Microeconometrics deals

Microeconometrics deals with the theory and applications of methods of data analysis developed for microdata pertaining to individuals, households, and firms. A broader definition might also include regional- and state-level data. Microdata are usually either cross sectional, in which case they refer to conditions at the same point in time, or longitudinal (panel) in which case they refer to the same observational units over several periods. Such observations are generated from both nonexperimental setups, such as censuses and surveys, and quasi experimental or experimental setups, such as social experiments implemented by governments with the participation of volunteers. A microeconometric model may be a full specification of the probability distribution of a set of microeconomic observations; it may also be a partial specification of some distributional properties, such as moments, of a subset of variables. The mean of a single dependent variable conditional on regressors is of particular interest. There are several objectives of microeconometrics. They include both data description and causal inference. The first can be defined broadly to include moment properties of response variables, or regression equations that highlight associations rather
than causal relations. The second category includes causal relationships that aim at measurement and/or empirical confirmation or refutation of conjectures and propositions regarding microeconomic behavior. The type and style of empirical investigations therefore span a wide spectrum. At one end of the spectrum can be found very highly structured models, derived from detailed specification of the underlying economic behavior, that analyze causal (behavioral) or structural relationships for interdependent microeconomic variables. At the other end are reduced form studies that aim to uncover correlations and associations among variables, without necessarily relying on a detailed specification of all relevant interdependencies. Both approaches share the common goal of uncovering important and striking relationships that could be helpful in understanding microeconomic behavior, but they differ in the extent to which they rely on economic theory to guide their empirical investigations.

As a subdiscipline microeconometrics is newer than macroeconometrics, which is concerned with modeling of market and aggregate data. A great deal of the early work in applied econometrics was based on aggregate time-series data collected by government agencies. Much of the early work on statistical demand analysis up until about 1940 used market rather than individual or household data (Hendry and Morgan, 1996). Morgan’s (1990) book on the history of econometric ideas makes no reference to microeconometric work before the 1940s, with one important exception. That exception is the work on household budget data that was instigated by concern with the living standards of the less well-off in many countries. This led to the collection of household budget data that provided the raw material for some of the earlier microeconometric studies such as those pioneered by Allen and Bowley (1935). Nevertheless, it is only since the 1950s that microeconometrics has emerged as a distinctive and recognized subdiscipline. Even into the 1960s the core of microeconometrics consisted of demand analyses based on household surveys. With the award of the year 2000 Nobel Prize in Economics to James Heckman and Daniel McFadden for their contributions to microeconometrics, the subject area has achieved clear recognition as a distinct subdiscipline. The award cited Heckman “for his development of theory and methods for analyzing selective samples” and McFadden “for his development of theory and methods for analyzing discrete choice.” Examples of the type of topics that microeconometrics deals with were also mentioned in the citation: “ . . . what factors determine whether an individual decides to work and, if so, how many hours? How do economic incentives affect individual choices regarding education, occupation or place of residence? What are the effects
of different labor-market and educational programs on an individual’s income and employment?” Applications of microeconometric methods can be found not only in every area of microeconomics but also in other cognate social sciences such as political science, sociology, and geography.

Beginning with the 1970s and especially within the past two decades revolutionary advances in our capacity for handling large data sets and associated computations have taken place. These, together with the accompanying explosion in the availability of large microeconomic data sets, have greatly expanded the scope of microeconometrics. As a result, although empirical demand analysis continues to be one of the most important areas of application for microeconometric methods, its style and content have been heavily influenced by newer methods and models. Further, applications in economic development, finance, health, industrial organization, labor and public economics, and applied microeconomics generally are now commonplace, and these applications will be encountered at various places in this book. The primary focus of this book is on the newer material that has emerged in the past three decades. Our goal is to survey concepts, models, and methods that we regard as standard components of a modern microeconometrician’s tool kit. Of course, the notion of standard methods and models is inevitably both subjective and elastic, being a function of the presumed clientele of this book as well as the authors’ own backgrounds. There may also be topics we regard as too advanced for an introductory book such as this that others would place in a different category.

Microeconometrics focuses on the complications of nonlinear models and on obtaining estimates that can be given a structural interpretation. By contrast, the distinguishing feature of econometrics is the emphasis placed on causal modeling.
This chapter introduces the key concepts related to causal (and noncausal) modeling, concepts that are germane to both linear and nonlinear models. [ A. Colin Cameron, Pravin K. Trivedi: Microeconometrics: Methods and Applications, 2005]

microeconomics

Microeconomics (from Greek prefix micro- meaning "small" + "economics") is a branch of economics that studies how the individual parts of the economy, the household and the firms, make decisions to allocate limited resources, typically in markets where goods or services are being bought and sold. Microeconomics examines how these decisions and behaviours affect the supply and demand for goods and services, which determines prices, and how prices, in turn, determine the supply and demand of goods and services.

This is a contrast to macroeconomics, which involves the "sum total of economic activity, dealing with the issues of growth, inflation, and unemployment. Microeconomics also deals with the effects of national economic policies (such as changing taxation levels) on the before mentioned aspects of the economy. Particularly in the wake of the Lucas critique, much of modern macroeconomic theory has been built upon 'microfoundations' — i.e. based upon basic assumptions about micro-level behaviour.

One of the goals of microeconomics is to analyze market mechanisms that establish relative prices amongst goods and services and allocation of limited resources amongst many alternative uses. Microeconomics analyzes market failure, where markets fail to produce efficient results, and describes the theoretical conditions needed for perfect competition. Significant fields of study in microeconomics include general equilibrium, markets under asymmetric information, choice under uncertainty and economic applications of game theory. Also considered is the elasticity of products within the market system.