Dynamic Panel Data Models With Irregular Spacing: With an Application to Early Childhood Development

Millimet, Daniel L.; McDonough, Ian K.

doi:10.1002/jae.2548

Cited by 25 publications

(20 citation statements)

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“…Finally, comparing the results in columns , , and with those in columns , , and , one can only come to the conclusion that it is hard to anticipate the effect of omitting the varying‐time interval in the case of the nonautoregressive covariates. As shown in Millimet and McDonough (), such effect will in general depend on their own dynamic behavior.…”

Section: Resultsmentioning

confidence: 97%

“…As noted above, the observations are irregularly spaced (with two‐ or three‐year intervals, depending on the period). This varying interval length introduces several problems in the context of dynamic panel models (see Millimet and McDonough, ). Millimet and McDonough () evaluate a range of estimators for panels with irregular spacing, including the A‐B estimator, and conclude that they are all inconsistent in presence of endogenous regressors (see table 3 in Millimet and McDonough, ).…”

Section: Resultsmentioning

confidence: 99%

“…This varying interval length introduces several problems in the context of dynamic panel models (see Millimet and McDonough, ). Millimet and McDonough () evaluate a range of estimators for panels with irregular spacing, including the A‐B estimator, and conclude that they are all inconsistent in presence of endogenous regressors (see table 3 in Millimet and McDonough, ). Wages and the market potential function are jointly determined in the theoretical model proposed here, so their exogeneity in the estimated model would be unwarranted.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Location and Entrepreneurship: Insights From a Spatially‐explicit Occupational Choice Model With an Application to Chile

Modrego

McCann

Foster

et al. 2016

Journal of Regional Science

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Occupational choice and heterogeneous managerial ability enter a spatial Dixit-Stiglitz setting, linking location, wages and regional entrepreneurship rates. Market potential has a positive partial effect and wages a negative partial effect on the regional supply of entrepreneurs, both balancing in equilibrium with endogenous wages. Market potential increases profits, but also the opportunity cost of entrepreneurship. In the long-run equilibrium with perfect mobility, the cut-off level of ability determining selection into entrepreneurship will be the same across regions; moreover, regional differences in entrepreneurship rates depend only in differences in average fixed costs of firms. An empirical application is provided for Chile.Our model explicitly includes both push and pull mechanisms conditioning the spatial distribution of entrepreneurship. More specifically, we introduce occupational choice and heterogeneous managerial ability, two features of microeconomic theories of entrepreneurship (Parker, 2009), into the spatial Dixit-Stiglitz setting (Fujita et al., 1999). The resulting model predicts a positive partial effect of market potential, and a negative partial effect of domestic wages on the regional supply of entrepreneurs. But wages are also determined by the regional market potential, and so the model predicts, in the short-run equilibrium, offsetting effects of MP over the regional supply of entrepreneurs. The long-run equilibrium result, after full mobility of workers across regions and occupations, is that differences in entrepreneurship rates are given by differences in fixed costs across regions. We provide an empirical application of the model, by econometrically testing the offsetting effects of regional market potential on short-run changes in municipal entrepreneurship rates in Chile. The evidence supports the model's theoretical implications.Our model introduces several innovations to existing NEG-based models of location and entrepreneurship. First, in contrast to Sato et al. (2012), we incorporate and test for interregional demand linkages as an explanation for the spatial variation in entrepreneurship, using a supply equation relating regional entrepreneurship rates and the Helpman-Hanson's market potential function (Helpman, 1998;Hanson, 2005). Second, in contrast to what are known as "footloose entrepreneurs" models of agglomeration (e.g., Forslid and Ottaviano, 2003), where there is an exogenous stock of entrepreneurs choosing their location, our model focuses on the effects of regional MP in the individual's decision to become an entrepreneur. Third, unlike Sato et al. (2012), our model endogenizes both business profits and wages, both conditioning the selection into entrepreneurship. The model yields testable parameter restrictions reflecting the trade-offs between regional incentives and the opportunity costs of entrepreneurship. Finally, it incorporates entrepreneurs' heterogeneity, leading to the sensible implication that, ceteris paribus, less efficient firms can succeed in area...

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Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Location and Entrepreneurship: Insights From a Spatially‐explicit Occupational Choice Model With an Application to Chile

Modrego

McCann

Foster

et al. 2016

Journal of Regional Science

View full text Add to dashboard Cite

show abstract

“…As the correlation between an individual’s responses at two points in time

t_{1}

and

t_{2}

will typically decay with

| t_{2} ‐ t_{1} |

, the coefficients of the lagged response and time‐varying covariates capturing changes between

t_{1}

and

t_{2}

cannot be assumed invariant to spacing. The naïve treatment of unequally spaced observations as if they are equally spaced will lead to biased coefficients of the lagged response and serially correlated predictors (Millimet & McDonough, 2017; Sasaki & Xin, 2017). The usual approach to this problem is simply to exclude observations to achieve an equally spaced panel, which may lead to a large reduction in the number of measurement occasions available for analysis.…”

Section: Introductionmentioning

confidence: 99%

Random Effects Dynamic Panel Models for Unequally Spaced Multivariate Categorical Repeated Measures: An Application to Child–Parent Exchanges of Support

Steele

Grundy

2021

Journal of the Royal Statistical Society Series C: Applied Statistics

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Exchanges of practical or financial help between people living in different households are a major component of intergenerational exchanges within families and an increasingly important source of support for individuals in need. Using longitudinal data, bivariate dynamic panel models can be applied to study the effects of changes in individual circumstances on help given to and received from non-coresident parents and the reciprocity of exchanges. However, the use of a rotating module for collection of data on exchanges leads to data where the response measurements are unequally spaced and taken less frequently than for the timevarying covariates. Existing approaches to this problem focus on fixed effects linear models for univariate continuous responses. We propose a random effects estimator for a family of dynamic panel models that can handle continuous, binary or ordinal multivariate responses. The performance of the estimator is assessed in a simulation study. A bivariate probit dynamic panel model is then applied to estimate the effects of partnership and employment transitions in the previous year and the presence and age of children in the current year on an individual's propensity to give or receive help. Annual data on respondents' partnership, employment status and dependent children, and data on exchanges of help collected at 2-and 5-year intervals are used in this study. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The unequally spaced pattern is also useful for repeated sales of houses that are not sold each year but at irregularly spaced intervals. It is also a common problem for longitudinal surveys and household surveys in developed as well as developing countries, see examples of these in Table 1 of McKenzie (2001) as well as Table 1 of Millimet and McDonough (2017). Unbalanced panel data estimation and testing has been studied in econometrics, see Chapter 9 of Baltagi (2013a) and the references cited there.…”

Section: Introductionmentioning

confidence: 99%

Forecasting with unbalanced panel data

Baltagi

Liu

2020

Journal of Forecasting

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This paper derives the best linear unbiased prediction (BLUP) for an unbalanced panel data model. Starting with a simple error component regression model with unbalanced panel data and random effects, it generalizes the BLUP derived by Taub (1979) to unbalanced panels. Next it derives the BLUP for an unequally spaced panel data model with serial correlation of the AR(1) type in the remainder disturbances considered by Baltagi and Wu (1999). This in turn extends the BLUP for a panel data model with AR(1) type remainder disturbances derived by Baltagi and Li (1992) from the balanced to the unequally spaced panel data case. The derivations are easily implemented and reduce to tractable expressions using an extension of the Fuller and Battese (1974) transformation from the balanced to the unbalanced panel data case.

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Dynamic Panel Data Models With Irregular Spacing: With an Application to Early Childhood Development

Cited by 25 publications

References 60 publications

Location and Entrepreneurship: Insights From a Spatially‐explicit Occupational Choice Model With an Application to Chile

Location and Entrepreneurship: Insights From a Spatially‐explicit Occupational Choice Model With an Application to Chile

Random Effects Dynamic Panel Models for Unequally Spaced Multivariate Categorical Repeated Measures: An Application to Child–Parent Exchanges of Support

Forecasting with unbalanced panel data

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