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The purpose of this paper is to introduce the upcoming symposium on experimental macroeconomics in the November issue.

Experimental, survey of articles in the symposium.

The paper describes how experiments can be used in macroeconomics.

The paper discusses the rationale for using behavioral experiments in macroeconomics, and summarizes the papers in the symposium.

Expectations play a crucial role in finance, macroeconomics, monetary economics, and fiscal policy. In the last decade a rapidly increasing number of laboratory experiments have been performed to study individual expectation formation, the interactions of individual forecasting rules, and the aggregate macro behavior they co-create. The aim of this article is to provide a comprehensive literature survey on laboratory experiments on expectations in macroeconomics and finance. In particular, we discuss the extent to which expectations are rational or may be described by simple forecasting heuristics, at the individual as well as the aggregate level.

This paper aims to introduce a new perspective on long-term stock return predictability by focusing on the relative (individual and hybrid) informative power of a wide range of accounting (firm-related), technical and macroeconomic factors while considering the past performance of the stocks using machine learning algorithms.

The sample includes a panel data set of 94 non-financial firms listed in Egyptian Exchange 100 index from 2014: Q1 to 2019: Q4. Relativity has been investigated by comparing relevant factors’ individual and combined informative power and differentiating between losers and winners based on historical stock returns. To predict the quarterly stock returns, Gaussian process regression (GPR) has been used. The robustness of the results is examined through the out-of-sample test. This study also uses linear regression (LR) as a benchmark model.

The past performance and the presence of other predictors influence the informative power of relevant factors and hence their predictive ability. The out-of-sample results show a trade-off between GPR and LR with proven superiority to GPR in limited experiments. The individual informative power outperforms the hybrid power, in which macroeconomic indicators outperform the remaining sets of indicators for losers, while winners show mixed results in terms of various performance evaluation metrics. Prediction accuracy is generally higher for losers than for winners.

This study provides interesting insight into the dynamic nature of the predictor variables in terms of stock return predictability. Hence, this study also deepens the understanding of asset pricing in a way that directly contributes to practitioners’ portfolio diversification strategies.

In concern of the chaos of factors in the literature and its accompanying misleading conclusions, this study takes another look at the approach that studies stock return predictability. To the best of the authors’ knowledge, this is the first study in the Egyptian context that re-examines the predictive power of the previously discovered factors from a different perspective that highlights their relative nature.

This article discusses the methodology of using laboratory methods to address macroeconomic questions. It also provides summaries of the articles in this volume.

This article explores the importance of accessible and focal information in influencing beliefs and attention in a learning-to-forecast laboratory experiment where subjects are incentivized to form accurate expectations about inflation and the output gap. We consider the effects of salient and accessible forecast error information and learning on subjects’ forecasting accuracy and heuristics, and on aggregate stability. Experimental evidence indicates that, while there is considerable heterogeneity in the heuristics used, subjects’ forecasts can be best described by a constant gain learning model where subjects respond to forecast errors. Salient forecast error information reduces subjects’ overreaction to their errors and leads to greater forecast accuracy, coordination of expectations, and macroeconomic stability. The benefits of this focal information are short-lived and diminish with learning.

We design an experiment to investigate the influence of announced future variations in interest rates and prices on consumption decisions. In an experimental implementation of the discounted utility model, the subjects learn the entire paths of inflation and interest rates prior to deciding on a consumption path. We decompose the total change in consumption that results from changes in either interest rates or inflation rates into anticipation and impact effects. While impact effects are of similar orders of magnitude as in the model, future changes in inflation or interest rates exert substantially smaller effects on current consumption than predicted by the model.

The purpose of this paper is to shed some light on the role of macroeconomic policy‐mix in achieving the Millennium Development Goals (MDGs), especially the goal of poverty reduction.

The paper employs descriptive approach and provides an analytical narrative of historical experience.

It is argued that macroeconomic policy‐mixes pursued by many developing countries as part of conditions to receive support from international financial institutions and the donor community have been largely restrictive. They have failed, in most cases, to generate high enough growth to have significant impacts on poverty reduction. The poverty reducing impact of growth has also been weakened by the rise in inequality due to associated policy reforms promoting market liberalization and deregulation.

The paper argues in favor of using full and productive employment, which is one of the core MDGs, as the goal of macroeconomic policies.

The paper argues that there should be refocusing of macroeconomic policies to align with MDGs.

The General Theory of Employment, Interest and Money by John Maynard Keynes (1936) gave us the macroeconomic theory for an economy of a sovereign nation state. Concerned students of macroeconomics may study earlier works. Did Karl Marx's Das Kapital and the Physiocrats' A Tableau Economique offer to teach us some aspects of macroeconomics? One may venture to suggest that Arthashatra by Kautilya, written in Sanskrit some two thousand years ago, was an ancient treatise on macroeconomics.

The purpose of this paper is to explore the ability of monetary policy to generate real effects in laboratory general equilibrium production economies.

To understand why monetary policy is not consistently effective at stabilizing economic activity, the author vary the types of agents interacting in the economy and consider treatments where subjects are playing the role of households (firms) in an economy where automated firms (households) are programmed to behave rationally.

While the majority of participants’ expectations respond to monetary policy in the direction intended, subjects do form expectations adaptively, relying heavily on past variables and forecasts in forming two-steps-ahead forecasts. Moreover, in the presence of counterparts that are boundedly rational, forecast accuracy worsens significantly. When interacting with automated households, updating firms’ prices respond modestly to monetary policy and significantly to anticipated marginal costs and future prices. The greatest deviations in behavior from theoretical predictions arise from human households (HH). Households persistent oversupply of labor and under-consumption is attributed to precautionary saving and debt aversion. The results provide evidence that the effects of monetary policy on decision making hinge on the distribution of indebtedness of households.

The author present causal evidence of the effects of potential bounded rationality on agents’ consumption and labor decisions.

The purpose of this study is to contrast the forecasting performance of two non-linear models, a regime-switching vector autoregressive model (RS-VAR) and a recurrent neural network (RNN), to that of a linear benchmark VAR model. Our specific forecasting experiment is U.K. inflation and we utilize monthly data from 1969 to 2003. The RS-VAR and the RNN perform approximately on par over both monthly and annual forecast horizons. Both non-linear models perform significantly better than the VAR model.