse probability weighted (IPW) estimators, depending on outcome regression and propensity score models in high-dimensional settings. These regression models are often fitted by regularized likelihood-based estimation, while ignoring how the fitted functions are used in the subsequent inference about the treatment parameters. Such separate estimation can be associated with known difficulties in existing methods. We develop regularized calibrated estimation for fitting propensity score and outcome regression models, where sparsity-including penalties are employed to facilitate variable selection but the loss functions are carefully chosen such that valid confidence intervals can be obtained under possible model misspecification. Unlike in the case of binary treatments, the usual augmented IPW estimator is generalized to ensure just-identification of parameters from new calibration equations. For propensity score estimation, the new loss function and estimating functions are directly tied to achieving covariate balance between weighted treatment groups. We develop practical algorithms for computing the regularized calibrated estimators with group Lasso by innovatively exploiting Fisher scoring, and provide rigorous high-dimensional analysis for the resulting augmented IPW estimators under suitable sparsity conditions, while tackling technical issues absent or overlooked in previous analyses. We present simulation studies and an empirical application to estimate the effects of maternal smoking on birth weights. The proposed methods are implemented in the R package <span>mRCAL</span>.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 1","pages":"Article 105852"},"PeriodicalIF":9.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0304407624001970/pdfft?md5=bcd51f3983e07a702d8ed2d7dc8fdb38&pid=1-s2.0-S0304407624001970-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142314950","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}

{"title":"Latent utility and permutation invariance: A revealed preference approach","authors":"Roy Allen ,&nbsp;John Rehbeck","doi":"10.1016/j.jeconom.2024.105844","DOIUrl":"10.1016/j.jeconom.2024.105844","url":null,"abstract":"<div><div>This paper provides partial identification results for latent utility models that satisfy an invariance property on unobservables such as exchangeability. We employ a simple revealed preference argument to “difference out” unobservables, obtaining identifying inequalities for utility indices. We show the differencing argument is also useful for counterfactual analysis. The framework generalizes existing work in discrete choice by allowing latent feasibility sets and by allowing individuals to purchase multiple (possibly continuous) goods. We present a new framework leveraging nesting structures that generalizes nested logit. In a panel setting, we innovate by allowing preferences for variety.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"244 1","pages":"Article 105844"},"PeriodicalIF":9.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0304407624001891/pdfft?md5=f06dc5d27091606b5c0da892b207657d&pid=1-s2.0-S0304407624001891-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311360","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}