On estimating long-run effects in models with lagged dependent variables

A common procedure in economics is to estimate long-run effects from models with lagged dependent variables. For example, macro panel studies frequently are concerned with estimating the long-run impacts of fiscal policy, international aid, or foreign investment.Our analysis points out the hazards of this practice. We use Monte Carlo experiments to demonstrate that estimating long-run impacts from dynamic models produces unreliable results.Biases can be substantial, sample ranges very wide, and hypothesis tests can be rendered useless in realistic data environments. There are three reasons for this poor performance. First, OLS estimates of the coefficient of a lagged dependent variable are downwardly biased in finite samples. Second, small biases in the estimate of the lagged, dependent variable coefficient are magnified in the calculation of long-run effects. And third, and perhaps most importantly, the statistical distribution associated with estimates of the LRP is complicated, heavy-tailed, and difficult to use for hypothesis testing. While many of the underlying problems have been long-known in the literature, the continued widespread use of the associated empirical procedures suggests that researchers are unaware of the extent and severity of the estimation problems. This study aims to illustrate their practical importance for applied research.     

A Tutorial on Palm Distributions for Spatial Point Processes

This tutorial provides an introduction to Palm distributions for spatial point processes. Initially, in the context of finite point processes, we give an explicit definition of Palm distributions in terms of their density functions. Then we review Palm distributions in the general case. Finally, we discuss some examples of Palm distributions for specific models and some applications.     

Impact of contracts in high yielding varieties seed production on profits and yield: The case of Nepal

Nepal’s population continues to grow, but the agricultural sector’s performance remains almost stagnant. This has led to a decline in the per capita availability of food. Increasing agricultural productivity is the key to agricultural growth, and one strategy for increasing agricultural productivity is to use improved seeds. This study investigates the impact of contract farming (CF) in high yielding varieties (HYV) of paddy seed production on costs, yield, and profits of smallholder farms in Nepal. Using farm-level data and a non-parametric propensity score matching estimator, the study finds a significant positive impact of contract HYV seed farming on revenues, profits, and yield, and a significant negative impact on total costs of production. Additionally, very small farms (⩽0.43 ha) with CF in HYV paddy seeds tend to gain the most when it comes to yield per hectare. Our estimates reveal that the average smallholder household in Nepal engaged in CF with input conditions receives higher profits. However, farmers engaged in CF with output conditions tend to have higher yields but smaller profits. Finally, farmers engaged in CF in HYV paddy seeds with both input and output conditions have the highest yield gains and significantly higher profits.     

Accounting for breakout in Britain: The industrial revolution through a Malthusian lens

This paper introduces a simple dynamic model to examine the breakout from a Malthusian economy to a modern growth regime. It identifies several factors that determine the fastest rate at which the population can grow without engendering declining living standards. We then apply the framework to Britain and find a dramatic increase in sustainable population growth at the time of the Industrial Revolution, well before the beginning of modern levels of income growth. The main contributions to the British breakout were technological improvements and structural change away from agricultural production, while coal, capital, and trade played a minor role. In addition to solidifying the link between the Industrial Revolution and rising living standards, this research reconciles the gradualist and limited Crafts–Harley view of the Industrial Revolution with a dramatic and rapid change in Britain’s macroeconomic character.     

Some Variants of Constrained Estimation in Finite Population Sampling

This is a review article that unifies several important examples using constrained optimisation techniques. The basic tools are three simple mathematical optimisation results subject to certain constraints. Applications include calibration, benchmarking in small area estimation and imputation. A final illustration is constrained optimisation under a general divergence loss.     

Celebrity endorsements: Investigating the interactive effects of internalization,identification and product type on consumers’ attitudes and intentions

In line with Kang and Herr's work (2006), this research questions the ELM's contention that celebrity credibility serves foremost as a peripheral element in a persuasive communication context. Nevertheless, in a different light to the Kang and Herr's ‘either-central-or-peripheral-role’ of a source characteristic, this research advances that celebrity credibility plays concomitantly central and peripheral roles in a persuasive message context depending on product involvement and brand-purchase motive. Particularly, this research uses the notions of ‘source internalization’ and ‘source identification’ (Kelman, 1961) to theorize that source credibility can have a concomitant dual role (peripheral and central) in a persuasion context. More precisely, this research investigates the interactive effects of source identification and internalization with product involvement as well as brand-purchase motives on consumers' attitudes and intentions. Source internalization is predicted to have persuasive effects in the contexts of high-involvement as well as informational products. Source identification is predicted to have persuasive effects in the context of low-involvement as well as transformational products. The findings of two experiments show that celebrity credibility acts through only a single route (i.e., only internalization has persuasive effects) uniformly across different product involvement levels and brand-purchase motive types. We interpret these results with the lens of the ‘match-up’ hypothesis (Kamins, 1990).     

Estimating the gravity model when zero trade flows are frequent and economically determined

ABSTRACT

Since the 1950s, we have known that the presence of zero-valued dependent variables can seriously bias econometric estimates whether the zeros are included or excluded. Yet the widely-used gravity model is frequently estimated on samples that include large fractions of zeros. An influential paper by Santos Silva and Tenreyro – based on simulations that include no economically-determined zeros – concludes that the bias problems resulting from zeros and those resulting from heteroscedasticity and nonlinearity can be solved using the Poisson Pseudo-Maximum-Likelihood (PPML) model including the zero values. This paper begins by adapting the Santos Silva and Tenreyro experimental design to include economically-determined zeros to see whether this conclusion continues to hold. With this design, it finds that alternative estimators have lower bias than PPML. Changing to a Monte Carlo design that replicates the much-higher real-world frequency of predicted values near zero restores the finding of lower bias with the PPML estimator. The results highlight the need for very careful design of Monte Carlo experiments when evaluating alternative estimators of the gravity model.     

Reliable Inference in Categorical Regression Analysis for Non‐randomly Coarsened Observations

In most surveys, one is confronted with missing or, more generally, coarse data. Traditional methods dealing with these data require strong, untestable and often doubtful assumptions, for example, coarsening at random. But due to the resulting, potentially severe bias, there is a growing interest in approaches that only include tenable knowledge about the coarsening process, leading to imprecise but reliable results. In this spirit, we study regression analysis with a coarse categorical‐dependent variable and precisely observed categorical covariates. Our (profile) likelihood‐based approach can incorporate weak knowledge about the coarsening process and thus offers a synthesis of traditional methods and cautious strategies refraining from any coarsening assumptions. This also allows a discussion of the uncertainty about the coarsening process, besides sampling uncertainty and model uncertainty. Our procedure is illustrated with data of the panel study ‘Labour market and social security' conducted by the Institute for Employment Research, whose questionnaire design produces coarse data.     

Varying Coefficient Regression Models: A Review and New Developments

Varying coefficient regression models are known to be very useful tools for analysing the relation between a response and a group of covariates. Their structure and interpretability are similar to those for the traditional linear regression model, but they are more flexible because of the infinite dimensionality of the corresponding parameter spaces. The aims of this paper are to give an overview on the existing methodological and theoretical developments for varying coefficient models and to discuss their extensions with some new developments. The new developments enable us to use different amount of smoothing for estimating different component functions in the models. They are for a flexible form of varying coefficient models that requires smoothing across different covariates' spaces and are based on the smooth backfitting technique that is admitted as a powerful technique for fitting structural regression models and is also known to free us from the curse of dimensionality.     

Inference for large financial systems

We treat the parameter estimation problem for mean‐field models of large interacting financial systems such as the banking system and a pool of assets held by an institution or backing a security. We develop an asymptotic inference approach that addresses the scale and complexity of such systems. Harnessing the weak convergence results developed for mean‐field financial systems in the literature, we construct an approximate likelihood for large systems. The approximate likelihood has a conditionally Gaussian structure, enabling us to design an efficient numerical method for its evaluation. We provide a representation of the corresponding approximate estimator in terms of a weighted least‐squares estimator, and use it to analyze the large‐system and large‐sample behavior of the estimator. Numerical results for a mean‐field model of systemic financial risk highlight the efficiency and accuracy of our estimator.