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We compare the precision of critical values obtained under conventional sampling-based methods with those obtained using sample order statics computed through draws from a randomized counterfactual based on the null hypothesis. When based on a small number of draws (200), critical values in the extreme left and right tail (0.005 and 0.995) contain a small bias toward failing to reject the null hypothesis which quickly dissipates with additional draws. The precision of randomization-based critical values compares favorably with conventional sampling-based critical values when the number of draws is approximately 7 times the sample size for a basic OLS model using homoskedastic data, but considerably less in models based on clustered standard errors, or the classic Differences-in-Differences. Randomization-based methods dramatically outperform conventional methods for treatment effects in Differences-in-Differences specifications with unbalanced panels and a small number of treated groups.
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Lectures in Macroeconomics: A Capitalist Economy Without Unemployment' provides a systematic account of the principle of aggregate demand based on the work of Polish economist Michal Kalecki, best known as one of the originators of the Keynesian Revolution in macroeconomics.The lectures demonstrate the importance of aggregate demand in determining total output and employment in the capitalist economy. They show how the investment decisions of firms affect economic growth, arguing that due to the unstable nature of investment it is important that the government has a central role in stabilizing the economy.0This English translation of Kazimierz Laski's final work brings up to date fundamental concepts to give a picture of the twenty-first capitalist economy, and the obstacles that must be overcome in bringing it to full employment. It introduces the role of money and finance in the contemporary capitalist economy, as well as the central role of the labour market and wages. The analysis is illustrated with statistics and discussion around the evolution of capitalist economies and the rise of economic inequality since the Second World War, culminating in the 2008 crisis and the economic deflation affecting Europe since that crisis. Lectures in Macroeconomics remarks critically upon the neo-classical approach to economics that has brought about slow economic growth, unemployment, and inequality.
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Economic agents face many different types of economic incentives when making financial and moral decisions. We provide experimental data from a population that uniquely responds to incentives to lie compared to previously studied populations. We conduct a standard 6-sided die rolling lying study within a population that believes that God has knowledge of all their actions. Within this population, we find that those who attend church frequently appear to refrain from lying while those that do not frequently attend church do lie, but do not disguise their lies like more secular populations. We further explain how our data fits into the theoretical work on lying.
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Economic agents face many different types of economic incentives when making financial and moral decisions. We provide experimental data from a population that uniquely responds to incentives to lie compared to previously studied populations. We conduct a standard 6-sided die rolling lying study within a population that believes that God has knowledge of all their actions. Within this population, we find that those who attend church frequently appear to refrain from lying while those that do not frequently attend church do lie, but do not disguise their lies like more secular populations. We further explain how our data fits into the theoretical work on lying.
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We compare the precision of critical values obtained under conventional sampling-based methods with those obtained using sample order statics computed through draws from a randomized counterfactual based on the null hypothesis. When based on a small number of draws (200), critical values in the extreme left and right tail (0.005 and 0.995) contain a small bias toward failing to reject the null hypothesis which quickly dissipates with additional draws. The precision of randomization-based critical values compares favorably with conventional sampling-based critical values when the number of draws is approximately 7 times the sample size for a basic OLS model using homoskedastic data, but considerably less in models based on clustered standard errors, or the classic Differences-in-Differences. Randomization-based methods dramatically outperform conventional methods for treatment effects in Differences-in-Differences specifications with unbalanced panels and a small number of treated groups.
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