HOW GOOD ARE INPUT DEMAND MODELS USED IN PREVIOUS STUDIES NOW?

Demand and Price Analysis,

Pitfalls and Remedies for Cross Validation with Multi-trait Genomic Prediction Methods.

Incorporating measurements on correlated traits into genomic prediction models can increase prediction accuracy and selection gain. However, multi-trait genomic prediction models are complex and prone to overfitting which may result in a loss of prediction accuracy relative to single-trait genomic prediction. Cross-validation is considered the gold standard method for selecting and tuning models for genomic prediction in both plant and animal breeding. When used appropriately, cross-validation gives an accurate estimate of the prediction accuracy of a genomic prediction model, and can effectively choose among disparate models based on their expected performance in real data. However, we show that a naive cross-validation strategy applied to the multi-trait prediction problem can be severely biased and lead to sub-optimal choices between single and multi-trait models when secondary traits are used to aid in the prediction of focal traits and these secondary traits are measured on the individuals to be tested. We use simulations to demonstrate the extent of the problem and propose three partial solutions: 1) a parametric solution from selection index theory, 2) a semi-parametric method for correcting the cross-validation estimates of prediction accuracy, and 3) a fully non-parametric method which we call CV2*: validating model predictions against focal trait measurements from genetically related individuals. The current excitement over high-throughput phenotyping suggests that more comprehensive phenotype measurements will be useful for accelerating breeding programs. Using an appropriate cross-validation strategy should more reliably determine if and when combining information across multiple traits is useful

Missing Aggregate Dynamics: On the Slow Convergence of Lumpy Adjustment Models

The dynamic response of aggregate variables to shocks is one of the central concerns of applied macroeconomics. The main measurement procedure for these dynamics consists of estimmiating an ARMA or VAR (VARs, for short). In non- or semi-structural approaches, the characterization of dynamics stops there. In other, more structural approaches, researcher try to uncover underlying adjustment cost parameters from the estimated VARs. Yet, in others, such as in RBC models, these estimates are used as the benchmark over which the success of the calibration exercise, and the need for further theorizing, is assessed. The main point of this paper is that when the microeconomic adjustment underlying the corresponding aggregates is lumpy, conventional VARs procedures are often inadequate for all of the above practices. In particular, the researcher will conclude that there is less persistence in the response of aggregate variables to aggregate shocks than there really is. Paradoxically, while idiosyncratic productivity and demand shocks smooth away microeconomic non-convexities and are often used as a justification for approximating aggregate dynamics with linear models, their presence exacerbate the bias. Since in practice idiosyncratic uncertainty is many times larger than aggregate uncertainty, we conclude that the problem of missing aggregate dynamics is prevalent in empirical and quantitative macroeconomic research.Speed of adjustment, Discrete adjustment, Lumpy adjustment, Aggregation, Calvo model, ARMA process, Partial adjustment, Expected response time, Monetary policy, Investment, Labor demand, Sticky prices, Idiosyncratic shocks, Impulse response function, Time-to-build

A MODIFIED PARTIAL ADJUSTMENT MODEL OF AGGREGATE U.S. AGRICULTURAL SUPPLY

Aggregate U.S. agricultural supply response is modeled through a modified partial adjustment model, where the effects of weather and other temporal stochastic effects are structured to be purely static, while the effects of price and technology, or trend, are dynamic. The model is applied to a time series of aggregate U.S. farm output, aggregate U.S. crop production, and aggregate U.S. livestock and livestock products production for several sample periods within the period 1911-1958. The three aggregate output indexes are tested for irreversibilities in supply response, and no evidence of a definitive irreversible supply function is found for any of the dynamic supply models. The use of a nonstochastic difference equation to model the aggregate farm output and crop production equations results in short-run elasticity estimates that are somewhat smaller than previous studied suggest while the long-run elasticities are somewhat larger.Demand and Price Analysis, Production Economics,

Income and Democracy: Revisiting the Evidence

It is well-known in the literature that income per capita is strongly correlated with the level of democracy across countries. In an influential paper, Acemoglu et al. (2008) find that this linear correlation disappears once they control for country-specific effects focusing on within-country variation. In this paper we find evidence of a non-linear effect from income to democracy even after controlling for country-specific effects. While a positive effect emerges for poor countries, this effect vanishes for rich countries.Democracy; Income; Lipset hypothesis; panel data

Adjustment is Much Slower than You Think

In most instances, the dynamic response of monetary and other policies to shocks is infrequent and lumpy. The same holds for the microeconomic response of some of the most important economic variables, such as investment, labor demand, and prices. We show that the standard practice of estimating the speed of adjustment of such variables with partial-adjustment ARMA procedures substantially overestimates this speed. For example, for the target federal funds rate, we find that the actual response to shocks is less than half as fast as the estimated response. For investment, labor demand and prices, the speed of adjustment inferred from aggregates of a small number of agents is likely to be close to instantaneous. While aggregating across microeconomic units reduces the bias (the limit of which is illustrated by Rotemberg's widely used linear aggregate characterization of Calvo's model of sticky prices), in some instances convergence is extremely slow. For example, even after aggregating investment across all establishments in U.S. manufacturing, the estimate of its speed of adjustment to shocks is biased upward by more than 80 percent. While the bias is not as extreme for labor demand and prices, it still remains significant at high levels of aggregation. Because the bias rises with disaggregation, findings of microeconomic adjustment that is substantially faster than aggregate adjustment are generally suspect.