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We aim to devise a Ramsey test analysis of actual causation. Our method is to define a strengthened Ramsey test for causal models. Unlike the accounts of Halpern and Pearl ([2005]) and Halpern ([20...

Financial performance as a measuring instrument to know the process of implementing financial resources owned by the company. The Covid-19 pandemic has impacted the banking sector, resulting in poor financing due to debtors' disbursements as a result of the large number of people losing their jobs and difficulties in financing payments. This research aims to analyze the financial performance of Islamic Banks during the Covid-19 pandemic, using records of annual financial statements from 2011 to 2020 through Multiple Linear Regression testing and linearity testing of the model used ramsey test. As a result of this study, the results of the t test found that the Capital Adequacy Ratio (CAR), Operating Costs to Operating Income (BOPO), Financing to Deposit Ratio (FDR) had a positive and significant effect on financial performance (ROA) while Not Performing Financing (NPF) had a negative and insignificant effect on financial performance (ROA). Furthermore, simultaneously capital adequacy ratio (CAR), Operating Costs to Operating Income (BOPO), Financing to Deposit Ratio (FDR) and Not Performing Financing (NPF) significantly influenced the financial performance (ROA) of Sharia banks in Indonesia.

This is an essay on the understanding of disposition concepts, which exploits the Ramsey test semantics of conditionals for a refined conditional analysis of dispositions. I will show that this semantics allows for a more accurate account of disposition concepts than the standard semantics of conditionals by David Lewis. This includes dealing with the counterexamples to the conditional analysis, which are known as finks, antidotes, and mimics.

Purpose: The present study is an attempt to investigate the co-integration relation between daily movement of the three MCX India Commodity indices viz. MCX Agri, MCX Energy and MCX Metals for the period Oct 1, 2014 to Sep 30, 2019. Design; The methodology employed for this purpose includes ARDL and Non Linear ARDL Partial Bounds Co-integration test after incorporating single structural break. For optimal model, AIC criteria has been followed in the study . For detecting serial correlation, we have used BGLM test , ADF for stationarity while for stability CUSUM plots have been used . The linearity has been tested using Ramsey Reset test. Findings: The results of the study as given by Partial ‘F’ Bounds test showed that long run co-integration under the linear ARDL was established for two of our indices MCX Energy and MCX Metals while MCX Agri had co-integration when the NARDL was employed. All the Models could satisfactorily pass all the required pre-requisites in terms of serial correlation, stationarity and stability. For structural break, Dummy was incorporated and was significant in both the linear ARDL Models. The ECM term was negative and significant in all the three models , however speed of adjustment was at a slow rate ranging between 1% - 3 % per period for all models. Research implications: These results assumes importance for policy makers as any important policy announcement for one particular commodity sector is bound to have its repercussions on other sectors in due course of time. We have already seen how agriculture prices of some crops were driven by the crude prices in early 2000 which was commonly called energy-food nexus. Hence a similar situation is likely to emerge if policy makers decide to target any one commodity sector. In fact knowledge of these inter-linkages amongst commodity indices with a slow rate of adjustment may not always be harmful to the policy makers and investors. On the contrary, this may become a good strategy in some situations e.g. by giving stimulus to agriculture, the government would be automatically passing on the benefit to other sectors with a lag thereby avoiding simultaneous stimulus to all the commodity sectors

This article explores conditionals expressing that the antecedent makes a difference for the consequent. A ‘relevantised’ version of the Ramsey Test for conditionals is employed in the context of the classical theory of belief revision. The idea of this test is that the antecedent is relevant to the consequent in the following sense: a conditional is accepted just in case (i) the consequent is accepted if the belief state is revised by the antecedent and (ii) the consequent fails to be accepted if the belief state is revised by the antecedent’s negation. The connective thus defined violates almost all of the traditional principles of conditional logic, but it obeys an interesting logic of its own. The article also gives the logic of an alternative version, the ‘Dependent Ramsey Test,’ according to which a conditional is accepted just in case (i) the consequent is accepted if the belief state is revised by the antecedent and (ii) the consequent is rejected (e.g., its negation is accepted) if the belief state is revised by the antecedent’s negation. This conditional is closely related to David Lewis’s counterfactual analysis of causation.

I propose an account of indicative conditionals that combines features of minimal change semantics and information semantics. As in information semantics, conditionals are interpreted relative to an information state in accordance with the Ramsey test idea: “if p then q” is supported at a state s iff q is supported at the hypothetical state s[p] obtained by restricting s to the p-worlds. However, information states are not modeled as simple sets of worlds, but by means of a Lewisian system of spheres. Worlds in the inner sphere are considered possible; worlds outside of it are ruled out, but to different degrees. In this way, even when a state supports “not p”, it is still possible to suppose p consistently. I argue that this account does better than its predecessors with respect to a set of desiderata concerning inferences with conditionals. In particular, it captures three important facts: (i) that a conditional is logically independent from its antecedent; (ii) that a sequence of antecedents behaves like a single conjunctive antecedent (the import-export equivalence); and (iii) that conditionals restrict the quantification domain of epistemic modals. I also discuss two ways to construe the role of a premise, and propose a generalized notion of entailment that keeps the two apart.

According to Woodward’s (Making things happen. A theory of causal explanation, Oxford University Press, New York, 2003) influential account of explanation, explanations have a counterfactual structure, and explanatory counterfactuals are analysed in terms of causal relations and interventions. In this paper, we provide a formal semantics of explanatory counterfactuals based on a Ramsey Test semantics of conditionals. Like Woodward’s account, our account is guided by causal considerations. Unlike Woodward’s account, it makes no reference to causal graphs and it also covers cases of explanation where interventions are impossible.

In this paper, we analyse actual causation in terms of production. The latter concept is made precise by a strengthened Ramsey Test semantics of conditionals: \(A \gg C\) iff, after suspending judgement about A and C, C is believed in the course of assuming A. This test allows us to (epistemically) verify or falsify that an event brings about another event. Complementing the concept of production by a weak condition of difference-making gives rise to a full-fledged analysis of causation.

The study examines the empirical nexus between poverty and unemployment on economic growth in Nigeria between 1980 and 2016. Auto-Regressive Distributed Lag (ARDL), Bound cointegration testing, and Error Correction Methods (ECM) were used to investigate the link between unemployment, poverty rate, and economic growth in Nigeria. Post estimation tests such as the Jarque-Bera test, Breusch-Pagan, ARCH test, and Ramsey reset test were also adopted in order to validate the research finding. The diagnostic tests further disclosed that the estimated model follows the Ordinary Least Square technique assumptions to attain efficiency and consistency of the model employed. The Jarque-Bera test suggests that residuals for both models are normally distributed, and the Breusch-Godfrey Serial Correlation (LM) test indicates that the hypothesis of no autocorrelation cannot be rejected. Interestingly, the ARDL and ECM results show that unemployment and poverty significantly impact economic growth both in the short and long run. Hence, the study recommended that the Nigeria government should ensure that adequate measures are put in place: Such as investment in education, agricultural sector reform, expansionary fiscal policy, intervention in micro-lending for small scale businesses by the government should be implemented to reduce the level of unemployment and poverty rate both in the short run and long run.

The belief revision literature has largely focussed on the issue of how to revise one's beliefs in the light of information regarding matters of fact. Here we turn to an important but comparatively neglected issue: How might one extend a revision operator to handle conditionals as input? Our approach to this question of 'conditional revision' is distinctive insofar as it abstracts from the controversial details of how to revise by factual sentences. We introduce a 'plug and play' method for uniquely extending any iterated belief revision operator to the conditional case. The flexibility of our approach is achieved by having the result of a conditional revision by a Ramsey Test conditional ('arrow') determined by that of a plain revision by its corresponding material conditional ('hook'). It is shown to satisfy a number of new constraints that are of independent interest., Extended version of a paper accepted to KR 2020

Traditionally, belief change is modelled as the construction of a belief set that satisfies a success condition. The success condition is usually that a specified sentence should be believed (revision) or not believed (contraction). Furthermore, most models of belief change employ a select-and-intersect strategy. This means that a selection is made among primary objects that satisfy the success condition, and the intersection of the selected objects is taken as outcome of the operation. However, the select-and-intersect method is difficult to justify, in particular since the primary objects (usually possible worlds or remainders) are not themselves plausible outcome candidates. Some of the most controversial features of belief change theory, such as recovery and the impossibility of Ramsey test conditionals, are closely connected with the select-and-intersect method. It is proposed that a selection mechanism should instead operate directly on the potential outcomes, and select only one of them. In this way many of the problems that are associated with the select-and-intersect method can be avoided. This model is simpler than previous models in the important Ockhamist sense of doing away with intermediate, cognitively inaccessible objects. However, the role of simplicity as a choice criterion in the direct selection among potential outcomes is left as an open issue.

The well-known formal semantics of conditionals due to Stalnaker (in: Rescher (ed) Studies in logical theory, Blackwell, Oxford, 1968), Lewis (Counterfactuals, Blackwell, Oxford, 1973a), and Gardenfors (in: Niiniluoto, Tuomela (eds) The logic and 1140 epistemology of scientific change, North-Holland, Amsterdam, 1978, Knowledge in flux, MIT Press, Cambridge, 1988) all fail to distinguish between trivially and nontrivially true indicative conditionals. This problem has been addressed by Rott (Erkenntnis 25(3):345–370, 1986) in terms of a strengthened Ramsey Test. In this paper, we refine Rott’s strengthened Ramsey Test and the corresponding analysis of explanatory relations. We show that our final analysis captures the presumed asymmetry between explanans and explanandum much better than Rott’s original analysis.

This paper examines the Presence of Nonlinear Relationship between Natural Gas Consumption and Economic Growth in Nigeria from 1981 to 2015. The Ramsey Reset test,Incremental F-test, and Wald test have been employed to test for non-linearity in the relationship between natural gas consumption and economic growth in Nigeria. The nonlinearity test results revealed that the relationship is nonlinear. However, the results are suggestive of the fact that linear models might not be the appropriate statistical tools for estimating the relationship between natural gas consumption and economic growth in Nigeria. Therefore, the paper recommends that the Nigeria’s policymakers consider taking into cognizance nonlinear modeling techniques as alternative tools for modeling, estimating and forecasting the relationship between natural gas consumption and economic growth in the country.

Vann McGee has proposed a counterexample to the Ramsey Test. In the counterexample, a seemingly trustworthy source has testified that p and that if not-p, then q. If one subsequently learns not-p (and so learns that the source is wrong about p), then one has reason to doubt the trustworthiness of the source (perhaps even the identity of the source) and so, the argument goes, one has reason to doubt the conditional asserted by the source. Since what one learns is that the antecedent of the conditional holds, these doubts are contrary to the Ramsey Test. We argue that the counterexample fails. It rests on a principle of testimonial dependence that is not applicable when a source hedges his or her claims.

Chapter 7 is a critical discussion of conditional commitment in both the Bayesian and the Belief model. Both use their treatment of conditional commitment as something to connect their theory of states with their respective transition theory. In doing so both models are immediately hit with technical difficulties. The Belief Model generates the ‘impossibility theorem’ first proved by Peter Gärdenfors, and the Bayesian model generates ‘triviality results’ first proved by David Lewis. Each of these technical areas is explained from scratch and diagnosed philosophically. It is argued that the bombshells discussed are best seen as showing that the Binary-Attitude Assumption is false when it comes to conditional commitment, and that there is no essential tie between conditional commitment and rational shift-in-view. Throughout the discussion the 3-place theory of conditionality is related back to Chapter 4’s restricted-vision approach to conditionality.

The log-Birnbaum-Saunders regression model introduced by Rieck and Nedelman (1991) is useful for modeling lifetimes of materials and equipments subject to different conditions. Our goal in this article is twofold. First, we numerically evaluate the finite sample performances of the likelihood ratio, score and Wald tests in the log-Birnbaum-Saunders regression model. Second, we introduce a RESET-like misspecification test for that model. The null hypothesis is that the model is correctly specified which is tested against the alternative hypothesis of model misspecification. The power of the test is evaluated using Monte Carlo simulations. Bootstrap-based inference is also considered. An empirical application is presented and discussed.

The aim of the paper is to show the relevance of nexus between tax forms and economic growth and how they affect on gross domestic product in Serbia for the period 2006-2015. The impact is manifested through the analysis of three main tax forms: personal income tax (PIT), corporate income tax (CIT) and value-added tax (VAT) and their effect on the macroeconomic indicator as gross domestic product (GDP). The analysis is for a period of ten years in Serbia, where the regression model is constructed so that the GDP is defined as the dependent variable, while the tax forms are set as independent variables. To ensure correctly specified regression model, authors used the next test: VIF test, BP and BPG test, as well as Ramsey reset test. Results show a high degree of positive correlation between the observed variables and the positive impact of the personal income tax, corporate income tax and value-added tax on the gross domestic product, but it is only the impact of value added tax statistically significant.

Consider the usual linear regression model consisting of two or more explanatory variables. There are many methods aimed at indicating the relative importance of the explanatory variables. But in general these methods do not address a fundamental issue: when all of the explanatory variables are included in the model, how strong is the empirical evidence that the first explanatory variable is more or less important than the second explanatory variable? How strong is the empirical evidence that the first two explanatory variables are more important than the third explanatory variable? The paper suggests a robust method for dealing with these issues. The proposed technique is based on a particular version of explanatory power used in conjunction with a modification of the basic percentile method.

According to the co-occurrence test, q is (non-monotonically) inferrible from p if and only if q holds in all the reasonably plausible belief change outcomes in which p holds. A formal model is introduced that contains representations of both the co-occurrence test (for non-monotonic inference) and the Ramsey test (for conditionals). In this model, (non-nested) conditionals and non-monotonic inference satisfy the same logical principles. However, in spite of this similarity the two notions do not coincide. They should be carefully distinguished from each other.

This study examined the impact of private domestic investment on manufacturing sector output in Nigeria from 1970 to 2017. The study specifically looked at the impact of private domestic investment on manufacturing sector’s output in a static and dynamic model. Six variables were employed in the study and were sourced from CBN statistical bulletin and World Development Indicators for the period covering from 1970 to 2017. The analysis of the variables undergoes three approaches, the pre-analysis of data, model estimation and the diagnostic analysis of the model. The first approach employed tables and graphs to explain the behaviour of the data, equally the univariate analysis of the data were examine with the Augmented Dickey-Fuller equations and the possibility of long-term relationship. The models were estimated with the ARDL estimator and model selected with the Akaike Information Criteria, and finally the models estimated were tested using the Jaque-Bera statistics, Ramsey RESET Test, Breusch-Godfrey and Harvey test for residual normality, specification bias, autocorrelation and heteroskedasticity respectively. The results from the analytical methods shows that there is over 82 percent increase in the output of Manufacturing sector in the late 1970s and early 1980s and over 98 percent increase the output of the manufacturing sector in the late within 2010 and 2015. Also, the study observed that the responses of output of the manufacturing sector to private domestic investment are positive and significant in the static and dynamic models. The study found that the impact of private domestic investment on manufacturing sector output were fairly elastic in the static model and fairly inelastic in the dynamic model. Finally, the study found that the model have a weak adjustment mechanism. The adjustment of disequilibrium between static and dynamic equilibrium is weak or just 24.9 percent. Since private domestic investment is significant and positively impacted on the performance of the manufacturing sector irrespective of the time zone, the study recommended for increase in the credit to private sector by the apex monetary authority.

The objective of this research is to compare Ramsey test, White test and Terasvirta test in the identification of nonlinearity. Ramsey test is a test based on the regression specification error test. While White test and Terasvirta test are based on neural network models. The difference between White test and Terasvirta test is in determining its weight, White test based on random sampling, while Terasvirta test based on Taylor expansion. Simulation studies are carried out with various scenarios in each test by generating linear models, linear models with outliers and nonlinear models. The results of the simulation study showed that Terasvirta test had better power than Ramsey test and White test in detecting nonlinearity. Terasvirta test is also more sensitive to the presence of outliers in linear models.

This reply to Oaksford and Chater’s (O&C)’s critical discussion of our use of logic programming (LP) to model and predict patterns of conditional reasoning will frame the dispute in terms of the semantics of the conditional. We begin by outlining some common features of LP and probabilistic conditionals in knowledge-rich reasoning over long-term memory knowledge bases. For both, context determines causal strength; there are inferences from the absence of certain evidence; and both have analogues of the Ramsey test. Some current work shows how a combination of counting defeaters and statistics from network monitoring can provide the information for graded responses from LP reasoning. With this much introduction, we then respond to O&C’s specific criticisms and misunderstandings.

Chalmers and H?jek showed that one of the common interpretations of the Ramzey test leads to absurd consequences. Leitgeb proposed a solution. I will try to formulate a better solution, which is compatible with Leitgeb?s, but which I think is more clear and more general.

Two of the major problems in AGM-style belief revision, namely the difficulties in accounting for iterated change and for Ramsey test conditionals, have satisfactory solutions in descriptor revision. In descriptor revision, the input is a (set of) metalinguistic sentence(s) specifying the success condition of the operation. The choice mechanism selects one of the potential outcomes (available belief sets) in which the success condition is satisfied. Iteration of this operation is unproblematic. Ramsey test conditionals can be introduced without giving rise to the paradoxical results that they generate in other systems. In addition to standard (sentential) Ramsey test conditionals, a more general variant of epistemic conditionals is defined, representing statements of the form ”if the belief state is changed to satisfy condition A then it will satisfy condition B”. An axiomatic characterization of such descriptor conditionals is presented. It is related in intricate ways to the KLM postulates for cumulative reasoning.

The paper brings arguments in favour of the idea that Student test statistics may not be considered only in comparison with the critical (tabelled) values in order to determine the significance of estimated parameters. Interesting information on the quality of estimation can be obtained by studying the correlation between the Student test statistics related to each explanatory variable in case of simple and multiple regressions, respectively. This way, it is possible to identify the factors which determine the change the Student test statistics related to considered explanatory variable as the number of the other explanatory variables increase in the linear regression equation. In this context, a factorial model for the analysis of the above-mentioned change is proposed. The proposed analysis methodology is practically applied to a private consumption function estimated for Romania during the period 1990-2009.

THE RAMSEY TEST FOR BELIEF If two people are arguing ‘If p will q?’ and are both in doubt as to p, they are adding p hypothetically to their stock of knowledge and arguing on that basis about q; so that in a sense ‘If p, q’ and ‘If p, ￢q’ are contradictories. (Ramsey, 1990/1929, p. 155) This remark of Frank Ramsey appears only as a footnote to his paper ‘General Propositions and Causality’, but it has sufficed to lend his name to a hypothesis that has figured prominently in contemporary debate in both the semantics and pragmatics of conditionals. This interest in the Ramsey Test hypothesis, as it is usually called, is fuelled by widespread dissatisfaction with the material conditional as a rendition of the semantic content of ordinary language conditionals. Discontent is focused on two points: the fact that the material conditional interpretation appears to support fallacious reasoning and the fact that reasonable belief in conditionals appears to diverge from that demanded by the material conditional interpretation of them. On the material conditional construal of conditionals, α → β is logically equivalent to ￢ α ∨ αβ . Hence, for example, ￢ (α → β) implies that α . But the inference from ‘It is not the case that if it snows tomorrow then the government will fall’ to ‘It will snow tomorrow’ is clearly not valid, because denying that the weather will have an impact on the government's fortunes does not commit one to any particular meteorological prognosis. Likewise, disbelieving that the government will fall if it snows does not mean believing that it will snow (and, in summer, should not). Nor does the material conditional do any better as an interpretation of counterfactuals – indeed, if anything, it does worse. On a material conditional interpretation, the claim expressed by the sentence ‘If George Bush had been concerned to protect the environment, then he would have lowered the tax on fuel’ should be highly credible, because of the improbability of its antecedent (this follows from the fact that this view implies that P(α → β) = P( ￢ α) + P(αβ) ). But intuitively the claim is implausible because environmentalists typically believe that fuel taxes should be raised.

The new Bayesian paradigm in the psychology of reasoning aims to integrate the study of human reasoning, decision making, and rationality. It is supported by two findings. One, most people judge the probability of the indicative conditional, P(if A then B), to be the conditional probability, P(B|A), as implied by the Ramsey test. Two, they judge if A then B to be void when A is false. Their three-valued response table used to be called 'defective', but should be termed the de Finetti table. We show how to study general de Finetti truth tables for negations, conjunctions, disjunctions, and conditionals.

Present paper seeks to examine whether inflation hampers economic growth in Nepal or not with the help of Distributed Lag Models using the annual data of GDP and Consumer Price Index (CPI). The nominal GDP is converted into real terms and transformed into logarithmic form and the first difference of the real GDP in logarithmic form is taken as the proxy for economic growth. The CPI data is converted into logarithmic form and its first difference is taken as the proxy for inflation. While using the distributed lag models, the economic growth of Nepal at current time is adversely affected by inflation of the same time, whereas the current economic growth is favorably affected by the inflation of preceding time. The estimated regression of economic growth on inflation up to lag one is found to be robust and stable as indicated by residuals diagnostic test ( serial correlation, heteroscedasticity and normality tests) and Ramsey's RESET test. The findings of the study throw light in policy point of view. The present study suggests that the rate of inflation would automatically be adjusted with increasing output if rate of investment in increased.

This paper proposes a new conditional mean test to assess the validity of binary and fractional parametric regression models. The new test checks the joint significance of two simple functions of the fitted index and is based on a very flexible parametric generalization of the postulated model. A Monte Carlo study reveals a promising behaviour for the new test, which compares favourably with that of the well-known RESET test as well as with tests where the alternative model is nonparametric.

Conditionals somehow express conditional beliefs. However, conditional belief is a bi-propositional attitude that is generally not truth-evaluable, in contrast to unconditional belief. Therefore, this article opts for an expressivistic semantics for conditionals, grounds this semantics in the arguably most adequate account of conditional belief, that is, ranking theory, and dismisses probability theory for that purpose, because probabilities cannot represent belief. Various expressive options are then explained in terms of ranking theory, with the intention to set out a general interpretive scheme that is able to account for the most variegated usage of conditionals. The Ramsey test is only the first option. Relevance is another, familiar, but little understood item, which comes in several versions. This article adds a further family of expressive options, which is able to subsume also counterfactuals and causal conditionals, and indicates at the end how this family allows for partial recovery of truth conditions for conditionals.

This paper proposes a formalization of conditional reasoning using Moss and Parikh’s logic of subsets so that a reasoner can express both conditional assertions about beliefs, as well as beliefs about conditional assertions. We present a complete axiomatization of the logic and show that it is decidable. A version of the Ramsey test is found to be compatible with this logic and provides a correspondence between conditionals and belief contraction.

I formulate a counterfactual version of the notorious ‘Ramsey Test’. Whereas the Ramsey Test for indicative conditionals links credence in indicatives to conditional credences, the counterfactual version links credence in counterfactuals to expected conditional chance. I outline two forms: a Ramsey Identity on which the probability of the conditional should be identical to the corresponding conditional probability/expectation of chance; and a Ramsey Bound on which credence in the conditional should never exceed the latter. Even in the weaker, bound, form, the counterfactual Ramsey Test makes counterfactuals subject to the very argument that Lewis used to argue against the indicative version of the Ramsey Test. I compare the assumptions needed to run each, pointing to assumptions about the time-evolution of chances that can replace the appeal to Bayesian assumptions about credence update in motivating the assumptions of the argument. I finish by outlining two reactions to the discussion: to indicativize the debate on counterfactuals; or to counterfactualize the debate on indicatives.

In `A Defence of the Ramsey Test', Richard Bradley makes a case for not concluding from the famous impossibility results regarding the Ramsey Test the thesis that a rational agent believes a conditional if he would believe the consequent upon learning the antecedent that the thesis is false. He lays the blame instead on one of the other premisses in these results, namely the Preservation condition. In this paper, we explore how this condition can be weakened by strengthening the notion of consistency which appears in it. After considering the e ects of such weakenings for Bradley's argument, we propose a re nement of the Preservation condition which does not fall prey to Bradley's argument nor to Gardenfors's impossibility theorem. We brie y compare it to Bradley's suggested restriction of Preservation. In `A Defence of the Ramsey Test', Richard Bradley (2007) makes a case for not concluding from the famous `impossibility theorems' regarding

Binary response index models may be affected by several forms of misspecification, which range from pure functional form problems (e.g. incorrect specification of the link function, neglected heterogeneity, heteroskedasticity) to various types of sampling issues (e.g. covariate measurement error, response misclassification, endogenous stratification, missing data). In this article we examine the ability of several versions of the RESET test to detect such misspecifications in an extensive Monte Carlo simulation study. We find that: (i) the best variants of the RESET test are clearly those based on one or two fitted powers of the response index; and (ii) the loss of power resulting from using the RESET instead of a test directed against a specific type of misspecification is very small in many cases.

Chalmers and Hajek argue that on an epistemic reading of Ramsey’s test for the rational acceptability of conditionals, it is faulty. They claim that applying the test to each of a certain pair of conditionals requires one to think that one is omniscient or infallible, unless one forms irrational Moore-paradoxical beliefs. I show that this claim is false. The epistemic Ramsey test is indeed faulty. Applying it requires that one think of anyone as all-believing and if one is rational, to think of anyone as infallible-if-rational. But this is not because of Moore-paradoxical beliefs. Rather it is because applying the test requires a certain supposition about conscious belief. It is important to understand the nature of this supposition.

According to the Ramsey Test, conditionals reflect changes of beliefs: α > β is accepted in a belief state iff β is accepted in the minimal revision of it that is necessary to accommodate α. Since Gardenfors’s seminal paper of 1986, a series of impossibility theorems (“triviality theorems”) has seemed to show that the Ramsey test is not a viable analysis of conditionals if it is combined with AGM-type belief revision models. I argue that it is possible to endorse that Ramsey test for conditionals while staying true to the spirit of AGM. A main focus lies on AGM’s condition of Preservation according to which the original belief set should be fully retained after a revision by information that is consistent with it. I use concrete representations of belief states and (iterated) revisions of belief states as semantic models for (nested) conditionals. Among the four most natural qualitative models for iterated belief change, two are identified that indeed allow us to combine the Ramsey test with Preservation in the language containing only flat conditionals of the form α > β. It is shown, however, that Preservation for this simple language enforces a violation of Preservation for nested conditionals of the form α > (β > γ). In such languages, no two belief sets are ordered by strict subset inclusion. I argue that it has been wrong right from the start to expect that Preservation holds in languages containing nested conditionals.

I analyse the relationship between the Ramsey Test (RT) for the acceptance of indicative conditionals and the so-called problem of decision-instability. In particular, I argue that the situations which allegedly bring about this problem are troublesome just in case the relevant conditionals are evaluated by non-suppositional versions, e.g. causal/evidential, of the test. In contrast, a suppositional RT, by highlighting the metacognitive nature of the evaluation of indicative conditionals, allows an agent to run a simulation of such evaluation, without yet committing her to the acceptance of such conditionals. I conclude that a suppositional interpretation of RT is superior to its non-suppositional counterparts and by briefly showing that a suppositional RT is compatible with a deliberational decision theory.

Famously, Frank P. Ramsey suggested a test for the acceptability of conditionals. Recently, David Chalmers and Alan Hajek (2007) have criticized a qualitative variant of the Ramsey test for indicative conditionals. In this paper we argue for the following three claims: (i) Chalmers and Hajek are right that the variant of the Ramsey test that they attack is not the correct way of spelling out an acceptability test for indicative conditionals. But there is a suppositional variant of the Ramsey test which is still stated in purely qualitative terms, which avoids the problems, and which looks correct. (ii) While the variant of the Ramsey test that Chalmers and Hajek criticize is not correct, it is still a good approximation of a correct formulation of the Ramsey test which may be usefully employed in various contexts. (iii) The variant of the Ramsey test that Chalmers and Hajek suggest as a substitute for the deficient version of the Ramsey test is itself subject to worries similar to those raised by Chalmers and Hajek, if it is given a non-suppositional interpretation.

In contemporary discussions of the Ramsey Test for conditionals, it is commonly held that (i) supposing the antecedent of a conditional is adopting a potential state of full belief, and (ii) Modus Ponens is a valid rule of inference. I argue on the basis of Thomason Conditionals (such as ‘If Sally is deceiving, I do not believe it’) and Moore’s Paradox that both claims are wrong. I then develop a double-indexed Update Semantics for conditionals which takes these two results into account while doing justice to the key intuitions underlying the Ramsey Test. The semantics is extended to cover some further phenomena, including the recent observation that epistemic modal operators give rise to something very like, but also very unlike, Moore’s Paradox.

In regression analysis, model misspecification can produce spurious spatial correlation in the residuals. By means of Monte Carlo simulations, I show that the RESET test can help to disentangle this conundrum in large samples. Small samples can pose a serious challenge to finding the correct model.

Peter Gärdenfors proved a theorem purporting to show that it is impossible to adjoin to the AGM-postulates for belief-revision a principle of monotonicity for revisions. The principle of monotonicity in question is implied by the Ramsey test for conditionals. So Gärdenfors’ result has been interpreted as demonstrating that it is impossible to combine the Ramsey test for conditionals with the basic postulates for rational belief-revision. It is shown here that this interpretation of Gärdenfors’ result is unwarranted. A new diagnosis is offered of a methodological error made in the statement of the key principle of monotonicity. Crucial applications of this principle in Gärdenfors’ proof require one to regard as revisions what are really expansions. If monotonicity is stated only for genuine revisions, then Gärdenfors’ proof does not go through. Nor can it; for, when the monotonicity principle for revisions is correctly formulated, one can actually establish a contrary consistency result. This requires only a slight adjustment to the postulates of AGM-theory, in order to ensure that the three operations of expansion, contraction, and revision trichotomize the domain of theory-changes. It is further shown that being careful in this way about the proper domains of definition of the three operations of expansion, contraction, and revision also disposes of another, more direct, impossibility result, due to Arló-Costa, that targets the Ramsey test.

Conditionals that contain a modality in the consequent give rise to a particular semantic phenomenon whereby the antecedent of the conditional blocks possibilities when interpreting the modality in the consequent. This explains the puzzling logical behaviour of constructions like "If you don't buy a lottery ticket, you can't win", "If you eat that poison, it is unlikely that you will survive the day" and "If you kill Harry, you ought to kill him gently". In this paper it is argued that a semantic version of the Ramsey Test provides a key in the analysis of such constructions. The logic for this semantics is axiomatized and some examples are studied, among them a well-known puzzle for contrary-to-duty obligations.

The paper presents a non-monotonic inference relation on a language containing a conditional that satisfies the Ramsey Test. The logic is a weakening of classical logic and preserves many of the ‘paradoxes of implication’ associated with the material implication. It is argued, however, that once one makes the proper distinction between supposing that something is the case and accepting that it is the case, these ‘paradoxes’ cease to be counterintuitive. A representation theorem is provided where conditionals are given a non-bivalent semantics and epistemic states are represented via preferential models.

Le test de Ramsey ne peut pas s'appliquer aux conditionnels ontiques (ontic conditionals) et dans le cas des conditionnels epistemiques il pose probleme. L'A. analyse le paradoxe de Gardenfors et les facons de modifier le test de Ramsey afin d'eviter ce paradoxe

This paper proposes a simple specification test for quantile regression models. Our test is based on Ramsey’s (J. R. Stat. Soc. B 31:350–371, 1969) RESET (regression specification error test). Comparing to existing nonparametric specification tests, the proposed test does not contain kernel functions and bandwidth parameters and is easy to implement. Although the proposed test is not necessarily consistent against all types of misspecification, simulation results indicate that our test has reasonable size and power properties and can be more powerful than nonparametric specification tests in small samples.

In many applications of instrumental-variables regression, researchers seek to defend the plausibility of a key assumption: the instrumental variable is independent of the error term in a linear regression model. Although fulfilling this exogeneity criterion is necessary for a valid application of the instrumental-variables approach, it is not sufficient. In the regression context, the identification of causal effects depends not just on the exogeneity of the instrument but also on the validity of the underlying model. In this article, I focus on one feature of such models: the assumption that variation in the endogenous regressor that is related to the instrumental variable has the same effect as variation that is unrelated to the instrument. In many applications, this assumption may be quite strong, but relaxing it can limit our ability to estimate parameters of interest. After discussing two substantive examples, I develop analytic results (simulations are reported elsewhere). I also present a specification test that may be useful for determining the relevance of these issues in a given application.

The small sample properties of the systemwise RESET (Regression Specification Error Test) test for functional misspecification are investigated using normal and non-normal error terms. When using normally distributed or less heavy tailed error terms, we find the Rao's multivariate F-test to be best among all other alternative test methods (i.e. Wald, Lagrange Multiplier and Likelihood Ratio). Using the bootstrap critical values, however, all test methods perform satisfactorily in almost all situations. However, the test methods perform extremely badly (even the RAO test) when the error terms are very heavy tailed.

We consider belief revision involving conditional statements where the antecedent is almost certainly false. In order to represent such statements, we use Ordinal Conditional Functions that may take infinite values. In this manner, we are able to capture the intuition that the antecedent can not be verified by a finite number of observations. We define belief revision in this context through basic ordinal arithmetic, and we propose an approach to conditional revision in which only the right hypothetical levels are revised by conditional information. We compare our approach to existing work on conditional revision and belief improvement.