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Displaying results 1 to 22 of 22.

  1. Coresets for regressions with panel data
    Author: Huang, Lingxiao; Sudhir, K.; Vishnoi, Nisheeth K.
    Published: 2021
    Publisher:  Cowles Foundation for Research in Economics, Yale University, New Haven, Connecticut

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    Series: Cowles Foundation discussion paper ; no. 2309 (November 2021)
    Subjects: Panel; Regressionsanalyse; Nichtlineare Optimierung; Theorie
    Scope: 1 Online-Ressource (circa 28 Seiten), Illustrationen
  2. Short-term Covid-19 forecast for latecomers
    Author: Medeiros, Marcelo C.; Street, Alexandre; Valladão, Davi; Vasconcelos, Gabriel; Zilberman, Eduardo
    Published: [2021]
    Publisher:  Departamento de Economia, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ

    The number of Covid-19 cases is increasing dramatically worldwide, with several countries experiencing a second and worse wave. Therefore, the availability of reliable forecasts for the number of cases and deaths in the coming days is of fundamental... more

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    The number of Covid-19 cases is increasing dramatically worldwide, with several countries experiencing a second and worse wave. Therefore, the availability of reliable forecasts for the number of cases and deaths in the coming days is of fundamental importance. We propose a simple statistical method for short-term real-time forecasting of the number of Covid-19 cases and fatalities in countries that are latecomers i.e., countries where cases of the disease started to appear some time after others. In particular, we propose a penalized (LASSO) regression with an error correction mechanism to construct a model of a latecomer in terms of the other countries that were at a similar stage of the pandemic some days before. By tracking the number of cases in those countries, we forecast through an adaptive rolling-window scheme the number of cases and deaths in the latecomer. We apply this methodology to four different countries: Brazil, Chile, Mexico, and Portugal. We show that the methodology performs very well. These forecasts aim to foster a better short-run management of the health system capacity and can be applied not only to countries but to different regions within a country, as well.

     
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    Series: Texto para discussão / PUC Rio, Departamento de Economia ; no. 670
    Subjects: Coronavirus; Prognoseverfahren; Regressionsanalyse; Kointegration; Sterblichkeit; Theorie; Brasilien
    Scope: 1 Online-Ressource (circa 31 Seiten), Illustrationen
  3. Informational Content of Factor Structures in Simultaneous Binary Response Models
    Author: Khan, Shakeeb; Maurel, Arnaud; Zhang, Yichong
    Published: 2021
    Publisher:  National Bureau of Economic Research, Cambridge, Mass

    We study the informational content of factor structures in discrete triangular systems. Factor structures have been employed in a variety of settings in cross sectional and panel data models, and in this paper we formally quantify their identifying... more

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    We study the informational content of factor structures in discrete triangular systems. Factor structures have been employed in a variety of settings in cross sectional and panel data models, and in this paper we formally quantify their identifying power in a bivariate system often employed in the treatment effects literature. Our main findings are that imposing a factor structure yields point identification of parameters of interest, such as the coefficient associated with the endogenous regressor in the outcome equation, under weaker assumptions than usually required in these models. In particular, we show that a non-standard exclusion restriction that requires an explanatory variable in the outcome equation to be excluded from the treatment equation is no longer necessary for identification, even in cases where all of the regressors from the outcome equation are discrete. We also establish identification of the coefficient of the endogenous regressor in models with more general factor structures, in situations where one has access to at least two continuous measurements of the common factor

     
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    Series: NBER working paper series ; no. w28327
    Subjects: Nichtparametrische Schätzung; Einzelgleichungsmodell; Regressionsanalyse; Clusteranalyse
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  4. Housing Precarity & the COVID-19 Pandemic
    Impacts of Utility Disconnection and Eviction Moratoria on Infections and Deaths Across US Counties
    Author: Jowers, Kay; Timmins, Christopher; Bhavsar, Nrupen; Marshall, Julia
    Published: 2021
    Publisher:  National Bureau of Economic Research, Cambridge, Mass

    The COVID-19 pandemic has necessitated the adoption of a number of policies that aim to reduce the spread of the disease by promoting housing stability. Housing precarity, which includes both the risk of eviction and utility disconnections or... more

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    The COVID-19 pandemic has necessitated the adoption of a number of policies that aim to reduce the spread of the disease by promoting housing stability. Housing precarity, which includes both the risk of eviction and utility disconnections or shut-offs, reduces a person's ability to abide by social distancing orders and comply with hygiene recommendations. Our analysis quantifies the impact of these various economic policies on COVID-19 infection and death rates using panel regression techniques to control for a variety of potential confounders. We find that policies that limit evictions are found to reduce COVID-19 infections by 3.8% and reduce deaths by 11%. Moratoria on utility disconnections reduce COVID-19 infections by 4.4% and mortality rates by 7.4%. Had such policies been in place across all counties (i.e., adopted as federal policy) from early March 2020 through the end of November 2020, our estimated counterfactuals show that policies that limit evictions could have reduced COVID-19 infections by 14.2% and deaths by 40.7%. For moratoria on utility disconnections, COVID-19 infections rates could have been reduced by 8.7% and deaths by 14.8%. Housing precarity policies that prevent eviction and utility disconnections have been effective mechanisms for decreasing both COVID-19 infections and deaths

     
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    Series: NBER working paper series ; no. w28394
    Subjects: Wohnung; Coronavirus; Infektionskrankheit; Infektionsschutz; Armut; Armutsbekämpfung; Panel; Regressionsanalyse; USA
    Scope: 1 Online-Ressource, illustrations (black and white)
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  5. Extremile regression
    Author: Daouia, Abdelaati; Gijbels, Irène; Stupfler, Gilles
    Published: January 2021
    Publisher:  Toulouse School of Economics, [Toulouse]

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    Series: Working papers / Toulouse School of Economics ; no 1176
    Subjects: Regressionsanalyse; Schätztheorie; Asymmetric least squares; Extremes; Heavy tails; Regression extremiles; Regression quantiles; Tail index
    Scope: 1 Online-Ressource (circa 33 Seiten), Illustrationen
  6. The Effects of Career and Technical Education
    Evidence from the Connecticut Technical High School System
    Author: Brunner, Eric J.; Dougherty, Shaun; Ross, Stephen L.
    Published: 2021
    Publisher:  National Bureau of Economic Research, Cambridge, Mass

    We examine the effect of attending stand-alone technical high schools on student short- and long-term outcomes using a regression discontinuity design. Male students are 10 percentage points more likely to graduate from high school and have half a... more

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    We examine the effect of attending stand-alone technical high schools on student short- and long-term outcomes using a regression discontinuity design. Male students are 10 percentage points more likely to graduate from high school and have half a semester less time enrolled in college, although effects on college fade-out. Male students have 32% higher quarterly earnings. Earnings effects may in part reflect general skills: male students have higher attendance rates and test scores, and industry fixed effects explain less than 1/3rd of earnings gains. We find little evidence that attending a technical high school affects the outcomes of female students

     
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    Series: NBER working paper series ; no. w28790
    Subjects: Allgemeinbildende Schule; Bildungsniveau; Technische Berufe; Unterricht; Geschlecht; Regressionsanalyse; Connecticut; USA
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  7. Minimax Risk and Uniform Convergence Rates for Nonparametric Dyadic Regression
    Author: Graham, Bryan S.; Niu, Fengshi; Powell, James
    Published: 2021
    Publisher:  National Bureau of Economic Research, Cambridge, Mass

    We study nonparametric regression in a setting where N(N-1) dyadic outcomes are observed for N randomly sampled units. Outcomes across dyads sharing a unit in common may be dependent (i.e., our dataset exhibits dyadic dependence). We present two sets... more

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    We study nonparametric regression in a setting where N(N-1) dyadic outcomes are observed for N randomly sampled units. Outcomes across dyads sharing a unit in common may be dependent (i.e., our dataset exhibits dyadic dependence). We present two sets of results. First, we calculate lower bounds on the minimax risk for estimating the regression function at (i) a point and (ii) under the infinity norm. Second, we calculate (i) pointwise and (ii) uniform convergence rates for the dyadic analog of the familiar Nadaraya-Watson (NW) kernel regression estimator. We show that the NW kernel regression estimator achieves the optimal rates suggested by our risk bounds when an appropriate bandwidth sequence is chosen. This optimal rate differs from the one available under iid data: the effective sample size is smaller and dimension of the regressor vector influences the rate differently

     
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    Series: NBER working paper series ; no. w28548
    Subjects: Nichtparametrisches Verfahren; Regressionsanalyse; Entscheidung unter Unsicherheit; Schätztheorie
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  8. Synthetic Controls with Staggered Adoption
    Author: Ben-Michael, Eli; Feller, Avi; Rothstein, Jesse
    Published: 2021
    Publisher:  National Bureau of Economic Research, Cambridge, Mass

    Staggered adoption of policies by different units at different times creates promising opportunities for observational causal inference. Estimation remains challenging, however, and common regression methods can give misleading results. A promising... more

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    Staggered adoption of policies by different units at different times creates promising opportunities for observational causal inference. Estimation remains challenging, however, and common regression methods can give misleading results. A promising alternative is the synthetic control method (SCM), which finds a weighted average of control units that closely balances the treated unit's pre-treatment outcomes. In this paper, we generalize SCM, originally designed to study a single treated unit, to the staggered adoption setting. We first bound the error for the average effect and show that it depends on both the imbalance for each treated unit separately and the imbalance for the average of the treated units. We then propose "partially pooled" SCM weights to minimize a weighted combination of these measures; approaches that focus only on balancing one of the two components can lead to bias. We extend this approach to incorporate unit-level intercept shifts and auxiliary covariates. We assess the performance of the proposed method via extensive simulations and apply our results to the question of whether teacher collective bargaining leads to higher school spending, finding minimal impacts. We implement the proposed method in the augsynth R package

     
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    Series: NBER working paper series ; no. w28886
    Subjects: Kausalanalyse; Regressionsanalyse
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  9. Residual based nodewise regression in factor models with ultra-high dimensions: analysis of mean-variance portfolio efficiency and estimation of out-of-sample and constrained maximum Sharpe ratios
    Author: Caner, Mehmet; Medeiros, Marcelo C.; Vasconcelos, Gabriel F. R.
    Published: [2021]
    Publisher:  Departamento de Economia, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, RJ

    We provide a new theory for nodewise regression when the residuals from a fitted factor model are used to apply our results to the analysis of maximum Sharpe ratio when the number of assets in a portfolio is larger than its time span. We introduce a... more

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    We provide a new theory for nodewise regression when the residuals from a fitted factor model are used to apply our results to the analysis of maximum Sharpe ratio when the number of assets in a portfolio is larger than its time span. We introduce a new hybrid model where factor models are combined with feasible nodewise regression. Returns are generated from increasing number of factors plus idiosyncratic components (errors). The precision matrix of the idiosyncratic terms is assumed to be sparse, but the respective covariance matrix can be non-sparse. Since the nodewise regression is not feasible due to unknown nature of errors, we provide a feasible-residual based nodewise regression to estimate the precision matrix of errors, as a new method. Next, we show that the residual-based nodewise regression provides a consistent estimate for the precision matrix of errors. In another new development, we also show that the precision matrix of returns can be estimated consistently, even with increasing number of factors. Benefiting from the consistency of the precision matrix estimate of returns, we show that: (1) the portfolios in high dimensions are mean-variance efficient; (2) maximum out-of-sample Sharpe ratio estimator is consistent and the number of assets slows the convergence up to a logarithmic factor; (3) the maximum Sharpe ratio estimator is consistent when the portfolio weights sum to one; and (4) the Sharpe ratio estimators are consistent in global minimum-variance and mean-variance portfolios.

     
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    Series: Texto para discussão / PUC Rio, Departamento de Economia ; no. 684
    Subjects: Portfolio-Management; Kapitalmarkttheorie; Regressionsanalyse; Schätztheorie
    Scope: 1 Online-Ressource (circa 73 Seiten), Illustrationen
  10. Canadian Start-ups
    growth and scale-up transitions
    Author: Song, May; Bérube, Charles
    Published: 2021
    Publisher:  Innovation, Science and Economic Development Canada, Ottawa, ON

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    ISBN: 9780660353135
    Subjects: Unternehmensgründung; Unternehmenswachstum; Regressionsanalyse; Deskriptive Statistik; Kanada
    Scope: 1 Online-Ressource (circa 25 Seiten), Illustrationen
  11. Consistent Inference for Predictive Regressions in Persistent Economic Systems
    Author: Andersen, Torben; Varneskov, Rasmus Tangsgaard
    Published: 2021
    Publisher:  National Bureau of Economic Research, Cambridge, Mass

    We study standard predictive regressions in economic systems governed by persistent vector autoregressive dynamics for the state variables. In particular, all - or a subset - of the variables may be fractionally integrated, which induces a spurious... more

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    We study standard predictive regressions in economic systems governed by persistent vector autoregressive dynamics for the state variables. In particular, all - or a subset - of the variables may be fractionally integrated, which induces a spurious regression problem. We propose a new inference and testing procedure - the Local speCtruM (LCM) approach - for joint significance of the regressors, that is robust against the variables having different integration orders and remains valid regardless of whether predictors are significant and if they induce cointegration. Specifically, the LCM procedure is based on fractional filtering and band-spectrum regression using a suitably selected set of frequency ordinates. Contrary to existing procedures, we establish a uniform Gaussian limit theory and a standard χ2-distributed test statistic. Using LCM inference and testing techniques, we explore predictive regressions for the realized return variation. Standard least squares inference indicates that popular financial and macroeconomic variables convey valuable information about future return volatility. In contrast, we find no significant evidence using our robust LCM procedure. If anything, our tests support a reverse chain of causality: rising financial volatility predates adverse innovations to macroeconomic variables. Simulations illustrate the relevance of the theoretical arguments for finite-sample inference

     
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    Series: NBER working paper series ; no. w28568
    Subjects: Prognoseverfahren; Finanzmarkt; Regressionsanalyse; Schätztheorie; VAR-Modell
    Scope: 1 Online-Ressource, illustrations (black and white)
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  12. Consistent Local Spectrum (LCM) Inference for Predictive Return Regressions
    Author: Andersen, Torben; Varneskov, Rasmus Tangsgaard
    Published: 2021
    Publisher:  National Bureau of Economic Research, Cambridge, Mass

    This paper studies the properties of predictive regressions for asset returns in economic systems governed by persistent vector autoregressive dynamics. In particular, we allow for the state variables to be fractionally integrated, potentially of... more

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    This paper studies the properties of predictive regressions for asset returns in economic systems governed by persistent vector autoregressive dynamics. In particular, we allow for the state variables to be fractionally integrated, potentially of different orders, and for the returns to have a latent persistent conditional mean, whose memory is difficult to estimate consistently by standard techniques in finite samples. Moreover, the predictors may be endogenous and "imperfect". In this setting, we provide a cointegration rank test to determine the predictive model framework as well as the latent persistence of returns. This motivates a rank-augmented Local Spectrum (LCM) procedure, which is consistent and delivers asymptotic Gaussian inference. Simulations illustrate the theoretical arguments. Finally, in an empirical application concerning monthly S&P 500 return prediction, we provide evidence for a fractionally integrated conditional mean component. Moreover, using the rank-augmented LCM procedure, we document significant predictive power for key state variables such as the price-earnings ratio and the default spread

     
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    Series: NBER working paper series ; no. w28569
    Subjects: Aktienindex; Prognoseverfahren; Regressionsanalyse
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  13. The Causal Effect of Depression and Anxiety on Life Satisfaction
    An Instrumental Variable Approach
    Author: Buason, Arnar; Norton, Edward C.; McNamee, Paul; Edda Bjork Thordardottir; Tinna Laufey Ásgeirsdóttir
    Published: 2021
    Publisher:  National Bureau of Economic Research, Cambridge, Mass

    Within the vast body of literature on the relationship between mental disorders and life satisfaction, no satisfactory treatment has been proposed to deal with the bi-directional relationship between the two. We estimate the causal effect of... more

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    Within the vast body of literature on the relationship between mental disorders and life satisfaction, no satisfactory treatment has been proposed to deal with the bi-directional relationship between the two. We estimate the causal effect of depression and anxiety on life satisfaction by applying an instrumental-variable regression approach to the Household, Income and Labour Dynamics in Australia (HILDA) survey. Our identification strategy exploits regional variation in the tendency to diagnose depression and anxiety, while also using an individual-level panel-data method. Our results show that previous research seriously overestimates the effect of depression and anxiety on life satisfaction. The most comparable estimate from previous research is over five times the size of our estimate. Furthermore, those papers that use such estimates to measure the monetary value of not suffering from depression or anxiety find it to be between $14 to $600 million a year per individual, compared to our estimate of around $60 thousand. Another source of bias which further inflates previous monetary estimates is the endogeneity of income. We account for this issue by using irregular sources of income, such as lottery winnings, instead of regular household income

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
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    Series: NBER working paper series ; no. w28575
    Subjects: Psychische Krankheit; Depressive Störung; Lebensqualität; Regressionsanalyse; Schätzung; Australien
    Scope: 1 Online-Ressource, illustrations (black and white)
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    Hardcopy version available to institutional subscribers
  14. Public Health Policy At Scale
    Impact of a Government-sponsored Information Campaign on Infant Mortality in Denmark
    Author: Altındaǧ, Onur; Greve, Jane; Tekin, Erdal
    Published: 2021
    Publisher:  National Bureau of Economic Research, Cambridge, Mass

    We evaluate the impact of a nationwide public health intervention on deaths from sudden infant death syndrome (SIDS), using population data from Denmark in a regression discontinuity research design. The information campaign-implemented primarily... more

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    We evaluate the impact of a nationwide public health intervention on deaths from sudden infant death syndrome (SIDS), using population data from Denmark in a regression discontinuity research design. The information campaign-implemented primarily through a universal nurse home visiting program-reduced infant mortality by 17.6 percent and saved between 11.5-14.5 lives over 10,000 births. The estimated effect sizes are 11-14 times larger among low birthweight and preterm infants relative to the overall population. Improvement in infant mortality is concentrated among those with lower socio-economic status and with limited access to health information, thereby reducing health inequities at birth in Denmark

     
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    Source: Union catalogues
    Language: English
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    Series: NBER working paper series ; no. w28621
    Subjects: Kindersterblichkeit; Gesundheitspolitik; Regressionsanalyse; Dänemark
    Scope: 1 Online-Ressource, illustrations (black and white)
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    Hardcopy version available to institutional subscribers
  15. Powerful t-tests in the presence of nonclassical measurement error
    Author: Kim, Dongwoo; Wilhelm, Daniel
    Published: [2021]
    Publisher:  Cemmap, Centre for Microdata Methuods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    This paper proposes a powerful alternative to the t-test in linear regressions when a regressor is mismeasured. We assume there is a second contaminated measurement of the regressor of interest. We allow the two measurement errors to be nonclassical... more

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    This paper proposes a powerful alternative to the t-test in linear regressions when a regressor is mismeasured. We assume there is a second contaminated measurement of the regressor of interest. We allow the two measurement errors to be nonclassical in the sense that they may both be correlated with the true regressor, they may be correlated with each other, and we do not require any location normalizations on the measurement errors. We propose a new maximal t-statistic that is formed from the regression of the outcome onto a maximally weighted linear combination of the two measurements. Critical values of the test are easily computed via a multiplier bootstrap. In simulations, we show that this new test can be significantly more powerful than t-statistics based on OLS or IV estimates. Finally, we apply the proposed test to the studies of returns to education based on twins data from the US and the UK. With our maximal t-test, we are able to discover statistically significant returns to education when standard t-tests do not.

     
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    Language: English
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    hdl: 10419/241954
    Series: Cemmap working paper ; CWP21, 18
    Subjects: Statistischer Test; Statistischer Fehler; Regressionsanalyse; Simulation; Bildungsertrag; Großbritannien
    Scope: 1 Online-Ressource (circa 43 Seiten), Illustrationen
  16. Limited self-knowledge and survey response behavior
    Author: Falk, Armin; Neuber, Thomas; Strack, Philippe
    Published: July 2021
    Publisher:  CESifo, Center for Economic Studies & Ifo Institute, Munich, Germany

    We study response behavior in surveys and show how the explanatory power of self-reports can be improved. First, we develop a choice model of survey response behavior under the assumption that the respondent has imperfect self-knowledge about her... more

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    We study response behavior in surveys and show how the explanatory power of self-reports can be improved. First, we develop a choice model of survey response behavior under the assumption that the respondent has imperfect self-knowledge about her individual characteristics. In panel data, the model predicts that the variance in responses for different characteristics increases in self-knowledge and that the variance for a given characteristic over time is non-monotonic in self-knowledge. Importantly, the ratio of these variances identifies an individual’s level of self-knowledge, i.e. the latter can be inferred from observed response patterns. Second, we develop a consistent and unbiased estimator for self-knowledge based on the model. Third, we run an experiment to test the model’s main predictions in a context where the researcher knows the true underlying characteristics. The data confirm the model’s predictions as well as the estimator’s validity. Finally, we turn to a large panel data set, estimate individual levels of self-knowledge, and show that accounting for differences in self-knowledge significantly increases the explanatory power of regression models. Using a median split in self-knowledge and regressing risky behaviors on self-reported risk attitudes, we find that the R2 can be multiple times larger for above- than below-median subjects. Similarly, gender differences in risk attitudes are considerably larger when restricting samples to subjects with high self-knowledge. These examples illustrate how using the estimator may improve inference from survey data.

     
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    hdl: 10419/245360
    Series: CESifo working paper ; no. 9179 (2021)
    Subjects: Befragung; Meinung; Verhalten; Selbstevaluation; Persönlichkeitspsychologie; Regressionsanalyse; Risikopräferenz; Geschlechterunterschiede; Experiment
    Scope: 1 Online-Ressource (circa 60 Seiten), Illustrationen
  17. The elasticity of taxable income: a meta-regression analysis
    Author: Neisser, Carina
    Published: February 2021
    Publisher:  ECONtribute, Bonn

    The elasticity of taxable income (ETI) is a key parameter in tax policy analysis. To examine the large variation found in the literature of taxable and broad income elasticities, I conduct a comprehensive meta-regression analysis using information... more

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    The elasticity of taxable income (ETI) is a key parameter in tax policy analysis. To examine the large variation found in the literature of taxable and broad income elasticities, I conduct a comprehensive meta-regression analysis using information from 61 studies containing 1,720 estimates. My findings reveal that estimated elasticities are not immutable parameters. They are correlated with contextual factors and the choice of the empirical specification influences the estimated elasticities. Finally, selective reporting bias is prevalent, and the direction of bias depends on whether deductions are included in the tax base.

     
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    Language: English
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    hdl: 10419/231492
    Series: ECONtribute discussion paper ; no. 067
    Subjects: Einkommensteuer; Einkommensverteilung; Elastizität; Meta-Analyse; Regressionsanalyse; elasticity of taxable income; income tax; behavioural response; meta-regression analysis
    Scope: 1 Online-Ressource (circa 67 Seiten), Illustrationen
  18. Inference in predictive regression models with persistent regressors
    Author: Hillmann, Benjamin
    Published: 2021

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    Universitätsbibliothek Kiel, Zentralbibliothek
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    TH 13050
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    Universitätsbibliothek Kiel, Zentralbibliothek
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    Source: Union catalogues
    Contributor: Demetrescu, Matei (AkademischeR BetreuerIn)
    Language: English
    Media type: Dissertation
    Format: Print
    Subjects: Kapitalmarktrendite; Prognoseverfahren; Regressionsanalyse; Nichtlineare Regression; Gauß-Prozess; Induktive Statistik; Theorie; Schätzung; USA
    Scope: VIII, 95 Seiten, Illustrationen, Diagramme
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    Kumulatives Verfahren, enthält 1 Zeitschriftenaufsatz

    Dissertation, Christian-Albrechts-Universität zu Kiel, 2021
  19. Linear panel regressions with two-way unobserved heterogeneity
    Author: Freeman, Hugo; Weidner, Martin
    Published: [2021]
    Publisher:  Cemmap, Centre for Microdata Methuods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    This paper studies linear panel regression models in which the unobserved error term is an unknown smooth function of two-way unobserved fixed effects. In standard additive or interactive fixed effect models the individual specific and time specific... more

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    This paper studies linear panel regression models in which the unobserved error term is an unknown smooth function of two-way unobserved fixed effects. In standard additive or interactive fixed effect models the individual specific and time specific effects are assumed to enter with a known functional form (additive or multiplicative), while we allow for this functional form to be more general and unknown. We discuss two different estimation approaches that allow consistent estimation of the regression parameters in this setting as the number of individuals and the number of time periods grow to infinity. The first approach uses the interactive fixed effect estimator in Bai (2009), which is still applicable here, as long as the number of factors in the estimation grows asymptotically. The second approach first discretizes the two-way unobserved heterogeneity (similar to what Bonhomme, Lamadon and Manresa 2021 are doing for one-way heterogeneity) and then estimates a simple linear fixed effect model with additive two-way grouped fixed effects. For both estimation methods we obtain asymptotic convergence results, perform Monte Carlo simulations, and employ the estimators in an empirical application to UK house price data.

     
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    hdl: 10419/246807
    Series: Cemmap working paper ; CWP21, 39
    Subjects: Panel; Regressionsanalyse; Fixed-Effects-Modell; Monte-Carlo-Simulation; Schätztheorie
    Scope: 1 Online-Ressource (circa 47 Seiten)
  20. Sustainable energy deployment in developing countries
    the role of composition of energy aid
    Author: Jain, Panika; Bardhan, Samaresh
    Published: [2021]
    Publisher:  Leibniz Institute for East and Southeast European Studies, IOS, Regensburg

    Increasing the share of renewable energy in the global energy mix requires enormous investments in low-carbon energy infrastructure in developing countries. Energy aid, being an important funding channel, seems to play a pivotal role in augmenting... more

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    Increasing the share of renewable energy in the global energy mix requires enormous investments in low-carbon energy infrastructure in developing countries. Energy aid, being an important funding channel, seems to play a pivotal role in augmenting the shift towards cleaner energy sources. This paper presents a panel analysis on the effectiveness of energy aid and its composition on the substitution potential of renewable sources in the power sector. Our findings based on dynamic panel data and panel quantile regression techniques for a sample of 67 developing countries during 2002-2017, in general, reveal the heterogeneous effects of various components of energy aid on renewable deployment. In particular, energy ODA and its components focus on hydropower infrastructure development without targeting non-hydro renewable sources effectively. Findings also reveal that renewable generation, policy, and distribution components of energy aid help augment the share of hydro sources, irrespective of the renewable energy transition stage. However, we did not find any such effects on non-hydro renewable sources. Probable policy implications call for restructuring the composition and geographical distribution of energy aid to support the development of low-carbon energy infrastructure in the developing world. Donors should disburse energy aid in countries with low renewable shares to attract domestic and international private investments, particularly for increasing the share of non-hydro renewable sources.

     
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    Source: Union catalogues
    Language: English
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    hdl: 10419/243221
    Series: IOS working papers ; no. 394 (October 2021)
    Subjects: Erneuerbare Energien; Wasserkraft; Entwicklungshilfe; Investition; Entwicklungsfinanzierung; Regressionsanalyse; Renewable energy deployment; energy aid; aid effectiveness; developingcountries; System GMM; Panel quantile regression
    Scope: 1 Online-Ressource (circa 47 Seiten), Illustrationen
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    Datei gelöscht auf Wunsch der Autoren
  21. Dynamic relationship between stock and bond returns: a GAS MIDAS copula approach
    Author: Nguyen, Hoang; Javed, Farrukh
    Published: [2021]
    Publisher:  Örebro University School of Business, Örebro, Sweden

    Stock and bond are the two most crucial assets for portfolio allocation and risk management. This study proposes generalized autoregressive score mixed frequency data sampling (GAS MIDAS) copula models to analyze the dynamic dependence between stock... more

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    Stock and bond are the two most crucial assets for portfolio allocation and risk management. This study proposes generalized autoregressive score mixed frequency data sampling (GAS MIDAS) copula models to analyze the dynamic dependence between stock returns and bond returns. A GAS MIDAS copula decomposes their relationship into a short-term dependence and a long-term dependence. While the long-term dependence is driven by related macro-finance factors using a MIDAS regression, the short-term effect follows a GAS process. Asymmetric dependence at different quantiles is also taken into account. We find that the proposed GAS MIDAS copula models are more effective in optimal portfolio allocation and improve the accuracy in risk management compared to other alternatives.

     
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    hdl: 10419/244589
    Series: Array ; 2021, 15
    Subjects: Multivariate Verteilung; Kapitalmarktrendite; Regressionsanalyse; Mixed Data Sampling; GAS copulas; MIDAS; asymmetry
    Scope: 1 Online-Ressource (circa 46 Seiten), Illustrationen
  22. Statistics for linguistics with R
    a practical introduction
    Author: Gries, Stefan Thomas
    Published: [2021]
    Publisher:  de Gruyter Mouton, Berlin

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