Heterogeneous causal effects of neighborhood policing in New York City with staggered adoption of the policy

Communities often self select into implementing a regulatory policy, and adopt the policy at different time points. In New York City, neighborhood policing was adopted at the police precinct level over the years 2015-2018, and it is of interest to both (1) evaluate the impact of the policy, and (2) understand what types of communities are most impacted by the policy, raising questions of heterogeneous treatment effects. We develop novel statistical approaches that are robust to unmeasured confounding bias to study the causal effect of policies implemented at the community level. Using techniques from high-dimensional Bayesian time-series modeling, we estimate treatment effects by predicting counterfactual values of what would have happened in the absence of neighborhood policing. We couple the posterior predictive distribution of the treatment effect with flexible modeling to identify how the impact of the policy varies across time and community characteristics. Using pre-treatment data from New York City, we show our approach produces unbiased estimates of treatment effects with valid measures of uncertainty. Lastly, we find that neighborhood policing decreases discretionary arrests, but has little effect on crime or racial disparities in arrest rates

Forecasting the Prices of Cryptocurrencies using a Novel Parameter Optimization of VARIMA Models

This work is a comparative study of different univariate and multivariate time series predictive models as applied to Bitcoin, other cryptocurrencies, and other related financial time series data. ARIMA models, long regarded as the gold standard of univariate financial time series prediction due to both its flexibility and simplicity, are used a baseline for prediction. Given the highly correlative nature amongst different cryptocurrencies, this work aims to show the benefit of forecasting with multivariate time series models—primarily focusing on a novel parameter optimization of VARIMA models outlined in this paper. These models are trained on 3 years of historical data, aggregated from different cryptocurrency exchanges by Coinmarketcap.com, which includes: daily average prices and trading volume. Historical time series data of traditional market data, including the stock Nvidia, the de facto leading manufacture of gaming GPU’s, is also analyzed in conjunction with cryptocurrency prices, as gaming GPU’s have played a significant role in solving the profitable SHA256 hashing problems associated with cryptocurrency mining and have seen equivalently correlated investor attention as a result. Models are trained on this historical data using moving window subsets, with window lengths of 100, 200, and 300 days and forecasting 1 day into the future. Validation of this prediction against the actually price from that day are done with following metrics: Directional Forecasting (DF), Mean Absolute Error (MAE), and Mean Squared Error (MSE)

Generalised Network Autoregressive Processes and the GNAR package

This article introduces the GNAR package, which fits, predicts, and simulates from a powerful new class of generalised network autoregressive processes. Such processes consist of a multivariate time series along with a real, or inferred, network that provides information about inter-variable relationships. The GNAR model relates values of a time series for a given variable and time to earlier values of the same variable and of neighbouring variables, with inclusion controlled by the network structure. The GNAR package is designed to fit this new model, while working with standard ts objects and the igraph package for ease of use

Model Selection Methods for Panel Vector Autoregressive Models

Multi-country dynamic time series models, called panel vector autoregressive (PVAR) models, allow for multilateral cross-border linkages and country-specific dependencies among variables. Thus, these models are excellent tools for macroeconomic spillover analyses. However, as they jointly model multiple variables of several countries, the dimensionality of unrestricted PVAR models is large and the estimation feasibility is thus not guaranteed with standard methods. Hence, model selection techniques which restrict PVAR models and thereby reduce the dimensionality of the models are necessary to ensure the estimation feasibility. Chapters 1 and 2 of this thesis propose Bayesian and classical selection methods for PVAR models which search for restrictions supported by the data and which take specific panel properties into account. Furthermore, theoretical arguments for commonly used recursive structural identification in multi-country models are often insufficient. The third chapter analyzes international monetary policy spillovers in a three-country vector autoregressive model using external instruments to identify monetary policy shocks. The first chapter introduces a Bayesian selection prior for PVAR models. The proposed selection prior allows for a data-based restrictions search ensuring the estimation feasibility. The prior is specified as a mixture distribution which allows to shrink parameters to restrictions or to estimate them freely. The prior specification differentiates between domestic and foreign variables by searching for zero restrictions on lagged foreign variables and for homogeneity across countries for coefficients of domestic variables. The prior, thereby, allows for a flexible panel structure and a restrictions search on single elements. Furthermore, the prior searches for restrictions on the covariance matrix. A Monte Carlo simulation shows that the selection prior outperforms alternative estimators for flexible panel structures in terms of mean squared errors measuring the deviation of the parameter estimates from the true values. Furthermore, a forecast exercise for G7 countries demonstrates that forecast performance improves for the proposed prior focusing on sparsity in form of no dynamic interdependencies. The second chapter proposes a new lasso (least absolute shrinkage and selection operator) for estimating PVAR models. The penalized regression ensures the feasibility of the estimation by specifying a shrinkage penalty that accommodates time series and cross section characteristics. It thereby accounts for the inherent panel structure within the data. Furthermore, using the weighted sum of squared residuals as the loss function enables the lasso for PVAR models to take into account correlations between cross-sectional units in the penalized regression. The specification of the penalty term allows to establish the asymptotic oracle properties. Given large and sparse models, simulation results point towards advantages of using the lasso for PVAR models over ordinary least squares estimation, standard lasso techniques as well as Bayesian estimators in terms of mean squared errors measuring the deviation of the estimates from their true values and forecast accuracy. Empirical forecasting applications with up to ten countries and four variables support these findings. The third chapter assesses the international macroeconomic spillover effects of monetary policy shocks for the United States, the United Kingdom, and the euro area. The Bayesian proxy three-country structural vector autoregressive model accounts for international interdependencies and traces the dynamic cross-border responses of macroeconomic variables to monetary policy shocks identified with external instruments. The instruments for monetary policy surprises capture changes in high frequency government bond future contracts around policy announcement dates. The results provide no evidence for cross-border macroeconomic effects.Dynamische Zeitreihenmodelle für mehrere Länder, genannt Panel vektorautoregressive (PVAR) Modelle, können gleichzeitig multilaterale internationale Abhängigkeiten und länderspezifische Eigenschaften modellieren. Damit eignen sich diese Modelle hervorragend zur Analyse von globalen, makroökonomischen Entwicklungen und grenzüberschreitenden Effekten. PVAR Modelle integrieren Variablen mehrerer Ländern in ein gemeinsames Modell. Somit ist die Dimensionalität der nicht restringierten Modelle so groß, dass diese häufig nicht mehr mit Standardmethoden geschätzt werden können. Um die Schätzbarkeit der Modelle zu garantieren, ist es notwendig, PVAR Modelle mithilfe Methoden der Modellselektion zu beschränken und damit die Dimensionalität der Modelle zu reduzieren. Kapitel 1 und 2 dieser Dissertation führen bayesianische und frequentistische Selektionsmethoden für PVAR Modelle ein, die datenbasierte Restriktionen suchen und dabei spezifische Eigenschaften von Paneldaten berücksichtigen. Darüber hinaus ist die strukturelle Identifizierung von PVAR Modellen aufgrund unzureichender theoretischer Argumente oftmals problematisch. Das dritte Kapitel analysiert internationale Effekte von geldpolitischen Schocks in einem PVAR Modell. Die strukturelle Identifizierung der geldpolitischen Schocks basiert auf externen Instrumenten. Das erste Kapitel führt einen bayesianischen selection prior für PVAR Modelle ein. Die vorgeschlagene a-priori Verteilung ermöglicht eine datenbasierte Suche von Restriktionen, die die Schätzbarkeit des Modells garantieren. Die a-priori Verteilung ist als Mischverteilung spezifiziert, die Parameter gegen Restriktionen schrumpft oder frei schätzt. Die Spezifizierung der a-priori Verteilung unterscheidet zwischen inländischen und ausländischen Variablen, indem nach Null-Restriktionen für ausländische Variablen und Homogenitäten zwischen Ländern für inländische Variablen gesucht wird. Die a-priori Verteilung nimmt somit eine flexible Panelstruktur an und führt eine Restriktionssuche basierend auf einzelnen Variablen durch. Die Ergebnisse von Monte Carlo Simulationen zeigen, dass bei flexibleren Panelstrukturen die mittleren quadratischen Abweichungen der geschätzten Werte vom wahren Wert mit dem vorgeschlagenen selection prior geringer sind als bei alternativen Schätzmethoden. Ebenso demonstriert eine Prognoseanwendung für G7 Länder, dass die Prognosefähigkeit der eingeführten a-priori Verteilung verbessert wird, wenn nach dem Fehlen von dynamischen Abhängigkeiten gesucht wird. Das zweite Kapitel führt einen neuen lasso (least absolute shrinkage and selection operator) zur Schätzung von Panel vektorautoregressiven Modellen ein. Dieser regularisierte Regressionsschätzer gewährleistet die Schätzung, indem eine Beschränkung spezifiziert wird, die sowohl Eigenschaften von Zeitreihen- als auch von Querschnittdaten berücksichtigt. Die in den Daten enthaltene Panelstruktur wird somit erfasst. Außerdem berücksichtigt die Spezifizierung der Verlustfunktion in der regularisierten Schätzung als gewichtete Residuenquadratsumme Korrelationen zwischen den Querschnittseinheiten. Die Spezifizierung der Beschränkung erlaubt es zudem, die asymptotischen Oracle Eigenschaften nachzuweisen. Die Monte Carlo Simulationen mit großen und sparsamen Modellen demonstrieren die Vorteile des lasso für PVAR Modelle gegenüber dem Kleinste-Quadrate-Schätzer, Standardvarianten des lasso und weiteren bayesianischen Schätzmethoden. So minimiert der lasso für PVAR Modelle die mittleren quadratischen Abweichungen der geschätzten Werte von deren wahren Werten und verbessert die Prognosegenauigkeit. Eine empirische Anwendung zur Prognose mit bis zu zehn Ländern und vier Variablen unterstützt die Ergebnisse. Das dritte Kapitel untersucht die internationalen makroökonomischen Effekte von geldpolitischen Schocks für die Vereinigten Staaten, Großbritannien und für den Euroraum. Das verwendete bayesianische Proxy strukturelle vektorautoregressive Modell für die drei Länder kann multilaterale globale Verknüpfungen erfassen und zeichnet die dynamischen grenzüberschreitenden makroökonomischen Auswirkungen von geldpolitischen Schocks nach. Die strukturellen geldpolitischen Schocks werden mit externen Instrumenten identifiziert. Die Instrumente erfassen Veränderungen der hochfrequenten Daten für Futures auf Staatsanleihen an Tagen mit geldpolitischen Ankündigungen. Die Resultate zeigen keine Evidenz für grenzüberschreitende makroökonomische Effekte

Modelling joint autoregressive moving average processes

This thesis explores Joint Autoregressive Moving-Average (JARMA) models for independent replicated univariate time series with common ARMA coefficients whose innovations variances are either in common, unique to each series or vary with the series mean. The constraint of a common variance is also applied to vector ARMA processes. Interleaving is shown to represent replicated series with a common variance as one series from the same process. The time and frequency domain properties of interleaved replicated stationary and invertible processes are established. As an aid to identification, hypothesis tests for comparing series are reviewed and several new tests are presented and explored along with a graphical method for identification. Unconditional maximum likelihood estimates of the parameters of various JARMA processes are derived using the methods of joint likelihood and interleaving. The properties of the estimators are examined using simulation and asymptotics. Finally JARMA models are fitted to over 60 years of daily univariate and bivariate temperature data to estimate differences in level due to location and climate change

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio