Markets do not always efficiently incorporate news, particularly when information is complex or ambiguous. Traditional text analysis methods fail to capture the economic structure of information and its firm-specific implications. We propose a novel methodology that guides LLMs to systematically identify and classify firm-specific economic shocks in news articles according to their type, magnitude, and direction. This economically-informed classification allows for a more nuanced understanding of how markets process complex information. Using a simple trading strategy, we demonstrate that our LLM-based classification significantly outperforms a benchmark based on clustering vector embeddings, generating consistent profits out-of-sample while maintaining transparent and durable trading signals. The results suggest that LLMs, when properly guided by economic frameworks, can effectively identify persistent patterns in how markets react to different types of firm-specific news. Our findings contribute to understanding market efficiency and information processing, while offering a promising new tool for analyzing financial narratives.

The EM principle implies the moments underlying the information matrix test for multivariate Markov switching autoregressive models with covariate-dependent transition probabilities are the smoothed values of the moments we would test were the latent Markov chain observed. Thus, we identify components related to the heteroskedasticity, skewness and kurtosis of the multivariate regression residuals for each of the regimes, the neglected multivariate heteroskedasticity of the generalised residuals for each of the columns of the transition matrix, and a final component that assesses the conditional independence of these generalised residuals and the regression residuals, their squares and cross-products given the observed variables.

We propose a causal framework for applications where the outcome of interest is a unit-specific response to events, which first needs to be measured from the data. We suggest a two-step procedure: first, estimate unit-level event studies (ULES) by comparing pre- and post-event outcomes of each unit to a suitable control group; second, use the ULES in causal analysis. We outline the theoretical conditions under which this two-step procedure produces interpretable results, highlighting the underlying statistical challenges. Our method overcomes the limitations of regression-based approaches prevalent in the empirical literature, allowing for a deeper examination of heterogeneity and dynamic effects. We apply this framework to analyze the impact of childcare provision reform on the magnitude of child penalties in the Netherlands, illustrating its ability to reveal nuanced positive relationships between childcare provision and parental labor supply. In contrast, traditional regression-based analysis delivers negative effects, thereby emphasizing the benefits of our two-step approach.

How substantial are means-tested transfers in the United States? How have these transfers evolved over time, and what is their impact on the income distribution? We use microdata from the Survey of Income and Program Participation to document the scope of the main means-tested programs for households headed by working-age adults. We report key features of these programs, their generosity, and coverage by household income, marital status, and the number and age of children in the household. We also assess the role of the transfer system in reducing income inequality and document its changing magnitude and effects in recent years. Finally, we provide parametric estimates of transfers as a function of income and household characteristics for use in applied work in macroeconomics and public finance.

This paper quantifies the aggregate costs of political connections using a general equilibrium model in which politically connected firms benefit from output subsidies and endogenously spend resources on rent-seeking activities. The model is structurally estimated using rich firm-level data for the Indonesian manufacturing sector and a firm-level measure of political connectedness based on a natural experiment from the authoritarian rule of Suharto at the end of the 1990s. A major innovation is to flexibly identify the distribution of output subsidies from relative total factor productivity (TFPQ) distributions across connected and non-connected firms. While only 1.3% of firms are connected, I find that connections impose large costs, with permanent consumption losses of 7.4% and output losses of 2.7%. 2/3 of costs are driven by too much dispersion in subsidies across connected firms, while 1/3 are driven by an excessive level of subsidies.