Inference in Approximately Sparse Correlated Random Effects Probit Models With Panel Data

This paper considers the problem of forecasting a collection of short time series using cross‐sectional information in panel data. We construct point predictors using Tweedie's formula for the posterior mean of heterogeneous coefficients under a correlated random effects distribution. This formula utilizes cross‐sectional information to transform the unit‐specific (quasi) maximum likelihood estimator into an approximation of the posterior mean under a prior distribution that equals the population distribution of the random coefficients. We show that the risk of a predictor based on a nonparametric kernel estimate of the Tweedie correction is asymptotically equivalent to the risk of a predictor that treats the correlated random effects distribution as known (ratio optimality). Our empirical Bayes predictor performs well compared to various competitors in a Monte Carlo study. In an empirical application, we use the predictor to forecast revenues for a large panel of bank holding companies and compare forecasts that condition on actual and severely adverse macroeconomic conditions.

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Partial effects in non-linear panel data models with correlated random effects

Econometrics Journal ◽

10.1093/ectj/utab004 ◽

2021 ◽

Author(s):

Jason Abrevaya ◽

Yu-Chin Hsu

Keyword(s):

Panel Data ◽

Random Effects ◽

Unobserved Heterogeneity ◽

Data Models ◽

Panel Data Models ◽

New Concepts ◽

Correlated Random Effects ◽

Non Linear ◽

Partial Effects

Summary Nonlinearity and heterogeneity are known to cause difficulties in estimating and interpreting partial effects. This paper provides a systematic characterization of the various partial effects in nonlinear panel data models that might be of interest to empirical researchers. The interpretation of the partial effects depends upon (i) whether the distribution of unobserved heterogeneity is treated as fixed or allowed to vary with covariates, and (ii) whether one is interested in particular covariate values or an average over such values. The characterization covers partial-effects concepts already in the literature but also includes new concepts for partial effects. A simple panel probit design highlights that the different partial effects can be quantitatively very different.

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FACTORS INFLUENCING DIVIDEND PAYOUT DECISION OF FINANCIAL AND NON-FINANCIAL COMPANIES LISTED ON NAIROBI SECURITIES EXCHANGE

American Journal of Finance ◽

10.47672/ajf.116 ◽

2017 ◽

Vol 1 (1) ◽

pp. 16

Author(s):

Samuel Thuo Gakumo ◽

Dr. C. Nanjala

Keyword(s):

Panel Data ◽

Random Effects ◽

Business Risk ◽

Financial Leverage ◽

Dividend Payout ◽

Financial Firms ◽

Earnings Per Share ◽

Factors Influencing ◽

Probit Models

Purpose: The purpose of this study was to examine the factors influencing dividend payout decision of financial and non-financial companies listed on Nairobi securities exchange.Methodology: The study conducted a census on 33 financial and non-financial firms listed on the NSE consistently since 2003 to 2012.Panel data was analyzed using random effects Tobit and random affects probit models. The findings indicated that four variables; financial leverage, business risk, profitability and Earnings per share significantly influenced the amount of dividend paid.Results: The findings indicated that four variables; financial leverage, business risk, profitability and Earnings per share significantly influenced the amount of dividend paid. Based on the findings, the study concluded that EPS, financial leverage and business risk play a key role in making the decision to pay or not to pay dividends. Earnings per share influences the decision to pay positively while both financial leverage and business risk influences the decision to pay dividends negatively.Unique contribution to theory, practice and policy: The study also recommends that managers may use the study findings when making the dividend payout policies since they will be given useful information regarding which factors they may consider when determining the dividend payouts

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