This paper builds on the structural equations, treatment effect, and machine learning literatures to provide a causal framework that permits the identification and estimation of causal effects from observational studies. We begin by providing a causal interpretation for standard exogenous regressors and standard “valid” and “relevant” instrumental variables. We then build on this interpretation to characterize extended instrumental variables (EIV) methods, that is methods that make use of variables that need not be valid instruments in the standard sense, but that are nevertheless instrumental in the recovery of causal effects of interest. After examining special cases of single and double EIV methods, we provide necessary and sufficient conditions for the identification of causal effects by means of EIV and provide consistent and asymptotically normal estimators for the effects of interest.

This chapter explains the logic of structural econometric models and compares them to other types of econometric models. We provide a framework researchers can use to develop and evaluate structural econometric models. This framework pays particular attention to describing different sources of unobservables in structural models. We use our framework to evaluate several literatures in industrial organization economics, including the literatures dealing with market power, product differentiation, auctions, regulation and entry.

This paper is an attempt to survey the literature on LAD estimation of single as well as multiequation linear econometric models

We argue that many methodological confusions in time-series econometrics may be seen as arising out of ambivalence or confusion about the error terms. Relationships between macroeconomic time series are inexact and, inevitably, the early econometricians found that any estimated relationship would only fit with errors. Slutsky interpreted these errors as shocks that constitute the motive force behind business cycles. Frisch tried to dissect further the errors into two parts: stimuli, which are analogous to shocks, and nuisance aberrations. However, he failed to provide a statistical framework to make this distinction operational. Haavelmo, and subsequent researchers at the Cowles Commission, saw errors in equations as providing the statistical foundations for econometric models, and required that they conform to a priori distributional assumptions specified in structural models of the general equilibrium type, later known as simultaneous-equations models (SEM). Since theoretical models were at that time mostly static, the structural modelling strategy relegated the dynamics in time-series data frequently to nuisance, atheoretical complications. Revival of the shock interpretation in theoretical models came about through the rational expectations movement and development of the VAR (Vector AutoRegression) modelling approach. The so-called LSE (London School of Economics) dynamic specification approach decomposes the dynamics of modelled variable into three parts: short-run shocks, disequilibrium shocks and innovative residuals, with only the first two of these sustaining an economic interpretation.

Abstract not found

I don’t want to rehash that. (II) Time I spent last year visiting central banks and interviewing people there about what econometric models they use and how they use them. (III) Recent technical developments that have converted theoretical advantages of Bayesian over classical approaches to inference into practical reality in some applied areas. Associated applied work and methodological commentary emerging in the literature. (IV) Haavelmo’s 1944 paper/monograph “The Probability Approach in Econometrics”, and some related previous literature. We are going to begin by discussing (IV), using it as a kind of table of contents for aspects of (II) and (III).

List of the last 10 Memoranda:

Can causal relationships between macroeconomic variables be identified using econometric methods? Only partly, we argue. Normally causal relationships cannot be identified from empirical analysis. However, if enough changes in policy rules have occurred during the observation period, it can be possible to identify causal relationships form policy instruments to target variables. An estimation procedure for obtaining this is sketched. This estimation procedure makes it possible to distinguish between causal effects of expected policy changes on one hand and (unexpected) shocks on the other.

One basic feature of aggregate data is the presence of time-varying variance in real and nominal variables. Periods of high volatility are followed by periods of low volatility. For instance, the turbulent 1970s were followed by the much more tranquil times of the great moderation from 1984 to 2007. Modeling these movements in volatility is important to understand the source of aggregate fluctuations, the evolution of the economy, and for policy analysis. In this chapter, we first review the different mechanisms proposed in the literature to generate changes in volatility similar to the ones observed in the data. Second, we document the quantitative importance of timevarying volatility in aggregate time series. Third, we present a prototype business cycle model with time-varying volatility and explain how it can be computed and how it can be taken to the data using likelihood-based methods and non-linear filtering theory. Fourth, we present two “real life”applications. We conclude by summarizing what we know and what we do not know about volatility in macroeconomics and by pointing out some directions for future research.

The rate of capital formation by businesses has long been among the most closely watched elements of the national accounts. During the last decade, this component of investment attracted considerable interest as capital spending helped support our uncommonly high rate of economic growth. Businesses ’ demand for capital goods grew rapidly, accounting for more than its typical share of the demand for output. Not only did this spending lift the growth of aggregate demand, it also increased our capacity for supplying goods and services, which in turn could allow output to continue growing rapidly in the future. This article analyzes the performance of conventional models of investment spending by comparing their abilities to describe this spending from 1960 to 1990 as well as their abilities to forecast spending during the 1990s. These comparisons test the models and provide standards for measuring the rate of investment spending. If spending has accelerated recently, these models can help define its timing and magnitude, while