Intensive longitudinal data (ILD) is an increasingly common data type in the social and behavioral sciences. Despite the many benefits these data provide, little work has been dedicated to realize the potential such data hold for forecasting dynamic processes at the individual level. To address this gap in the literature, we present the multi-VAR framework, a novel methodological approach allowing for penalized estimation of ILD collected from multiple individuals. Importantly, our approach estimates models for all individuals simultaneously and is capable of adaptively adjusting to the amount of heterogeneity present across individual dynamic processes. To accomplish this, we propose a novel proximal gradient descent algorithm for solving the multi-VAR problem and prove the consistency of the recovered transition matrices. We evaluate the forecasting performance of our method in comparison with a number of benchmark methods and provide an illustrative example involving the day-to-day emotional experiences of 16 individuals over an 11-week period.

Survey evidence tells us that stock prices reflect the risks investors associate with long-run technological change. However, there is a shortage of models that can rationalize long-run risks. Unlike the previous literature assuming a fixed number of products, our model allows for new product varieties that appear in the form of new firms which face entry costs and delay in the entry process. The fixed variety model has a significant limitation in translating macroeconomic volatility into asset return volatility. Our model with growing varieties induces endogenous low-frequency fluctuations in productivity driving large, persistent variations in consumption growth and asset prices. It also changes the valuation of assets through the increase in the volatility of the pricing kernel (with a positive long-run component) and leads to higher excess returns. Our model is motivated by a simple recursively identified VAR model containing quarterly US data 1992Q3-2018Q4 with the following list of variables: total factor productivity, output, a measure of firm entry, and the excess return on stocks.

Cyber–physical–social systems (CPSS) are physical devices that are embedded in human society and possess highly integrated functionalities of sensing, computing, communication, and control. CPSS rely on their intense collaboration and information sharing through networks to be functioning. In this paper, topology-informed network information dynamics models are proposed to characterize the evolution of information processing capabilities of CPSS nodes in networks. The models are based on a mesoscale probabilistic graph model, where the sensing and computing capabilities of the nodes are captured as the probabilities of correct predictions. A topology-informed vector autoregression model and a latent variable vector autoregression model are proposed to model the correlations between prediction capabilities of nodes as linear functional relationships. A hybrid Gaussian process regression model is also developed to capture both the nonlinear spatial and temporal correlations between nodes. The new information dynamics models are demonstrated and tested with a simulator of CPSS networks. The results show that the topological information of networks can improve the efficiency in constructing the time series models. The network topology also has influences on the prediction capabilities of CPSS.

Granger-causality tests and Sims' VAR technique were used to analyze the impact of macrovariables on farm output and prices in the U.S. for the period 1934-1987. Granger-causality tests show one-way causality from the macrovariables to the farm sector variables, and this is supported by impulse response functions from a VAR model. Money and interest rate effects on agricultural prices and output are apparent in the data.

A vector autoregression (VAR) model of corn, farm egg, and retail egg prices is estimated and shocked with a corn price increase. Impulse responses in egg prices, t-statistics for the impulse responses, and decompositions of forecast error variance are presented. Analyses of results provide insights on the corn/egg price transmission mechanism and on how corn price shocks pulsate through the egg-related economy.

Vector autoregression was utilized to investigate dynamic relationships existing between prices of purebred bulls and prices of slaughter steers, utility cows, feeder calves, and cow-calf pairs. Results suggest purebred bull prices respond most quickly to an increase in utility cow prices (proxy for slaughter bull prices). Feeder calf prices exhibited the most pronounced positive effect on the price of herd sires, with a lagged response which took over two years to build.

This discussion is a review and critique of the three papers in the 2012Southern Agricultural Economics Association invited paper session titledCauses of Agricultural and Food Price Inflation and Volatility.

In structural VARs, unexpected monetary tightening often leads to the price puzzle, a counterintuitive increase in inflation in the impulse response function. The identification of impulse responses requires at least a minimal set of structural assumptions, and models exhibiting the price puzzle typically use standard assumptions focusing mainly on relationships among contemporaneous disturbances. This note uses a well-established stylized fact, the long lags of monetary policy, to motivate a simple additional identifying assumption. The assumption eliminates a single term in one equation of the reduced form, and with it the price puzzle.

In this paper we investigate the effects of oil price uncertainty and its asymmetry on real economic activity in the United States, in the context of a bivariate vector autoregression with GARCH-in-mean errors. The model allows for the possibilities of spillovers and asymmetries in the variance–covariance structure for real output growth and the change in the real price of oil. Our measure of oil price uncertainty is the conditional variance of the oil price–change forecast error. We isolate the effects of volatility in the change in the price of oil and its asymmetry on output growth and employ simulation methods to calculate generalized impulse response functions and volatility impulse response functions to trace the effects of independent shocks on the conditional means and the conditional variances, respectively, of the variables. We find that oil price uncertainty has a negative effect on output, and that shocks to the price of oil and its uncertainty have asymmetric effects on output.

Monetary policy should be guided by macroeconomic models with limited nominal rigidity; ‘New Classical’ or even for some issues just plain Classical (i.e. with no nominal rigidity at all) models are perfectly adequate for understanding various aspects of the economy that have previously led economists to believe in a high degree of nominal rigidity. On UK data these models account for the facts of inflation persistence and exchange rate ‘overshooting’; their impulse responses are in line with the data; and a typical example, the Liverpool Model, is marginally accepted in its entirety by the data since 1979. Such models suggest that no increased macro instability would result from taking the rigours of monetary policy one stage further from inflation targeting and ensuring that the price level itself is returned to its long-run preset target path — so that the value of money over long periods of time would be utterly predictable.

Christopher Sims is a well-known intellectual leader in time-series econometrics and applied macroeconomics. Among his many honors and distinctions, he has been the President of the Econometric Society and he is a member of the National Academy of Sciences. He has made fundamental contributions to both statistical theory of time series and empirical macroeconomics. Sims' work is influential precisely because it was motivated by important problems in macroeconomics. Not only did Sims study questions of statistical approximation in abstract environments, he showed how to apply the resulting apparatus to a variety of specific problems confronting applied researchers. The applications include seasonality in economic time series, aggregation over time, and approximation in formulating statistical models with economic underpinnings. Moreover, Sims' contributions to causality in time series and to the development of vector autoregressive methods were complemented by an important body of empirical research. Sims has served as an effective advocate and critic of the extensively used vector autoregressive statistical methods. Motivated by his own and related empirical research, Sims is one of the leaders in rethinking how monetary policy should be modeled and reconsidering the channels by which monetary policy influences economic aggregates. This interview with Chris Sims gives an opportunity to explore further the context of many of these contributions. Sims typically has a unique perspective on many economic problems, a perspective that is articulated in his answers to a variety of questions.

This paper investigates conditions under which a long-run recursive model can be used to identify a structure. Economists frequently employ this type of empirical model. I define the class of long-run partially recursive structures. If an economic system is a member of this class, then certain long-run recursive empirical models will obtain some of the structural impulse response functions. This sufficient condition for a structure is first shown in a vector autoregression. A well-known example from the literature is used to illustrate this particular class of structures and to present some useful applications of the result. Then the result is shown in models of cointegrated time series. Necessary conditions for a long-run recursive model to identify structure are addressed in the conclusion.