Oil and the Macroeconomy: A Markov State-Switching Approach

Raymond, Jennie E.; Rich, Robert W.

doi:10.2307/2953675

Cited by 134 publications

(93 citation statements)

References 17 publications

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“…We tested the parameter restrictions that rule out SDS asymmetries, and could show that the outcomes are unaffected by allowing oil prices to affect the conditional mean of output growth. Our findings are broadly in line with those of Raymond and Rich (1997): oil prices do not appear to be the sole explanation of regime-switching behavior. Furthermore, the asymmetries detected in the business cycle do not appear to be explicable by oil prices.…”

Section: Discussionsupporting

confidence: 80%

“…. , M, whereξ m is the ergodic or unconditional probability of regime m. The findings of Raymond and Rich (1997) support the assumption of time-invariant transition probabilities:…”

Section: Asymmetries In Ms-ar Processessupporting

confidence: 64%

“…The relatively small number of cycles in the post War period suggest to us that complicated models with switching autoregressive parameters may be asking too much of the data, and in fact even the simplest models are capable of generating fairly complex behavior, suggesting that erring on the side of parsimony may be without much cost. Raymond and Rich (1997) appear to have followed this strategy: they set M = 2 and p = 0 in (1) (Hamilton, 1989, had p = 4 ), 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 Figure 2 shows that such a model is able to capture the NBER recessions. The second and third panels depict the probabilities of being in the contraction and expansion regimes, respectively, where the mean growths are estimated to be -0.27 and 1.16 percent per quarter, and the expected durations of expansions and contractions are 14 and 4 quarters.…”

Section: Two-regime Models Of the Us Business Cyclementioning

confidence: 99%

“…Our approach follows that of Raymond and Rich (1997) but we ask a different question: are oil prices responsible for business cycle asymmetries? Some authors appear to have assumed that the finding that a non-linear model (as opposed to a linear model) characterizes the data of itself suggests business cycle asymmetries: see e.g., Luukkonen and Teräsvirta (1991).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Hamilton (2000) has used a flexible approach to characterize the appropriate non-linear transform of the oil price, and finds that the transformation that gives the net increase in the oil price can not be rejected (but nor can a number of other transformations). Raymond and Rich (1997) have investigated the relationship between oil prices and the macroeconomy by including the net increase in the oil price (NOPI) in the two-regime Markov-Switching model of US output growth of Hamilton (1989) for the period 1952 -1999. Of interest is whether the recurrent shifts between expansion (high growth) and contraction (low growth, or declines in output) suggested by the Markov-switching model remain a feature of post War US output once an allowance has been made for the effects of oil price increases.…”

Section: Introductionmentioning

confidence: 99%

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Can oil shocks explain asymmetries in the US Business Cycle?

Clements

Krolzig

2002

Advances in Markov-Switching Models

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We consider whether oil prices can account for business cycle asymmetries. We test for asymmetries based on the Markov switching autoregressive model popularized by Hamilton (1989), using the tests devised by Clements and Krolzig (2000). We select the transformation of the oil price of Lee, Ni and Ratti (1995), based on a linear analysis of the relationship between output growth and the oil price employing PcGets. We find overwhelming evidence against the conventional wisdom that recessions are more violent than expansions: while some of violence of economic downturns can be attributed to dramatic changes in the price of oil, post-War US economic growth is characterized by the steepness of expansions. JEL classification: E32, C32, E24.

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Section: Discussionsupporting

confidence: 80%

“…. , M, whereξ m is the ergodic or unconditional probability of regime m. The findings of Raymond and Rich (1997) support the assumption of time-invariant transition probabilities:…”

Section: Asymmetries In Ms-ar Processessupporting

confidence: 64%

Section: Two-regime Models Of the Us Business Cyclementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Can oil shocks explain asymmetries in the US Business Cycle?

Clements

Krolzig

2002

Advances in Markov-Switching Models

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Pension funding problem with regime‐switching geometric Brownian motion assets and liabilities

Chen¹,

Yang²

2009

Appl Stoch Models Bus & Ind

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This paper extends the pension funding model in (N. Am. Actuarial J. 2003; 7:37-51) to a regimeswitching case. The market mode is modeled by a continuous-time stationary Markov chain. The asset value process and liability value process are modeled by Markov-modulated geometric Brownian motions. We consider a pension funding plan in which the asset value is to be within a band that is proportional to the liability value. The pension plan sponsor is asked to provide sufficient funds to guarantee the asset value stays above the lower barrier of the band. The amount by which the asset value exceeds the upper barrier will be paid back to the sponsor. By applying differential equation approach, this paper calculates the expected present value of the payments to be made by the sponsor as well as that of the refunds to the sponsor. In addition, we study the effects of different barriers and regime switching on the results using some numerical examples. The optimal dividend problem is studied in our examples as an application of our theory. can be significantly affected by the changes in political policies, the impact of economic news, etc., we may need to model the asset and liability value processes in a way with more flexibility.In the recent years, regime switching models have become popular in finance and related fields. This type of model is motivated by the intension to reflect the state of the financial market. For example, the state of the market can be roughly divided into 'bullish' and 'bearish' two regimes, in which the price movements of the stocks are quite different. Generally, in a regimeswitching model, the value of market modes is divided into a finite number of regimes. The key parameters, such as the bank interest rate, stocks appreciation rates, and volatility rates, will change according to the value of different market modes. Since the market state may change from one regime to another, both the nature of the regime and the change point should be estimated. If the market state process is modeled by a continuous time Markov chain with finite states, regime switching models are also referred to as Markov switching or Markov-modulated models in some literatures.With time-varying parameters, regime switching models are obviously more realistic than constant parameter model to reflect the random market environment. As discussed in Neftci [2], an appealing ability of these models is to account for the accumulating evidence that business cycles are asymmetric. Most of the studies indicate that regime-switching models perform well in some sense, for example, Hardy [3] used monthly data from the Standard and Poor's 500 and the Toronto Stock Exchange 300 indices to fit a regime switching lognormal model. In their paper, the fit of the regime switching model to the data was compared with other econometric models and they found that regime-switching models provided a significant improvement over all the other models in the sense of maximizing the likelihood function. In a special case, if the data are in lognor...

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Wang

et al. 2010

Appl Stoch Models Bus & Ind

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Oil and the Macroeconomy: A Markov State-Switching Approach

Cited by 134 publications

References 17 publications

Can oil shocks explain asymmetries in the US Business Cycle?

Can oil shocks explain asymmetries in the US Business Cycle?

Pension funding problem with regime‐switching geometric Brownian motion assets and liabilities

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