Some strange properties of panel data estimators

Robertson, D. H.; Symons, James

doi:10.1002/jae.3950070206

Cited by 138 publications

(80 citation statements)

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“…We show that the illusion of partial adjustment in county-level data arises because aggregating over similar micro units (i.e., fields in the same county) inflates the bias due to coefficients that differ across fields (Robertson and Symons 1992;Pesaran and Smith 1995). Our results indicate that researchers should exercise caution in interpreting dynamic economic behavior from commonly used dynamic panel estimators, especially if the panels represent aggregates over similar micro units.…”

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confidence: 81%

“…Furthermore, when the coefficients are heterogeneous across fields, estimating a single coefficient for all fields (i.e., pooling) leads to biased estimates (Robertson and Symons 1992;Pesaran and Smith 1995). The bias from heterogeneous coefficients occurs because the error term in the pooled model is autocorrelated since it contains the lagged dependent variable and autocorrelated prices.…”

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confidence: 99%

“…In order to account for spatial de-pendence in the data, we cluster standard errors by year. In dynamic panel regression models of the type that we estimate, coefficient heterogeneity can induce potentially large 2 biases (Robertson and Symons 1992;Pesaran and Smith 1995). We alleviate such biases by estimating separate econometric models for 24 regions with different soil and climate characteristics.…”

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confidence: 99%

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Crop Supply Dynamics and the Illusion of Partial Adjustment

Hendricks

Smith

Sumner

2014

American J Agri Economics

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We use field-level data to estimate the response of corn and soybean acreage to price shocks. Our sample contains more than eight million observations derived from satellite imagery and includes every field in Iowa, Illinois, and Indiana. We estimate that aggregate crop acreage responds more to price shocks in the short run than in the long run, and we show theoretically how the benefits of crop rotation generate this response pattern. In essence, farmers who change crops due to a price shock have an incentive to switch back to the previous crop to capture the benefits of crop rotation. Our result contradicts the long-held belief that agricultural supply responds gradually to price shocks through partial adjustment. We would not have obtained this result had we used county-level panel data. Standard econometric methods applied to county-level data produce estimates consistent with partial adjustment. We show that this apparent partial adjustment is illusory, and we demonstrate how it arises from the fact that fields in the same county are more similar to each other than to fields in other counties. This result underscores the importance of using models with appropriate micro-foundations and cautions against inferring micro-level rigidities from inertia in aggregate panel data. Our preferred estimate of the own-price long-run elasticity of corn acreage is 0.29 and the cross-price elasticity is -0.22. The corresponding elasticities for soybean acreage are 0.26 and -0.33. Our estimated short-run elasticities are 37 percent larger than their long-run counterparts. 2 How much more land gets allocated to a crop when relative prices change? The answer to this question is the central parameter for understanding world food prospects, the impacts of farm subsidies, and the environmental consequences from land use change, among other public and policy issues (e.g., Roberts and Schlenker 2013;Lichtenberg and Zilberman 1986;Searchinger et al. 2008;Donner and Kucharik 2008). We provide a new and better answer for an important set of commodities in the world food system. We use a conceptual approach that accounts for crop rotations, a massive sample of individual fields, and econometric methods that account for heterogeneous incentives to rotate crops and heterogeneous responses to prices. Moreover, we show that supply response is seriously misestimated when standard econometric methods are applied to county-level panel data. One consequence of this bias is a mistaken understanding of supply dynamics and, in particular, the relationship between short-run and long-run supply response.A typical agricultural field in the United States Corn Belt tends to alternate between growing corn in one year and soybeans the next. This pattern reflects a common agronomic feature of crop production: planting a crop on the same field in consecutive years decreases the productivity of the soil for growing that crop and increases pest populations. These features generate dynamic complementarity in crop production because the marginal valu...

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Crop Supply Dynamics and the Illusion of Partial Adjustment

Hendricks

Smith

Sumner

2014

American J Agri Economics

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“…In spite of the growing concern of potential heterogeneity among the cross-sectional units when performing pooled data analysis, proponents of the homogeneous panel sustain that gains from pooling outweigh any costs. In contrast, a number of scholars, for example, Robertson and Symons (1992), Pesaran and Smith (1995), and Pesaran, Smith and Im (1996) dismiss pooling the data across heterogeneous units on the grounds that heterogeneous estimates can be combined to obtain homogeneous estimates. More specifically, Pesaran and Smith (1995) argue that the inherent parameter heterogeneity of panels makes the homogeneous assumption redundant and therefore the average from individual regressions should be used instead.…”

Section: Econometric Methodologymentioning

confidence: 99%

A Keynesian-Kaleckian model of investment determination: a panel data investigation

Alexiou

2010

Journal of Post Keynesian Economics

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The undertaken study assesses the empirical merits of the Post Keynesian doctrine as this is reflected by both the Keynesian as well as the Kaleckian theoretical approaches to investment determination. A GMM panel data methodology provides the econometric platform upon which the respective models have been tested. Annual time series data has been used, spanning from 1970 to 2005, for the G7 economies.The generated evidence confirms previous analyses in so far as capacity utilization and profits assume a key role in the determination of investment.

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“…Exploratory research on the statistical properties of panel data models (notably Pesaran and Smith, 1995;Robertson and Symons, 1992) rates. This methodology, or a variation thereof, is applied by a large number of recent empirical papers on PPP (examples are provided in footnote 2).…”

Section: Introductionmentioning

confidence: 99%

Why Panel Tests of Purchasing Power Parity Should Allow for Heterogeneous Mean Reversion

2010

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Recent studies of purchasing power parity (PPP) use panel tests that fail to take into account heterogeneity in the speed of mean reversion across real exchange rates. In contrast to several other severe restrictions of panel models and tests of PPP, the assumption of homogeneous mean reversion is still widely used and its consequences are virtually unexplored. This paper analyzes the properties of homogeneous and heterogeneous panel unit root testing methodologies. Using Monte Carlo simulation, we uncover important adverse properties of the panel approach that relies on homogeneous estimation and testing. More specifically, power functions are low and assume irregular shapes. Furthermore, homogeneous estimates of the mean reversion parameters exhibit potentially large biases. These properties can lead to misleading inferences on the validity of PPP. Our findings highlight the importance of allowing for heterogeneous estimation when testing for a unit root in panels of real exchange rates.

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Some strange properties of panel data estimators

Cited by 138 publications

References 3 publications

Crop Supply Dynamics and the Illusion of Partial Adjustment

Crop Supply Dynamics and the Illusion of Partial Adjustment

A Keynesian-Kaleckian model of investment determination: a panel data investigation

Why Panel Tests of Purchasing Power Parity Should Allow for Heterogeneous Mean Reversion

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