Economics Dissertations and Theseshttps://hdl.handle.net/1808/141092024-05-22T05:31:46Z2024-05-22T05:31:46ZThree Essays on Computational Approaches to EconomicsYin, Xunzhaohttps://hdl.handle.net/1808/315192024-01-16T16:42:59Z2019-12-31T00:00:00ZThree Essays on Computational Approaches to Economics
Yin, Xunzhao
The three essays included in this dissertation are on three di?erent popular computational approaches that are widely applicable in economics. In Chapter 1, a state-space model is constructed which is linear in state variables and nonlinear in parameters. From the model, the time-varying level of natural interest rate is estimated using Kalman ?lter and Gibbs sampling algorithms. Chapter 2 proposes a new algorithm, called Implicit Particle Gibbs, to solve nonlinear state-space models. And Chapter 3 reviews recent development of deep learning and reinforcement learning algorithms and their applications in economics.
2019-12-31T00:00:00ZEssays on the Impact of Presidential Elections on the U.S. Stock MarketWang, Fanhttps://hdl.handle.net/1808/315172024-01-16T16:44:30Z2019-12-31T00:00:00ZEssays on the Impact of Presidential Elections on the U.S. Stock Market
Wang, Fan
This dissertation consists of two essays that are organized as chapters. In the first chapter, I examine the effect of presidential elections on the timing of turning points of stock market cycles in the United States. The empirical results from duration analysis show that compared to at other times, a market trough, the end of a bear market, is more likely in the period before an election when the incumbent is a Republican; meanwhile a market peak, the end of a bull market, occurs sooner following a Republican election victory. There is also evidence suggesting that bear markets are less likely to end after an election of a Republican president than in other periods. Results from further examination reveal that political control, the political alignment between the president and Congress, has a vital role in the timing of the turning points relative to the elections. In particular, political control found to reduce the probability of a market trough in the pre-election period and this reduction in the hazards for a bear market prior to an election is more significant for a Democratic president. Alternatively, political control boots the prospect of the completion of a bull market in the post-election period, especially when a Democrat was elected. Finally, political control in Congress can substantially shorten the duration of a bear market in the post-election period when the Republicans control both the White House and Capitol Hill. In the second chapter, I develop a dynamic factor model to examine the relations among presidential elections, investor sentiment, and stock market returns simultaneously. Results in the study uncover that there is a sizeable improvement of investor sentiment prior to an election, and this pre-election upsurge in sentiment can explain a substantial portion of the presidential election cycle effect in the stock returns. However, data in the study fail to provide significant evidence that Democratic presidents can install more optimism in the stock market. My result does confirm that there was a market-wide panic during the height of the recent financial crisis in iv 2007-2008. Furthermore, results from the asset pricing tests show that in addition to the conventional risk factors, the market return factor and the factor of change in investment opportunity set, the factor of investor sentiment is a critical component in asset pricing and prediction. By including the sentiment factor, the proposed Augmented Intertemporal Capital Asset Pricing Model (AICAPM) in the paper improves upon the explanatory and predictive power of other competing models such as the Intertemporal Capital Asset Pricing Model of Merton (1973) (ICAPM) and the Fama-French (1993) 3-factor model (FF3).
2019-12-31T00:00:00ZDivisia Monetary Aggregates and Exchange Rate ForecastingMolinas, Luishttps://hdl.handle.net/1808/315082021-03-05T16:53:56Z2019-12-31T00:00:00ZDivisia Monetary Aggregates and Exchange Rate Forecasting
Molinas, Luis
Divisia monetary aggregates have been shown to be an improvement on the simple-sum monetary aggregates used by policy makers in the great majority of central banks in the world. Since Barnett (1978, 1980) derived the User Cost Price and produced the theoretically correct from of aggregation, they have helped solve some of the diﬃcult problems in the profession. One such problem is forecasting exchange rates. SinceMeese & Rogoﬀ (1983) convincingly argued that no model could outperform a driftless random walk in predicting exchange rates, there have been many papers which have tried to ﬁnd some forecasting methodology that could beat the random walk, at least for certain forecasting periods. In particular, Wright (2008) introduced Bayesian Model Averaging as a tool to forecast exchange rates and Lam et al. (2008) compared Bayesian Model Averaging and three structural models to a benchmark model (the random walk), both studies obtaining positive results. Also, Carriero et al (2009) found positive results using a Bayesian Vector Auto-regression model. Barnett & Kwag (2005) availed themselves of the User Cost Price and Divisia monetary aggregatesandincludedthemasvariablesintheFlexiblePriceMonetarymodel, Sticky Price and Hooper Morton models to show that it has greater forecasting power than the random walk when the aforementioned variables replace the interest rate and simple sum monetary aggregates (respectively). Speciﬁcally, they worked with the US dollar/British pound exchange rate. This dissertation aims to extend three diﬀerent experiments. The ﬁrst chapter compares Purchasing Power Parity, Uncovered Interest Rate, Sticky Price, Bayesian Model Averaging, and Bayesian Vector Auto-regression models to the random walk benchmark in forecasting exchange rates between the Paraguayan Guarani and the US Dollar, the Brazilian Real and the Argentinian Peso. The second, follows Barnett and Kwag’s work, applied to the US dollar/Euro exchange rate, but also includes an Uncovered Interest-rate Parity model. I use the Root Mean Square Error, Direction of Change statistic, and the Diebold-Mariano statistic to compare the forecasting power of the models in Chapters 1 and 2. In the ﬁrst chapter, resultsindicatethatinshorterforecastinghorizonsBayesianModelAveraging,and Bayesian Vector Auto-regression models perform better than the random walk and that structural models outperform the random walk at longer horizons. In the second chapter, results indicate that Uncovered Interest-rate Parity with the User Cost Price instead of the interest rate improves on the random walk forecasts in every time horizon. In view of the results in Chapter2, Divisia monetary aggregates for the country of Paraguay are calculated in the last chapter with the aim to examine their performance against simple-sum monetary aggregates in estimating money demand. Results suggest that Divisia monetary aggregates are superior to simple-sum aggregates in money demand estimation.
2019-12-31T00:00:00ZThree Essays in Applied Microeconomics: Medicaid Expansion, CEMENT Coauthorship Networks, and Occupational LicensingNa, Rinahttps://hdl.handle.net/1808/313522021-03-05T16:53:01Z2019-08-31T00:00:00ZThree Essays in Applied Microeconomics: Medicaid Expansion, CEMENT Coauthorship Networks, and Occupational Licensing
Na, Rina
ABSTRACT This dissertation includes three essays in applied microeconomics, which is a fundamental outward-looking branch of economics that applies both economics theories and methodologies to actual questions of individual behavior and societal outcomes. The three essays are focusing on real world topics of the ACA’s Medicaid expansion, female economists’ collaboration networks and occupations licensing. In the first essay, using the restricted NHANES data from 2007 to 2014, effects of the ACA’s Medicaid expansion on three public health measures are examined by comparing expansion states with non-expansion states. The results show that the Medicaid expansion in 2014 decreases the systolic blood pressure and increases the usage of cholesterol lowering medication, however, no significant effects on diabetes measures. It is also confirmed that the ACA’s Medicaid expansion increases the total health and Medicaid coverage. In the second essay, a unique randomized control trial of CEMENT workshop is examined to investigate its effect on female economists’ collaboration networks. The CEMENT workshop provides a particular opportunity to observe female economists’ career accomplishments and research productivity in the program. The results show that the participating female economics scholars publish about one more paper and have about 0.5 more numbers of unique coauthors on average, comparing to the control group. The CEMENT workshop helps the treated female economists improve their research productivity and expand the magnitude of their collaboration networks. The last essay studies the effects of occupational licensing on non-U.S. citizen’s labor market outcomes, using the monthly CPS Job Certification data from 2015 to 2019. Non-U.S. citizens are found to be less likely to have job certificates or licenses. Compared to licensed U.S. natives, non-U.S. citizens are still suffering from a wage penalty even if with job certificates or licenses.
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