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on Econometrics |
| By: | Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Seong Yeon Chang (Department of Economics, Soongsil University, Seoul 06978, Korea) |
| Abstract: | This paper considers predictive regressions in which a structural break is allowed on an unknown date. We establish novel testing procedures for asset return predictability using empirical likelihood methods based on weighted-score equations. The theoretical results are useful in practice because our unified framework does not require distinguishing whether the predictor variables are stationary or nonstationary. Simulations show that the empirical likelihood-based tests perform well in terms of size and power in finite samples. As an empirical analysis, we test asset returns predictability using various predictor variables. |
| Keywords: | Autoregressive process; Empirical likelihood; Structural break; Unit root; Weighted estimation |
| JEL: | C12 C14 C32 G12 |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:kan:wpaper:202206&r= |
| By: | Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Ying Fang (The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian 361005, China and Department of Statistics & Data Science, School of Economics, Xiamen University, Xiamen, Fujian 361005, China); Ming Lin (The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian 361005, China and Department of Statistics and Data Science, School of Economics, Xiamen University, Xiamen, Fujian 361005, China); Mingfeng Zhan (The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian 361005, China) |
| Abstract: | Motivated by the paper by Hsiao, Ching and Wan (2012), which proposed a factor-based model to estimate the average treatment effect with panel data, this paper proposes a quantile treatment effect model for panel data to characterize the distributional effect of a treatment. We propose to estimate the counterfactual quantile for the treated unit by using the relationship between conditional and unconditional distributions. Also, the asymptotic properties for the proposed quantile treatment effect estimator are established, together with discussing the choice of control units and covariates. A simulation study is conducted to illustrate our method. Finally, the proposed method is applied to estimate the quantile treatment effects of introducing CSI 300 index futures trading on both the log-return and volatility of the stock market in China. |
| Keywords: | LASSO method; Panel data; Nonparametric estimation; Quantile regression; Treatment effects. |
| JEL: | C13 C14 C33 C52 C54 |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:kan:wpaper:202205&r= |
| By: | Tadao Hoshino |
| Abstract: | In this paper, we consider the estimation of a continuous treatment effect model in the presence of treatment spillovers through social networks. We assume that one's outcome is affected not only by his/her own treatment but also by the average of his/her neighbors' treatments, both of which are treated as endogenous variables. Using a control function approach with appropriate instrumental variables, in conjunction with some functional form restrictions, we show that the conditional mean potential outcome can be nonparametrically identified. We also consider a more empirically tractable semiparametric model and develop a three-step estimation procedure for this model. The consistency and asymptotic normality of the proposed estimator are established under certain regularity conditions. As an empirical illustration, we investigate the causal effect of the regional unemployment rate on the crime rate using Japanese city data. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.15114&r= |
| By: | Sven Otto; Nazarii Salish |
| Abstract: | A functional dynamic factor model for time-dependent functional data is proposed. We decompose a functional time series into a predictive low-dimensional common component consisting of a finite number of factors and an infinite-dimensional idiosyncratic component that has no predictive power. The conditions under which all model parameters, including the number of factors, become identifiable are discussed. Our identification results lead to a simple-to-use two-stage estimation procedure based on functional principal components. As part of our estimation procedure, we solve the separation problem between the common and idiosyncratic functional components. In particular, we obtain a consistent information criterion that provides joint estimates of the number of factors and dynamic lags of the common component. Finally, we illustrate the applicability of our method in a simulation study and to the problem of modeling and predicting yield curves. In an out-of-sample experiment, we demonstrate that our model performs well compared to the widely used term structure Nelson-Siegel model for yield curves. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.02532&r= |
| By: | Hao Dong; Taisuke Otsu; Luke Taylor |
| Abstract: | We propose two novel bandwidth selection procedures for the nonparametric regression model with classical measurement error in the regressors. Each method is based on evaluating the prediction errors of the regression using a second (density) deconvolution. The first approach uses a typical leave-one-out cross validation criterion, while the second applies a bootstrap approach and the concept of out-of-bag prediction. We show the asymptotic validity of both procedures and compare them to the SIMEX method of Delaigle and Hall (2008) in a Monte Carlo study. As well as enjoying advantages in terms of computational cost, the methods proposed in this paper lead to lower mean integrated squared error compared to the current state-of-the-art. |
| Keywords: | bandwidth selection, measurement error, bootstrap |
| JEL: | C14 |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:cep:stiecm:620&r= |
| By: | Loria, Francesca; Matthes, Christian; Wang, Mu-Chun |
| Abstract: | Economic theories are often encoded in equilibrium models that cannot be directly estimated because they lack features that, while inessential to the theoretical mechanism that is central to the specific theory, would be essential to fit the data well. We propose an econometric approach that confronts such theories with data through the lens of a time series model that is a good description of macroeconomic reality. Our approach explicitly acknowledges misspecificationas well as measurement error. We highlight in two applications that household heterogeneity greatly helps to fit aggregate data, independently of whether or not nominal rigidities are considered. |
| Keywords: | Bayesian Inference,Misspecification,Heterogeneity,VAR,DSGE |
| JEL: | C32 C50 E30 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdps:562021&r= |
| By: | Rituparna Sen; Anandamayee Majumdar; Shubhangi Sikaria |
| Abstract: | We develop a multivariate functional autoregressive model (MFAR), which captures the cross-correlation among multiple functional time series and thus improves forecast accuracy. We estimate the parameters under the Bayesian dynamic linear models (DLM) framework. In order to capture Granger causality from one FAR series to another we employ Bayes Factor. Motivated by the broad application of functional data in finance, we investigate the causality between the yield curves of two countries. Furthermore, we illustrate a climatology example, examining whether the weather conditions Granger cause pollutant daily levels in a city. |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.15315&r= |
| By: | Mike G. Tsionas (School of Economics and Centre for Efficiency and Productivity Analysis (CEPA) at The University of Queensland, Australia); Valentin Zelenyuk (School of Economics and Centre for Efficiency and Productivity Analysis (CEPA) at The University of Queensland, Australia) |
| Abstract: | The purpose of this paper is to develop formal tests for cost / profitt rationalization of observed data sets under measurement errors in both prices and quantities. The new techniques are based on new statistical formulations for inequalities that describe cost and pro t rationalizability, developed in a Bayesian framework. The new likelihood-based methods of inference are introduced and then illustrated using a data set of large U.S. banks. We also develop various robustness checks, including a normal and lognormal speci cation of the data generating process, as well as a multivariate mixture-of-normal-distributions. |
| Keywords: | Cost Minimization; Profit Maximization; Likelihood-based methods; Markov Chain Monte Carlo; Banking. |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:qld:uqcepa:173&r= |
| By: | Jack Britton (Institute for Fiscal Studies); Ben Waltmann (Institute for Fiscal Studies and IFS) |
| Abstract: | The ‘curse of dimensionality’ is a common problem in the estimation of dynamic models: as models get more complex, the computational cost of solving these models rises exponentially. Keane and Wolpin (1994) proposed a method for addressing this problem in finite-horizon dynamic discrete choice models by evaluating only a subset of state space points by Monte Carlo integration and interpolating the value of the remainder. This method was widely used in the late 1990s and 2000s but has rarely been used since, as it was found to be unreliable in some settings. In this paper, we develop an improved version of their method that relies on three amendments: systematic sampling, data-guided selection of state space points for Monte Carlo integration, and dispensing with polynomial interpolation when a multicollinearity problem is detected. With these improvements, the Keane and Wolpin (1994) method achieves excellent approximation performance even in a model with a large state space and substantial ex ante heterogeneity. |
| Date: | 2021–05–28 |
| URL: | http://d.repec.org/n?u=RePEc:ifs:ifsewp:21/13&r= |
| By: | Traoré, Fousseini; Diop, Insa |
| Abstract: | Since the seminal paper by Granger and Newbold (1974) on spurious regressions, applied econometricians have become aware of the consequences of unit roots in empirical analysis with time series data. Yet one can still find many published papers with unit root tests implemented in an inappropriate way. The objective of this Technical Note is to highlight the common pitfalls and best practices when testing for unit roots. In addition to the theoretical discussion, we provide examples using price data from Kenya, Mali, Togo, and South Africa to illustrate the procedures we think are worth following. |
| Keywords: | KENYA; MALI; TOGO; SOUTH AFRICA; AFRICA; AFRICA SOUTH OF SAHARA; CENTRAL AFRICA; EAST AFRICA; NORTH AFRICA; SOUTHERN AFRICA; WEST AFRICA; econometrics; parity; approaches; best practices; macroeconomics; tests; models; unit root; stationary tests |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:fpr:agrotn:tn-23&r= |
| By: | Margherita Fort; Andrea Ichino; Enrico Rettore; Giulio Zanella |
| Abstract: | In RD designs with multiple cutoffs, the identification of an average causal effect across cutoffs may be problematic if a marginally exposed subject is located exactly at each cutoff. This occurs whenever a fixed number of treatment slots is allocated starting from the subject with the highest (or lowest) value of the score, until exhaustion. Exploiting the “within” variability at each cutoff is the safest and likely efficient option. Alternative strategies exist, but they do not always guarantee identification of a meaningful causal effect and are less precise. To illustrate our findings, we revisit the study of Pop-Eleches and Urquiola (2013). |
| Keywords: | Regression Discontinuity, Multiple Cutoffs, Normalizing and Pooling |
| JEL: | C01 |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:fbk:wpaper:2022-01&r= |
| By: | Kuha, Jouni; Bukodi, Erzsébet; Goldthorpe, John H. |
| Abstract: | We analyse levels and trends of intergenerational social class mobility among three post-war birth cohorts in Britain, and examine how much of the observed mobility or immobility in them could be accounted for by existing differences in educational attainment between people from different class backgrounds. We propose for this purpose a method which quantifies associations between categorical variables when we compare groups which differ only in the distribution of a mediating variable such as education. This is analogous to estimation of indirect effects in causal mediation analysis, but is here developed to define and estimate population associations of variables. We propose estimators for these associations, which depend only on fitted values from models for the mediator and outcome variables, and variance estimators for them. The analysis shows that the part that differences in education play in intergenerational class mobility is by no means so dominant as has been supposed, and that while it varies with gender and with particular mobility transitions, it shows no tendency to change over time. |
| Keywords: | categorical data analysis; finite-population estimation; multinomial logistic models; path analysis; ES/I038187/1 |
| JEL: | C1 |
| Date: | 2021–12–21 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:110157&r= |
| By: | Farshad Noravesh; Hamid Boustanifar |
| Abstract: | The problem that is solved in this paper is known as index tracking. The method of Lasso is used to reduce the dimensions of S&P500 index which has many applications in both investment and portfolio management algorithms. The novelty of this paper is that post-selection inference is used to have better modeling and inference for Lasso approach to index tracking. Both confidence intervals and curves indicate that the performance of Lasso type method for dimension reduction of S&P500 is remarkably high. Keywords: index tracking, lasso, post-selection inference, S&P500 |
| Date: | 2021–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.15448&r= |
| By: | Valérie Lechene (Institute for Fiscal Studies and University College London); Krishna Pendakur (Institute for Fiscal Studies and Simon Fraser University); Alexander Wolf (Institute for Fiscal Studies and ECARES) |
| Abstract: | We provide a method to estimate resource shares—the fraction of total household expenditure allocated to each household member—using OLS estimation of Engel curves. The method is a linear reframing of the nonlinear model of Dunbar, Lewbel and Pendakur (2013), extended to allow single-parent and other complex households, scale economies in assignable goods and complementarities between non assignable goods, and supplemented with a linear identification test. We apply the model to data from 12 countries, and investigate resource shares, gender gaps, and poverty at the individual level. We reject equal sharing, and find large gender gaps in resource shares, and consequently in poverty rates, in some countries. |
| Date: | 2021–06–30 |
| URL: | http://d.repec.org/n?u=RePEc:ifs:ifsewp:21/19&r= |
| By: | Mei-Ling Cai; Zhang-HangJian Chen; Sai-Ping Li; Xiong Xiong; Wei Zhang; Ming-Yuan Yang; Fei Ren |
| Abstract: | In this paper, we propose a new dynamical model to study the two-stage volatility evolution of stock market index after extreme events, and find that the volatility after extreme events follows a stretched exponential decay in the initial stage and becomes a power law decay at later times by using high frequency minute data. Empirical study of the evolutionary behaviors of volatility after endogenous and exogenous events further demonstrates the descriptive power of our new model. To further explore the underlying mechanisms of volatility evolution, we introduce the sequential arrival of information hypothesis (SAIH) and the mixture of distribution hypothesis (MDH) to test the two-stage assumption, and find that investors transform from the uninformed state to the informed state in the first stage and informed investors subsequently dominate in the second stage. The testing results offer a supporting explanation for the validity of our new model and the fitted values of relevant parameters. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.03213&r= |
| By: | Lhuissier Stéphane |
| Abstract: | Motivated by empirically characterizing the relationship between financial conditions and downside macroeconomic risks in the euro area, I develop a regime-switching skew-normal model with time-varying probabilities of transitions. Using Bayesian methods, the model estimates show that a strong cyclical pattern emerges from the conditional skewness (a measure of the asymmetry of the predictive distribution), which has a tendency to rapidly decline to negative territory prior and during recessions. However, the inclusion of financial-specific information in time-varying probabilities does not help to anticipate such skewness nor more generally to provide advance warnings of tail risks. |
| Keywords: | Financial Conditions, Downside Risks, Predictability, Regime-Switching Models |
| JEL: | C11 C2 E32 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:bfr:banfra:863&r= |