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on Econometrics |
| By: | Liudas Giraitis (Queen Mary University of London, School of Economics and Finance); George Kapetanios (King's College London); Yufei Li (King's College London); Tien Chuong Nguyen (Vietnam National University) |
| Abstract: | This paper explores a semiparametric version of a time-varying regression, where a subset of the regressors have a fixed coefficient and the rest a time-varying one. We provide an estimation method and establish associated theoretical properties of the estimates and standard errors in extended for heterogeneity regression space. In particular, we show that the estimator of the fixed regression coefficient preserves the parametric rate of convergence, and that, despite of general heterogenous environment, the asymptotic normality property for components of regression parameters can be established and the estimators of standard errors have the same form as those given by White (1980). The theoretical properties of the estimator and good finite sample performance are confirmed by Monte Carlo experiments and illustrated by an empirical example on forecasting. |
| Keywords: | structural change, time-varying parameters, non-parametric estimation |
| JEL: | C13 C14 C50 |
| Date: | 2024–12–18 |
| URL: | https://d.repec.org/n?u=RePEc:qmw:qmwecw:985 |
| By: | Yuta Ota (Keio University, Department of Economics); Takahiro Hoshino (Keio University, Department of Economics); Taisuke Otsu (London School of Economics, Department of Economics) |
| Abstract: | Random assignment of treatment and concurrent data collection on treatment and control groups is often impossible in the evaluation of social programs. A standard method for assessing treatment effects in such infeasible situations is to estimate the local average treatment effect under exclusion restriction and monotonicity assumptions. Recently, several studies have proposed methods to estimate the average treatment effect by additionally assuming treatment effects homogeneity across principal strata or conditional independence of assignment and principal strata. However, these assumptions are often difficult to satisfy. We propose a new strategy for nonparametric identification of causal effects that relaxes these assumptions by using auxiliary observations that are readily available in a wide range of settings. Our strategy identifies the average treatment effect for compliers and average treatment effect on treated under only exclusion restrictions and the assumptions on auxiliary observations. The average treatment effect is then identified under relaxed treatment effects homogeneity. We propose sample analog estimators when the assignment is random and multiply robust estimators when the assignment is non-random. We then present details of the GMM estimation and testing methods which utilize overidentified restrictions. The proposed methods are illustrated by empirical examples which revisit the studies by Thornton (2008), Gerber et al. (2009), and Beam (2016), as well as an experimental data related to marketing in a private sector. |
| Keywords: | generalized method of moments, instrumental variables, noncompliance, nonparametric identification, treatment effect |
| JEL: | C14 C31 |
| Date: | 2024–12–02 |
| URL: | https://d.repec.org/n?u=RePEc:keo:dpaper:2024-022 |
| By: | Masahiro Kato |
| Abstract: | This study introduces a debiasing method for regression estimators, including high-dimensional and nonparametric regression estimators. For example, nonparametric regression methods allow for the estimation of regression functions in a data-driven manner with minimal assumptions; however, these methods typically fail to achieve $\sqrt{n}$-consistency in their convergence rates, and many, including those in machine learning, lack guarantees that their estimators asymptotically follow a normal distribution. To address these challenges, we propose a debiasing technique for nonparametric estimators by adding a bias-correction term to the original estimators, extending the conventional one-step estimator used in semiparametric analysis. Specifically, for each data point, we estimate the conditional expected residual of the original nonparametric estimator, which can, for instance, be computed using kernel (Nadaraya-Watson) regression, and incorporate it as a bias-reduction term. Our theoretical analysis demonstrates that the proposed estimator achieves $\sqrt{n}$-consistency and asymptotic normality under a mild convergence rate condition for both the original nonparametric estimator and the conditional expected residual estimator. Notably, this approach remains model-free as long as the original estimator and the conditional expected residual estimator satisfy the convergence rate condition. The proposed method offers several advantages, including improved estimation accuracy and simplified construction of confidence intervals. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.11748 |
| By: | Bernard M.S. van Praag (University of Amsterdam); J. Peter Hop (Independent); William H. Greene (University of South Florida) |
| Abstract: | In the last few decades, the study of ordinal data in which the variable of interest is not exactly observed but only known to be in a specific ordinal category has become important. In Psychometrics such variables are analysed under the heading of item response models (IRM). In Econometrics, subjective well-being (SWB) and self-assessed health (SAH) studies, and in Marketing Research, Ordered Probit, Ordered Logit, and Interval Regression models are common research platforms. To emphasize that the problem is not specific to a specific discipline we will use the neutral term coarsened observation. For single-equation models estimation of the latent linear model by Maximum Likelihood (ML) is routine. But, for higher -dimensional multivariate models it is computationally cumbersome as estimation requires the evaluation of multivariate normal distribution functions on a large scale. Our proposed alternative estimation method, based on the Generalized Method of Moments (GMM), circumvents this multivariate integration problem. The method is based on the assumed zero correlations between explanatory variables and generalized residuals. This is more general than ML but coincides with ML if the error distribution is multivariate normal. It can be implemented by repeated application of standard techniques. GMM provides a simpler and faster approach than the usual ML approach. It is applicable to multiple equation models with K-dimensional error correlation matrices and kJ response categories for the the kth equation. It also yields a simple method to estimate polyserial and polychoric correlations. Comparison of our method with the outcomes of the Stata ML procedure cmp yields estimates that are not statistically different, while estimation by our method requires only a fraction of the computing time. |
| Keywords: | ordered qualitative data, item response models, multivariate ordered probit, ordinal data analysis, generalized method of moments, polychoric correlations, coarsened events |
| JEL: | C13 C15 C24 C25 C26 C33 C34 C35 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:usf:wpaper:2024-06 |
| By: | Ruonan Xu; Luther Yap |
| Abstract: | We show how clustering standard errors in one or more dimensions can be justified in M-estimation when there is sampling or assignment uncertainty. Since existing procedures for variance estimation are either conservative or invalid, we propose a variance estimator that refines a conservative procedure and remains valid. We then interpret environments where clustering is frequently employed in empirical work from our design-based perspective and provide insights on their estimands and inference procedures. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13372 |
| By: | Michael C. Knaus |
| Abstract: | Estimators that weight observed outcomes to form effect estimates have a long tradition. Their outcome weights are widely used in established procedures, such as checking covariate balance, characterizing target populations, or detecting and managing extreme weights. This paper introduces a general framework for deriving such outcome weights. It establishes when and how numerical equivalence between an original estimator representation as moment condition and a unique weighted representation can be obtained. The framework is applied to derive novel outcome weights for the six seminal instances of double machine learning and generalized random forests, while recovering existing results for other estimators as special cases. The analysis highlights that implementation choices determine (i) the availability of outcome weights and (ii) their properties. Notably, standard implementations of partially linear regression-based estimators, like causal forests, employ outcome weights that do not sum to (minus) one in the (un)treated group, not fulfilling a property often considered desirable. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.11559 |
| By: | Martin Bruns (School of Economics, University of East Anglia); Helmut Lutkepohl (DIW Berlin and Freie Universitat Berlin) |
| Abstract: | A central assumption for identifying structural shocks in vector autoregressive (VAR) models via heteroskedasticity is the time-invariance of the impact effects of the shocks. It is shown how that assumption can be tested when long-run restrictions are available for identifying structural shocks. The importance of performing such tests is illustrated by investigating the impact of fundamental shocks on stock prices in the U.S.. It is found that fundamental shocks post-1986 have become more important than in the pre-1986 period. |
| Keywords: | Structural vector autoregression, heteroskedasticity, cointegration, structural vector error correction model |
| JEL: | C32 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:uea:ueaeco:2024-06 |
| By: | Pierdomenico Duttilo |
| Abstract: | This PhD Thesis presents an investigation into the analysis of financial returns using mixture models, focusing on mixtures of generalized normal distributions (MGND) and their extensions. The study addresses several critical issues encountered in the estimation process and proposes innovative solutions to enhance accuracy and efficiency. In Chapter 2, the focus lies on the MGND model and its estimation via expectation conditional maximization (ECM) and generalized expectation maximization (GEM) algorithms. A thorough exploration reveals a degeneracy issue when estimating the shape parameter. Several algorithms are proposed to overcome this critical issue. Chapter 3 extends the theoretical perspective by applying the MGND model on several stock market indices. A two-step approach is proposed for identifying turmoil days and estimating returns and volatility. Chapter 4 introduces constrained mixture of generalized normal distributions (CMGND), enhancing interpretability and efficiency by imposing constraints on parameters. Simulation results highlight the benefits of constrained parameter estimation. Finally, Chapter 5 introduces generalized normal distribution-hidden Markov models (GND-HMMs) able to capture the dynamic nature of financial returns. This manuscript contributes to the statistical modelling of financial returns by offering flexible, parsimonious, and interpretable frameworks. The proposed mixture models capture complex patterns in financial data, thereby facilitating more informed decision-making in financial analysis and risk management. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.11847 |
| By: | Ashesh Rambachan; Rahul Singh; Davide Viviano |
| Abstract: | While traditional program evaluations typically rely on surveys to measure outcomes, certain economic outcomes such as living standards or environmental quality may be infeasible or costly to collect. As a result, recent empirical work estimates treatment effects using remotely sensed variables (RSVs), such mobile phone activity or satellite images, instead of ground-truth outcome measurements. Common practice predicts the economic outcome from the RSV, using an auxiliary sample of labeled RSVs, and then uses such predictions as the outcome in the experiment. We prove that this approach leads to biased estimates of treatment effects when the RSV is a post-outcome variable. We nonparametrically identify the treatment effect, using an assumption that reflects the logic of recent empirical research: the conditional distribution of the RSV remains stable across both samples, given the outcome and treatment. Our results do not require researchers to know or consistently estimate the relationship between the RSV, outcome, and treatment, which is typically mis-specified with unstructured data. We form a representation of the RSV for downstream causal inference by predicting the outcome and predicting the treatment, with better predictions leading to more precise causal estimates. We re-evaluate the efficacy of a large-scale public program in India, showing that the program's measured effects on local consumption and poverty can be replicated using satellite |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.10959 |
| By: | Ruicheng Ao; Hongyu Chen; David Simchi-Levi |
| Abstract: | In this work, we introduce a new framework for active experimentation, the Prediction-Guided Active Experiment (PGAE), which leverages predictions from an existing machine learning model to guide sampling and experimentation. Specifically, at each time step, an experimental unit is sampled according to a designated sampling distribution, and the actual outcome is observed based on an experimental probability. Otherwise, only a prediction for the outcome is available. We begin by analyzing the non-adaptive case, where full information on the joint distribution of the predictor and the actual outcome is assumed. For this scenario, we derive an optimal experimentation strategy by minimizing the semi-parametric efficiency bound for the class of regular estimators. We then introduce an estimator that meets this efficiency bound, achieving asymptotic optimality. Next, we move to the adaptive case, where the predictor is continuously updated with newly sampled data. We show that the adaptive version of the estimator remains efficient and attains the same semi-parametric bound under certain regularity assumptions. Finally, we validate PGAE's performance through simulations and a semi-synthetic experiment using data from the US Census Bureau. The results underscore the PGAE framework's effectiveness and superiority compared to other existing methods. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.12036 |
| By: | Christian D. Blakely |
| Abstract: | We propose an error-correcting model for the microprice, a high-frequency estimator of future prices given higher order information of imbalances in the orderbook. The model takes into account a current microprice estimate given the spread and best bid to ask imbalance, and adjusts the microprice based on recent dynamics of higher price rank imbalances. We introduce a computationally fast estimator using a recently proposed hyperdimensional vector Tsetlin machine framework and demonstrate empirically that this estimator can provide a robust estimate of future prices in the orderbook. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13594 |
| By: | Gabriel Rodriguez-Rondon |
| Abstract: | This paper introduces a new approach for estimating core inflation indicators based on common factors across a broad range of price indices. Specifically, by utilizing procedures for detecting multiple regimes in high-dimensional factor models, we propose two types of core inflation indicators: one incorporating multiple structural breaks and another based on Markov switching. The structural breaks approach can eliminate revisions for past regimes, though it functions as an offline indicator, as real-time detection of breaks is not feasible with this method. On the other hand, the Markov switching approach can reduce revisions while being useful in real time, making it a simple and robust core inflation indicator suitable for real-time monitoring and as a short-term guide for monetary policy. Additionally, this approach allows us to estimate the probability of being in different inflationary regimes. To demonstrate the effectiveness of these indicators, we apply them to Canadian price data. To compare the real-time performance of the Markov switching approach to the benchmark model without regime-switching, we assess their abilities to forecast headline inflation and minimize revisions. We find that the Markov switching model delivers superior predictive accuracy and significantly reduces revisions during periods of substantial inflation changes. Hence, our findings suggest that accounting for time-varying factors and parameters enhances inflation signal accuracy and reduces data requirements, especially following sudden economic shifts. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.12845 |
| By: | Jeffrey Mollins; Rachit Lumb |
| Abstract: | This paper summarizes and assesses several of the most popular methods to seasonally adjust weekly data. The industry standard approach, known as X-13ARIMA-SEATS, is suitable only for monthly or quarterly data. Given the increased availability and promise of non-traditional data at higher frequencies, alternative approaches are required to extract relevant signals for monitoring and analysis. This paper reviews four such methods for high-frequency seasonal adjustment. We find that tuning the parameters of each method helps deliver a properly adjusted series. We optimize using a grid search and test for residual seasonality in each series. While no method works perfectly for every series, some methods are generally effective at removing seasonality in weekly data, despite the increased difficulty of accounting for the shock of the COVID-19 pandemic. Because seasonally adjusting high-frequency data is typically a difficult task, we recommend closely inspecting each series and comparing results from multiple methods whenever possible. |
| Keywords: | Econometric and statistical methods |
| JEL: | C1 C4 C52 C8 E01 E21 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocadp:24-17 |
| By: | Wang, Shu |
| Abstract: | This paper presents a high-frequency structural VAR framework for identifying oil price shocks and examining their uncertainty transmission in the U.S. macroeconomy and financial markets. Leveraging the stylized features of financial data - specifically, volatility clustering effectively captured by a GARCH model - this approach achieves global identification of shocks while allowing for volatility spillovers across them. Findings reveal that increased variance in aggregate demand shocks increases the oil-equity price covariance, while precautionary demand shocks, triggering heightened investor risk aversion, significantly diminish this covariance. A real-time forecast error variance decomposition further highlights that oil supply uncertainty was the primary source of oil price forecast uncertainty from late March to early May 2020, yet it contributed minimally during the 2022 Russian invasion of Ukraine. |
| Keywords: | Oil price, uncertainty, impulse response functions, structural VAR, forecast error variance decomposition, GARCH |
| JEL: | Q43 Q47 C32 C58 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:cegedp:307602 |
| By: | Fan Yang; Yi Zhang |
| Abstract: | We study the tail asymptotics of the sum of two heavy-tailed random variables. The dependence structure is modeled by copulas with the so-called tail order property. Examples are presented to illustrate the approach. Further for each example we apply the main results to obtain the asymptotic expansions for Value-at-Risk of aggregate risk. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.09657 |
| By: | Vasudha Chopra (Plaksha University); Christian A. Vossler (Department of Economics, University of Tennessee) |
| Abstract: | Researchers deploying stated preference surveys to elicit monetary valuations for public goods commonly use techniques devised to reduce bias in hypothetical choice settings. This practice is conceptually at odds with accumulated evidence that most survey respondents instead perceive that their decisions have economic consequences (i.e., affect their future welfare). We examine three bias reduction procedures in both hypothetical choice and incentive compatible, real payment settings: cheap talk, solemn oath, and certainty adjustment. While we find that the oath reduces willingness to pay (WTP) in a hypothetical setting, the oath instead increases WTP by over 30% in a consequential setting. Cheap talk does not alter mean WTP in a consequential setting but leads to a stark difference in WTP across sexes. Applying the common rules for ex post adjustment of choices based on stated response certainty leads to significant and large decreases in WTP estimates for both hypothetical and consequential cases. Our results suggest that survey researchers should make use of screening questions to better target hypothetical bias reduction techniques to only those prone to bias. |
| Keywords: | hypothetical bias, consequentiality, stated preferences, experiments, solemn oath, cheap talk, certainty adjustment |
| JEL: | C92 D82 D9 H41 Q51 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:ten:wpaper:2024-03 |