ASYMPTOTICALLY EFFICIENT MODEL SELECTION FOR PANEL DATA FORECASTING

2018 ◽  
Vol 35 (4) ◽  
pp. 842-899 ◽  
Author(s):  
Ryan Greenaway-McGrevy
Keyword(s):  
Panel Data ◽  
Model Selection ◽  
Least Squares ◽  
Cross Sections ◽  
Fixed Effects ◽  
Selection Criterion ◽  
Vector Autoregressions ◽  
Cross Sectional ◽  
Time Series Approach ◽  
Asymptotically Efficient

This article develops new model selection methods for forecasting panel data using a set of least squares (LS) vector autoregressions. Model selection is based on minimizing the estimated quadratic forecast risk among candidate models. We provide conditions under which the selection criterion is asymptotically efficient in the sense of Shibata (1980) as n (cross sections) and T (time series) approach infinity. Relative to extant selection criteria, this criterion places a heavier penalty on model dimensionality in order to account for the effects of parameterized forms of cross sectional heterogeneity (such as fixed effects) on forecast loss. We also extend the analysis to bias-corrected least squares, showing that significant reductions in forecast risk can be achieved.

scholarly journals A homogeneous approach to testing for Granger non-causality in heterogeneous panels

2020 ◽  
Author(s):  
Artūras Juodis ◽  
Yiannis Karavias ◽  
Vasilis Sarafidis
Keyword(s):  
Panel Data ◽  
Cross Sections ◽  
Fixed Effects ◽  
Wald Test ◽  
Panel Data Models ◽  
Finite Sample ◽  
Cross Sectional ◽  
Data Set ◽  
Jackknife Method ◽  
Heterogeneous Panels

AbstractThis paper develops a new method for testing for Granger non-causality in panel data models with large cross-sectional (N) and time series (T) dimensions. The method is valid in models with homogeneous or heterogeneous coefficients. The novelty of the proposed approach lies in the fact that under the null hypothesis, the Granger-causation parameters are all equal to zero, and thus they are homogeneous. Therefore, we put forward a pooled least-squares (fixed effects type) estimator for these parameters only. Pooling over cross sections guarantees that the estimator has a $$\sqrt{NT}$$ NT convergence rate. In order to account for the well-known “Nickell bias”, the approach makes use of the well-known Split Panel Jackknife method. Subsequently, a Wald test is proposed, which is based on the bias-corrected estimator. Finite-sample evidence shows that the resulting approach performs well in a variety of settings and outperforms existing procedures. Using a panel data set of 350 U.S. banks observed during 56 quarters, we test for Granger non-causality between banks’ profitability and cost efficiency.

scholarly journals Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 56-90
Author(s):  
Monica Defend ◽  
Aleksey Min ◽  
Lorenzo Portelli ◽  
Franz Ramsauer ◽  
Francesco Sandrini ◽  
...  
Keyword(s):  
Panel Data ◽  
Model Selection ◽  
Estimation Method ◽  
Selection Criterion ◽  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Mixed Frequency ◽  
Model Selection Criterion ◽  
Mixed Frequency Data

This article considers the estimation of Approximate Dynamic Factor Models with homoscedastic, cross-sectionally correlated errors for incomplete panel data. In contrast to existing estimation approaches, the presented estimation method comprises two expectation-maximization algorithms and uses conditional factor moments in closed form. To determine the unknown factor dimension and autoregressive order, we propose a two-step information-based model selection criterion. The performance of our estimation procedure and the model selection criterion is investigated within a Monte Carlo study. Finally, we apply the Approximate Dynamic Factor Model to real-economy vintage data to support investment decisions and risk management. For this purpose, an autoregressive model with the estimated factor span of the mixed-frequency data as exogenous variables maps the behavior of weekly S&P500 log-returns. We detect the main drivers of the index development and define two dynamic trading strategies resulting from prediction intervals for the subsequent returns.

Limitations of Fixed-Effects Models for Panel Data

2019 ◽  
Vol 63 (3) ◽  
pp. 357-369 ◽  
Author(s):  
Terrence D. Hill ◽  
Andrew P. Davis ◽  
J. Micah Roos ◽  
Michael T. French
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Statistical Power ◽  
Unobserved Heterogeneity ◽  
Critical Discussion ◽  
Causal Inferences ◽  
Cross Sectional ◽  
Fixed Effects Models ◽  
Time Invariance ◽  
Type Ii Errors

Although fixed-effects models for panel data are now widely recognized as powerful tools for longitudinal data analysis, the limitations of these models are not well known. We provide a critical discussion of 12 limitations, including a culture of omission, low statistical power, limited external validity, restricted time periods, measurement error, time invariance, undefined variables, unobserved heterogeneity, erroneous causal inferences, imprecise interpretations of coefficients, imprudent comparisons with cross-sectional models, and questionable contributions vis-à-vis previous work. Instead of discouraging the use of fixed-effects models, we encourage more critical applications of this rigorous and promising methodology. The most important deficiencies—Type II errors, biased coefficients and imprecise standard errors, misleading p values, misguided causal claims, and various theoretical concerns—should be weighed against the likely presence of unobserved heterogeneity in other regression models. Ultimately, we must do a better job of communicating the pitfalls of fixed-effects models to our colleagues and students.

scholarly journals Bias correction methods for dynamic panel data models with fixed effects

2017 ◽  
Vol 6 (2) ◽  
pp. 58
Author(s):  
Mohamed Abonazel
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Estimation Methods ◽  
Time Dimension ◽  
Dynamic Panel ◽  
Cross Sectional ◽  
Cross Sectional Dimension ◽  
Ls Estimator

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally; we display new estimators that presented by Youssef and Abonazel [40] as more efficient estimators than the conventional estimators.

scholarly journals Let’s Talk About Fixed Effects: Let’s Talk About All the Good Things and the Bad Things

2020 ◽  
Vol 72 (2) ◽  
pp. 289-299
Author(s):  
Matthias Collischon ◽  
Andreas Eberl
Keyword(s):  
Panel Data ◽  
Least Squares ◽  
Fixed Effects ◽  
Regression Models ◽  
Ordinary Least Squares

Abstract With the broader availability of panel data, fixed effects (FE) regression models are becoming increasingly important in sociology. However, in some studies the potential pitfalls of these models may be ignored, and common critiques of FE models may not always be applicable in comparison to other methods. This article provides an overview of linear FE models and their pitfalls for applied researchers. Throughout the article, we contrast FE and classical pooled ordinary least squares (OLS) models. We argue that in most cases FE models are at least as good as pooled OLS models. Therefore, we encourage scholars to use FE models if possible. Nevertheless, the limitations of FE models should be known and considered.

PANEL DATA MODELS WITH FINITE NUMBER OF MULTIPLE EQUILIBRIA

2009 ◽  
Vol 26 (3) ◽  
pp. 863-881 ◽  
Author(s):  
Jinyong Hahn ◽  
Hyungsik Roger Moon
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Multiple Equilibria ◽  
Finite Sample ◽  
Finite Support ◽  
Time Dimension ◽  
Cross Sectional ◽  
Incidental Parameters ◽  
Cross Sectional Dimension ◽  
Fixed Effects Estimator

We study a nonlinear panel data model in which the fixed effects are assumed to have finite support. The fixed effects estimator is known to have the incidental parameters problem. We contribute to the literature by making a qualitative observation that the incidental parameters problem in this model may not be not as severe as in the conventional case. Because fixed effects have finite support, the probability of correctly identifying the fixed effect converges to one even when the cross sectional dimension grows as fast as some exponential function of the time dimension. As a consequence, the finite sample bias of the fixed effects estimator is expected to be small.

scholarly journals Getting the Within Estimator of Cross-Level Interactions in Multilevel Models with Pooled Cross-Sections: Why Country Dummies (Sometimes) Do Not Do the Job

2018 ◽  
Vol 49 (1) ◽  
pp. 190-219 ◽  
Author(s):  
Marco Giesselmann ◽  
Alexander W. Schmidt-Catran
Keyword(s):  
Cross Sections ◽  
Fixed Effects ◽  
Multilevel Models ◽  
Unobserved Heterogeneity ◽  
Level Structure ◽  
Cross Sectional Survey ◽  
Social Scientists ◽  
Cross Sectional ◽  
Country Level ◽  
Fixed Effects Estimator

Multilevel models with persons nested in countries are increasingly popular in cross-country research. Recently, social scientists have started to analyze data with a three-level structure: persons at level 1, nested in year-specific country samples at level 2, nested in countries at level 3. By using a country fixed-effects estimator, or an alternative equivalent specification in a random-effects framework, this structure is increasingly used to estimate within-country effects in order to control for unobserved heterogeneity. For the main effects of country-level characteristics, such estimators have been shown to have desirable statistical properties. However, estimators of cross-level interactions in these models are not exhibiting these attractive properties: as algebraic transformations show, they are not independent of between-country variation and thus carry country-specific heterogeneity. Monte Carlo experiments consistently reveal the standard approaches to within estimation to provide biased estimates of cross-level interactions in the presence of an unobserved correlated moderator at the country level. To obtain an unbiased within-country estimator of a cross-level interaction, effect heterogeneity must be systematically controlled. By replicating a published analysis, we demonstrate the relevance of this extended country fixed-effects estimator in research practice. The intent of this article is to provide advice for multilevel practitioners, who will be increasingly confronted with the availability of pooled cross-sectional survey data.

An empirical analysis of the determinants of the cash conversion cycle in Kenyan listed non-financial firms

2014 ◽  
Vol 4 (2) ◽  
pp. 175-196 ◽  
Author(s):  
David Mutua Mathuva
Keyword(s):  
Least Squares ◽  
Fixed Effects ◽  
Capital Expenditure ◽  
Ordinary Least Squares ◽  
Return On Assets ◽  
Cross Sectional ◽  
Content Type ◽  
Internal Resources ◽  
Financial Firms ◽  
Cash Conversion Cycle

Purpose – The purpose of this paper is to investigate whether non-financial firms listed on the Nairobi Securities Exchange (NSE) exhibit a target cash conversion cycle (CCC). The study also examines the speed of adjustment to the target CCC and the factors that influence corporate decisions on the optimum length of the CCC. Design/methodology/approach – Based on a sample of 33 publicly traded firms on the NSE for the period between 1993 and 2008, cross-sectional and time series analyses were carried out on the data comprising 468 firm-years. A target adjustment model was developed to examine the significant determinants of the CCC. Various regression approaches including ordinary least squares, fixed effects and two-stage least squares estimation models were used in data analysis. Findings – The results, which are robust for endogeneity, show that non-financial firms listed on the NSE maintain a target CCC. Further analysis reveals that these firms adjust to the target CCC at a slower rate. The results show that the determinants of the CCC include both firm-specific and economy-wide factors. Specifically, the study establishes that older firms and firms with more internal resources maintain longer CCC. Moreover higher return on assets, investment in capital expenditure and growth opportunities have a significant negative association with the CCC. The results also show a significant positive relation between inflation and the CCC. Practical implications – The study establishes that other than internal firm-specific factors, the CCC is also influenced by inflation, which is an external, economy-wide factor. Originality/value – To the best of the author's knowledge, this is the first study to examine whether listed non-financial firms in a frontier market maintain a target CCC.

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