Maximum likelihood estimation of structural VARFIMA models

This paper considers the maximum likelihood estimation of a class of structural vector autoregressive fractionally integrated moving-average (VARFIMA) models. The structural VARFIMA model includes the fractional cointegration model as one of its special cases. We show that the conditional likelihood Durbin–Levinson (CLDL) algorithm of Tsay (2010a) is a fast and reliable approach to estimate the long-run effects as well as the short- and long-term dynamics of a structural VARFIMA process simultaneously. In particular, the computational cost of the CLDL algorithm is much lower than that proposed in Sowell (1989) and Dueker and Startz (1998). We apply the CLDL method to the Congressional approval data of Durr et al. (1997) and find that the long-run effect of economic expectations on Congressional approval is at least 0.5718, which is over twice the estimate of 0.24 found in Table 2 of Box-Steffensmeier and Tomlinson (2000). This paper also tests the divided party government hypothesis with the CLDL algorithm.     

Some evidence of congressional political markets in DOD personnel allocations across states

An empirical analysis of the distribution of Department of Defense (DOD) personnel (both military and civilian) across states is developed with a commonly used Public Choice model of resource allocation in a legislative setting. The model specification employs Congressional seniority, Congressional committee representation, Presidential electoral votes, and the per capita dollar value of prime defense contract awards to explain the variation of DOD personnel across states over time. The empirical analysis is performed over the last three decades, and the results indicate that this particular Public Choice legislative model performs well in explaining the variation in DOD personnel allocations across states. The results also provide some limited evidence of a possible political market between the states for DOD personnel allocations. This analysis has implications for future testing of whether the Base Realignment and Closure Commission (established in 1988 under President Reagan), and the Defense Base Closure and Realignment Commission of 1990 (under President Bush) were effective in significantly decreasing the legislative politics involved in the selection and approval process for base closure and realignment.     

Modeling Equilibrium Relationships: Error Correction Models with Strongly Autoregressive Data

Political scientists often argue that political processes movetogether in the long run. Examples include partisanship andgovernment approval, conflict and cooperation among countries,public policy sentiment and policy activity, economic evaluationsand economic conditions, and taxing and spending. Error correctionmodels and cointegrating relationships are often used to characterizethese equilibrium relationships and to test hypotheses aboutpolitical change. Typically the techniques used to estimateequilibrium relationships are based on the statistical assumptionthat the processes have permanent memory, implying that politicalexperiences cumulate. Yet many analysts have argued that thisis not a reasonable theoretical or statistical assumption formost political time series. In this paper I examine the consequencesof assuming permanent memory when data have long but not permanentmemory. I focus on two commonly used estimators: the Engle-Grangertwo-step estimator and generalized error correction. In my analysisI consider the important role of simultaneity and discuss implicationsfor the conclusions political scientists have drawn about thenature, even the existence, of equilibrium relationships betweenpolitical processes. I find that even small violations of thepermanent memory assumption can present substantial problemsfor inference on long-run relationships in situations that arelikely to be common in applied work in all fields and suggestways that analysts should proceed.     

Whom do you trust? An analysis of executive and congressional economic forecasts

Economic forecasts play an increasing role in U.S. budgetary and fiscal policies. This paper analyzes the accuracy and bias of economic forecasts prepared by the Executive Branch and Congress. Short-run forecasts by the Executive Branch for the forthcoming year do not appear biased. They are as accurate as private forecasts and the forecasts of the Congressional Budget Office (CBO); they are not sensitive to political factors such as the size of the deficit. At the same time, the accuracy of Executive branch's short-run economic forecasts has not improved over the period from 1962 to 1984, and the forecasts themselves can be well approximated by simple, single-equation models that use a small number of variables. In contrast to short-run forecasts, long-run economic forecasts of both the Executive branch and CBO are consistently optimistic. The degree of optimism increases with the length of the forecasting horizon and the degree of fiscal pressure the year in which they are issued. The Reagan Administration's long-run forecasts, while typically optimistic, have not been rosier than those issued under previous administrations.     

Do White In-group Processes Matter,Too? White Racial Identity and Support for Black Political Candidates

Scholars find that negative evaluations of Blacks lead Whites to vote against Black political candidates. However, can an in-group psychological process have the same effect? We consider White racial identity to be a strong candidate for such a process. We argue that the mere presence of a Black candidate cues the identity, reducing support for these candidates among Whites. We test this hypothesis on vote choice in seven instances. Five of them involve simple vote choice models: the 2008 and 2012 Presidential elections, and three elections in 2010: The Massachusetts Gubernatorial election, Black candidates for the US House, and Black candidates for the US Senate. The other two are tests of the notion that White racial identity reduced President Obama’s approval, thus reducing support for all Democratic Congressional candidates in the 2010 Midterm and 2012 Congressional elections. We find support for these notions in all seven cases, across these seven elections, using four different survey research datasets, and four different measures of White identity. Comparisons with other presidential elections show that White identity did not significantly affect mono-racial elections. Furthermore, we find the White identity and racial resentment results to be very similar in terms of their robustness and apparent effect sizes. This indicates in-group evaluations, and those that focus on out-groups, operate independently of one another.     

Prospects For Monitored Retrievable Storage Of High-Level Nuclear Waste

The Nuclear Waste Policy Act of 1982 authorized the U.S. Department of Energy (DOE) to evaluate monitored retrievable storage (MRS) as an option to manage spent fuel and high-level radioactive waste. In 1987, the DOE sought Congressional approval for construction of an MRS at a site identified in Tennessee, despite adamant opposition from the State. An amendment in late 1987 nullified DOE's proposal, and established the MRS Commission and the High-Level Nuclear Waste Negotiator, institutions created as a result of the DOE/Tennessee dispute. This article analyzes DOE's siting attempt in Tennessee, the actions of the Commission and Negotiator, and examines the prospects for implementation of nuclear waste policy.     

Evaluating partisan gains from Congressional gerrymandering: Using computer simulations to estimate the effect of gerrymandering in the U.S. House

What is the effect of gerrymandering on the partisan outcomes of United States Congressional elections? A major challenge to answering this question is in determining the outcomes that would have resulted in the absence of gerrymandering. Since we only observe Congressional elections where the districts have potentially been gerrymandered, we lack a non-gerrymandered counterfactual that would allow us to isolate its true effect. To overcome this challenge, we conduct computer simulations of the districting process to redraw the boundaries of Congressional districts without partisan intent. By estimating the outcomes of these non-gerrymandered districts, we are able to establish the non-gerrymandered counterfactual against which the actual outcomes can be compared. The analysis reveals that while Republican and Democratic gerrymandering affects the partisan outcomes of Congressional elections in some states, the net effect across the states is modest, creating no more than one new Republican seat in Congress. Therefore, the partisan composition of Congress can mostly be explained by non-partisan districting, suggesting that much of the electoral bias in Congressional elections is caused by factors other than partisan intent in the districting process.     

Taking Time Seriously

Dramatic world change has stimulated interest in research questions about the dynamics of politics. We have seen increases in the number of time series data sets and the length of typical time series. But three shortcomings are prevalent in published time series analysis. First, analysts often estimate models without testing restrictions implied by their specification. Second, researchers link the theoretical concept of equilibrium with cointegration and error correction models. Third, analysts often do a poor job of interpreting results. The consequences include weak connections between theory and tests, biased estimates, and incorrect inferences. We outline techniques for estimating linear dynamic regressions with stationary data and weakly exogenous regressors. We recommend analysts (1) start with general dynamic models and test restrictions before adopting a particular specification and (2) use the wide array of information available from dynamic specifications. We illustrate this strategy with data on Congressional approval and tax rates across OECD countries.     

Beyond the Unit Root Question: Uncertainty and Inference

A fundamental challenge facing applied time-series analysts is how to draw inferences about long-run relationships (LRR) when we are uncertain whether the data contain unit roots. Unit root tests are notoriously unreliable and often leave analysts uncertain, but popular extant methods hinge on correct classification. Webb, Linn, and Lebo (WLL; 2019) develop a framework for inference based on critical value bounds for hypothesis tests on the long-run multiplier (LRM) that eschews unit root tests and incorporates the uncertainty inherent in identifying the dynamic properties of the data into inferences about LRRs. We show how the WLL bounds procedure can be applied to any fully specified regression model to solve this fundamental challenge, extend the results of WLL by presenting a general set of critical value bounds to be used in applied work, and demonstrate the empirical relevance of the LRM bounds procedure in two applications.     

Strong feelings: Emotional responses to presidents

Although rational models of political behavior prevail in research on public approval of presidents, an emotions model is proposed to consider whether it actually predicts approval as well as, if not better than, the existing rational models. The emotions model is tested for Presidents Carter and Reagan using the American National Election Studies from 1980 to 1986. The model is compared with two rational models: one, an events and conditions model, suggests that people's prospective and retrospective views of their own financial circumstances and the economy dictate their approval of presidential performance; a second, an issue proximity model, proposes that the closeness (or distance) people perceive between their views on key issues and those of the president affects their approval of the president's job. Across numerous tests, the results indicate that the emotions model outperforms either of the rational models. In addition, a multivariate analysis combining rational and emotional indicators shows the strength of the emotions variables. The study concludes that political science research must more fully consider rational and emotional explanations of political behavior.     

Choosing the path of austerity: how parties and policy coalitions influence welfare state retrenchment in periods of fiscal consolidation

What are the conditions under which some austerity programmes rely on substantial cuts to social spending? More specifically, do the partisan complexion and the type of government condition the extent to which austerity policies imply welfare state retrenchment? This article demonstrates that large budget consolidations tend to be associated with welfare state retrenchment. The findings support a partisan and a politico-institutionalist argument: (i) in periods of fiscal consolidation, welfare state retrenchment tends to be more pronounced under left-wing governments; (ii) since welfare state retrenchment is electorally and politically risky, it also tends to be more pronounced when pursued by a broad pro-reform coalition government. Therefore, the article shows that during budget consolidations implemented by left-wing broad coalition governments, welfare state retrenchment is greatest. Using long-run multipliers from autoregressive distributed lag models on 17 OECD countries during the 1982–2009 period, substantial support is found for these expectations.     

Estimating property tax base elasticity over time: Evidence on the revenue maximizing politician

The Laffer curve analysis suggests a possible policy conflict between short-run revenue maximization and long-run fiscal health. This paper estimates short-run and long-run property tax base elasticity in order to test whether such a conflict exists for the property tax in central cities. A stock adjustment model is used and separate time-series estimates for four New York State central cities lend empirical support to such a conflict. The results show that while disincentive effects associated with property tax rate increases are not strong enough to reduce proeprty tax revenue in the short-run, they are substantial enough to reduce long-run revenue in all but one city. The paper also tests for asymmetric response to property tax rate changes and finds significant results for only one city.     

A political economic analysis of congressional voting patterns on NAFTA

This paper delves into the question of the determinants of Congressional voting on NAFTA. It uses a logit model to examine both House and Senate votes and is able to use district specific estimates in the former. It finds only very limited support for the thesis that narrowly defined employment gainers and losers were important determinants of Congressional voting patterns, though some substantial support that districts that were highly agricultural or already possessed a substantial Hispanic population and skilled labor force and had the most to gain from the general effects of NAFTA lead to a positive effect on the probability that a member of the House would vote affirmatively. Contributions from labor unions lowered the likelihood of affirmative votes. Political partisanship and ideological positions apparently had little effect on the votes. A Representative's political capital had a marginally significant effect on the probability on an affirmative vote, but it performed positively in the Senate and negatively in the House.     

Congress,the Supreme Court,and Judicial Review: Testing a Constitutional Separation of Powers Model

Recent scholarship suggests that the U.S. Supreme Court might be constrained by Congress in constitutional cases. We suggest two potential paths to Congressional influence on the Court's constitutional decisions: a rational‐anticipation model, in which the Court moves away from its preferences in order to avoid being overruled, and an institutional‐maintenance model, in which the Court protects itself against Congressional attacks to its institutional prerogatives by scaling back its striking of laws when the distance between the Court and Congress increases. We test these models by using Common Space scores and the original roll‐call votes to estimate support in the current Congress for the original legislation and the Court's preferences over that legislation. We find that the Court does not appear to consider the likelihood of override in constitutional cases, but it does back away from striking laws when it is ideologically distant from Congress.     

Economic Outcomes and Voting Behaviour in a Multi-Party System: An Application to the Netherlands

This paper is an empirical study to the effects of economic outcomes on party choice for the Netherlands. In the first part of the paper we employ a multinomial logit model to examine the links between voters' characteristics and party choice. The results suggest that there are long-run movements in party choice which are unlikely be the result of changing economic outcomes. In the second part, we use time series analysis to determine the effects of economic conditions on short-run and medium-run movements in votes shares. The estimations results provide support for the responsibility hypothesis and for the predictions of the partisan voter model that left-wing (right-wing) parties benefit (suffer) from favourable economic growth prospects.     

Further evidence on the relationship between economic freedom and economic growth

Much of the literature on the power of elected officials and bureaucratic agencies argues, from an empirical perspective, that bureaus appears to exercise autonomy. In this paper, a theoretical model sets out the conditions under which the Congress, the President, and one agency (we use the U.S. Federal Reserve as an extended example) can dictate policy outcomes. The results of the paper include the “Congressional Dominance” theorem: If more than 2/3 of House members, and more than 2/3 of Senate members, agree on something, they get it. The theorem is obvious (the “proof” is in the U.S. Constitution), but often forgotten in the substantive literature. More realistic results are derived for situations where the preferences of members of Congress are more diverse. Powers of the President to influence policy with, and without, appointments are also analyzed.     

Institutional quality and economic growth: Evidence from South-Asian countries

The current study investigates the relationship between institutional quality and economic growth, focusing on South-Asian countries entailing Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka, from 2002 to 2018. The data are analysed using the dynamic heterogeneous panel (panel autoregressive distributed lag [ARDL] model) approach, specifically the dynamic fixed effect (DFE), mean group (MG), and pooled mean group (PMG). Based on the findings, the three governance indicators, namely corruption control, accountability, and the rule of law, positively and significantly affect economic growth. All the nations have consistent long-run estimates but varied short-run estimates and adjustment speed for the long-term equilibrium. This is due to governance volatility that is evident in all the nations. This article offers both practical and theoretical contributions. This article contributes to the institutional quality and economic growth literature from a theoretical perspective from the South-Asian perspective. From a practical perspective, the study findings are significant for policymakers, particularly those from the countries that demonstrate major fiscal and external imbalance due to war and terrorism, low oil prices and weak trade. Hence, there is a pressing need to address economic growth issues instigated by the policymakers' negligence to ensure appropriate governance and macroeconomic management. Regulators can improve economic growth through national and regional image building by developing a stable economic and political landscape and maintaining macroeconomic stability by improving the institutional quality indicators. There is evidence that institutional quality improvement leads to better economic growth.     

How to Analyze Political Attention with Minimal Assumptions and Costs

Previous methods of analyzing the substance of political attention have had to make several restrictive assumptions or been prohibitively costly when applied to large-scale political texts. Here, we describe a topic model for legislative speech, a statistical learning model that uses word choices to infer topical categories covered in a set of speeches and to identify the topic of specific speeches. Our method estimates, rather than assumes, the substance of topics, the keywords that identify topics, and the hierarchical nesting of topics. We use the topic model to examine the agenda in the U.S. Senate from 1997 to 2004. Using a new database of over 118,000 speeches (70,000,000 words) from the Congressional Record , our model reveals speech topic categories that are both distinctive and meaningfully interrelated and a richer view of democratic agenda dynamics than had previously been possible.