We develop a theory of low-frequency movements in inflation expectations, and use it to interpret joint dynamics of inflation and inflation expectations for the United States and other countries over the post-war period. In our theory long-run inflation expectations are endogenous. They are driven by short-run inflation surprises, in a way that depends on recent forecasting performance and monetary policy. This distinguishes our theory from common explanations of low-frequency properties of inflation.  The model, estimated using only inflation and short-term forecasts from professional surveys, accurately predicts observed measures of long-term inflation expectations and identifies episodes of unanchored expectations.

Sequential learning problems are common in several fields of research and practical applications. Examples include dynamic pricing and assortment, design of auctions and incentives and permeate a large number of sequential treatment experiments. In this essay, we extend one of the most popular learning solutions, the t-greedy heuristics, to high-dimensional contexts considering a conservative directive. We do this by allocating part of the time the original rule uses to adopt completely new actions to a more focused search in a restrictive set of promising actions. The resulting rule might be useful for practical applications that still values surprises, although at a decreasing rate, while also has restrictions on the adoption of unusual actions. With high probability, we find reasonable bounds for the cumulative regret of a conservative high-dimensional decaying t-greedy rule. Also, we provide a lower bound for the cardinality of the set of viable actions that implies in an improved regret bound for the conservative version when compared to its non-conservative counterpart. Additionally, we show that end-users have sufficient flexibility when establishing how much safety they want, since it can be tuned without impacting theoretical properties. We illustrate our proposal both in a simulation exercise and using a real dataset. The second essay studies deterministic treatment effects when the assignment rule is both more complex than traditional ones and unknown to the public perhaps, among many possible causes, due to ethical reasons, to avoid data manipulation or unnecessary competition. More specifically, sticking to the well-known sharp RDD methodology, we circumvent the lack of knowledge of true cutoffs by employing a forest of classification trees which also uses sequential learning, as in the last essay, to guarantee that, asymptotically, the true unknown assignment rule is correctly identified. The tree structure also turns out to be suitable if the program’s rule is more sophisticated than traditional univariate ones. Motivated by real world examples, we show in this essay that, with high probability and based on reasonable assumptions, it is possible to consistently estimate treatment effects under this setup. For practical implementation we propose an algorithm that not only sheds light on the previously unknown assignment rule but also is capable to robustly estimate treatment effects regarding different specifications imputed by end-users. Moreover, we exemplify the benefits of our methodology by employing it on part of the Chilean P900 school assistance program, which proves to be suitable for our framework.

We characterize jump dynamics in stock market returns using a novel series of intraday prices covering over 80 years. Jump dynamics vary substantially over time. Trends in jump activity relate to secular shifts in the nature of news. Unscheduled news often involving major wars drives jump activity in early decades, whereas scheduled news and especially news pertaining to monetary policy drives jump activity in recent decades. Jump variation measures forecast excess stock market returns, consistent with theory. Results support models featuring a separate jump factor such that risk premium dynamics are not fully captured by volatility state variables

In third generation currency crises models, balance sheet losses from currency depreciations propagate the crises into the real sector of the economy. To test these models, we built a firm-level database that allowed us to measure currency mismatches around the 2002 Brazilian currency crisis. We found that between 2001 and 2003, firms with large currency mismatches just before the crisis reduced their investment rates 8.1 percentage points more than other publicly held firms. We also showed that the currency depreciation increased exporters revenue, but those with currency mismatches reduced investments 12.5 percentage points more than other exporters. These estimated reductions in investment are economically very significant, underscoring the importance of negative balance sheet effects in currency crises.

It is widely known that Google Trends has become one of the most popular free tools used by forecasters both in academics and in the private and public sectors. There are many papers, from several different fields, concluding that Google Trends improve forecasts’ accuracy. However, what seems to be widely unknown, is that each sample of Google search data is different from the other, even if you set the same search term, data and location. This means that it is possible to find arbitrary conclusions merely by chance. This paper aims to show why and when it can become a problem and how to overcome this obstacle.

Factor and sparse models are two widely used methods to impose a low-dimensional structure in high dimension. They are seemingly mutually exclusive. In this paper, we propose a simple lifting method that combines the merits of these two models in a supervised learning methodology that allows to efficiently explore all the information in high-dimensional datasets. The method is based on a very flexible linear model for panel data, called factor-augmented regression model with both observable, latent common factors, as well as idiosyncratic components as high-dimensional covariate variables. This model not only includes both factor regression and sparse regression as specific models but also significantly weakens the cross-sectional dependence and hence facilitates model selection and interpretability. The methodology consists of three steps. At each step, remaining cross-section dependence can be inferred by a novel test for covariance structure in high-dimensions. We developed asymptotic theory for the factoraugmented sparse regression model and demonstrated the validity of the multiplier bootstrap for testing high-dimensional covariance structure. This is further extended to testing highdimensional partial covariance structures. The theory and methods are further supported by an extensive simulation study and applications to the construction of a partial covariance network of the financial returns for the constituents of the S&P500 index and prediction exercise for a large panel of macroeconomic time series from FRED-MD database

This thesis is composed by two chapters. The first chapter shows that the presence of lead-lag effects in the US equity market is a broader phenomenon than previously found in the literature and is associated with the existence of a strong one-day factor momentum. Lead-lag effects are present whenever stocks are exposed to the same common risk factor, holding for almost 100 factors on a daily frequency. This phenomenon is not explained by the previously reported industry, large-cap to small-cap and other lead-lag effects. One-day factor momentum is directly related to the existence of factor-based stock cross-autocovariance and is present both in the cross-section and the time series. One-day factor momentum is profitable after trading costs and does not present crashes. One-month factor momentum is subsumed by one-day factor momentum with negative alpha in spanning tests. The relevance of the one-day effect is confirmed with machine learning techniques. Short-term reversals in stocks also become stronger after we control for this factor-based cross-autocovariance pattern. The second chapter shows how factor momentum impacts the performance of standard short-term single-equity reversal strategies in the US equity market. Significant benefits in performance can be achieved if the effects of factor momentum is considered in the construction of reversal strategies. Standard short-term reversal strategies have a negative exposure to factor momentum since they sell winner stocks that on average are more exposed to the winner factors and buy loser stocks that on average are more exposed to loser factors. The best way to neutralize this effect that drags down short-term reversal performance is to hedge stocks exposures simultaneously to a very large set of factors. For instance, hedging only with the 3 Fama-French factors does not eliminate the exposure to factor momentum. Sorting stocks using residual returns is not as efficient as sorting on total returns as it does not completely neutralize the negative exposure to factor momentum. We propose a fully-hedged reversal strategy that, differently from conventional short-term reversal strategies, is profitable after trading costs, that do not present crashes, that has Sharpe ratio 2.5 times higher than the conventional reversal strategies and that is profitable even if we restrict our sample to only large-cap stocks.

Literature shows that short-term reversal strategies in equity markets
can be interpreted as a proxy for liquidity provision. This study examines
whether the short-term reversal strategy bears similar results in Brazil, a way
less developed and illiquid market. On the developed American stock markets,
the expected return of such a strategy appears to be lucrative, strongly timevarying and highly predictable with liquidity and fear indexes such as the VIX
index. In the Brazilian, more volatile market, the profitability of such a strategy
seem to have reduced in the latest years, and contrary to expectations, the
EMBI+ Brazil is the only index with predictive power over such returns while
the VIX-EWZ and the Ivol-BR, both proxies to what would be a Brazilian VIX
have no predictive power. The expected returns of providing liquidity seem to
rise in times of increased risk perception, which indicates that withdrawal of
liquidity supply, translated in an increase in the expected returns from liquidity
provision, is one of the drivers behind the evaporation of liquidity in times of
market turmoil even in developing countries.  

n order to elucidate the revival of religion in the Muslim countries, we must not only understand the spread of puritan interpretations of the faith (as they have permeated movements running from Muslim Brothers to violent Islamist movements), but also comprehend the process whereby these more radical ideologies have been accommodated by the (autocratic) Muslim states. This necessitates that we explore the internal political economy of Muslim countries without neglecting the possible influence of international forces. The theory used as a background to the present work has precisely allowed us to achieve that objective. It works out the strategic interactions between three key players: the ruler, the clerics, and the military, in the context of an autocracy; moreover, it highlights the channels through which external events and shocks make themselves felt through the local political and social fabrics. Among these shocks, military defeats by powers considered as imperialist, and the declaration of a world war against terrorism seem to have played a more important role than the end of the Cold War.